High-resolution protein modeling through Cryo-EM and AI: current trends and future perspectives – a review

The structural elucidation of proteins is fundamental to understanding their biological functions and advancing drug discovery. Recent breakthroughs in cryo-electron microscopy (cryo-EM) and artificial intelligence (AI)-based structure prediction have revolutionized protein modeling by enabling near-atomic resolution visualization and highly accurate computational predictions from amino acid sequences. This review summarizes the latest advances that have transformed protein structural biology from a predominantly structure-solving endeavour to a discovery-driven science. We discuss the complementary roles of cryo-EM and AI, including developments in direct electron detectors, advanced image processing, and deep learning algorithms exemplified by AlphaFold 2 and the emerging AlphaFold 3. These technologies facilitate detailed insights into challenging protein targets such as membrane proteins, flexible and intrinsically disordered proteins, and large macromolecular complexes. Furthermore, we highlight applications of integrative approaches in drug design, enzymatic mechanism elucidation, and functional predictions, illustrated by examples including hemoglobin, which demonstrates both the strengths and current limitations of AI–cryo-EM integration, and cytochrome P450 enzymes, where AlphaFold predictions have been combined with cryo-EM maps to explore conformational diversity. The review also addresses ongoing challenges and promising future directions for integrating experimental and computational methods to accelerate the exploration of protein structure–function relationships, ultimately impacting biomedical research and therapeutic development.

GRAPHICAL ABSTRACT

Highlights

• Cryo-EM and AI enable accurate, high-resolution modeling of diverse protein structures.

• AI tools like AlphaFold drive rapid protein structure prediction and functional discovery.

• Integrative approaches resolve structures of membrane proteins and flexible assemblies.

• Structure insights guide drug design and disease research for targets like viral and amyloid proteins.

• Advances pave the way for discovery-driven protein science and therapeutic innovation.

1 Introduction

Biological macromolecules proteins, nucleic acids, and polysaccharides are essential components of life, driving fundamental processes such as DNA replication, enzyme activity, and cellular communication. For example, hemoglobin transports oxygen in the blood, while DNA polymerases ensure the accurate copying of genetic material during cell division (Kornberg Arthur, 1992; PERUTZ et al., 1960). Polysaccharides like cellulose provide structural support to plant cell walls, and glycosaminoglycans in the extracellular matrix help regulate tissue flexibility and cell signaling (Cosgrove, 2005; Hynes and Naba, 2012). The function of these macromolecules is directly linked to their three-dimensional (3D) structures, which determine their folding, interactions, and biological roles (Dill and MacCallum, 2012). Understanding these structures extends beyond academic interest, playing a pivotal role in addressing global health, energy, and sustainability challenges.

Structural biology is dedicated to elucidating the architectural design of biological macromolecules, aiding in the comprehension of their functions and the development of new drugs, biomaterials, and industrial enzymes. The field has advanced dramatically since the first structure of myoglobin was determined using X-ray crystallography (Kendrew et al., 1958). Technological innovations from early crystallography to contemporary cryo-electron microscopy (cryo-EM) and artificial intelligence (AI)-driven structure prediction have significantly enhanced our ability to visualize and understand molecular structures (Jumper et al., 2021; Nogales, 2016).

X-ray crystallography has long been a cornerstone of structural biology, helping scientists determine high-resolution structures of countless proteins, nucleic acids, and their complexes. For example, this technique was used to solve the structure of the ribosome, a large molecular machine responsible for protein synthesis, revealing intricate details about how it functions (Yusupov et al., 2001). However, X-ray crystallography has its limitations, particularly when studying large, flexible, or membrane-bound macromolecules that are difficult to crystallize (Caffrey, 2015). Despite these challenges, it remains a vital tool in structural biology, as seen in its role in identifying the structure of the SARS-CoV-2 main protease, a key target for antiviral drug development (Zhang et al., 2020).

Nuclear magnetic resonance (NMR) spectroscopy emerged as a valuable complementary technique, allowing researchers to study macromolecules in solution and observe their dynamic behavior. NMR has been particularly useful in analyzing small to medium-sized proteins, such as the oncogenic protein KRAS, which plays a crucial role in cancer signaling pathways (Ostrem et al., 2013). However, this technique has its own limitations it struggles to analyze larger macromolecular complexes or membrane proteins due to their complexity and size (Wüthrich, 2001).

The introduction of cryo-electron microscopy (cryo-EM) has transformed structural biology by overcoming many of the limitations of traditional techniques. Cryo-EM allows scientists to visualize large macromolecular complexes and membrane proteins at near-atomic resolution without requiring crystallization. The ability to determine atomic structures from patient-derived or native biological samples first demonstrated in studies such as Fitzpatrick et al. illustrates structural biology’s transformation into a powerful discovery tool capable of generating novel hypotheses and enabling translational advances directly from human disease material (Fitzpatrick et al., 2017). A major breakthrough was the determination of the structure of the TRPV1 ion channel, which revealed how this protein detects heat and pain (Liao et al., 2013). Further advancements, such as direct electron detectors and sophisticated image processing algorithms, have significantly improved the resolution and applicability of cryo-EM, making it a crucial tool in modern structural biology (Kühlbrandt, 2014). A pivotal breakthrough underlying the cryo-EM “resolution revolution” was the introduction of direct electron detection cameras (X. Li et al., 2013). These detectors provide dramatically improved signal-to-noise ratios, accurate electron event counting, and rapid frame rates, enabling correction of beam-induced motion and unlocking near-atomic resolution for previously intractable targets. The landmark structure of the TRPV1 ion channel, for example, was only attainable with these new generation detectors (Liao et al., 2013), which have since become standard across high-resolution cryo-EM studies.

At the same time, computational methods have become essential in structural biology. The rise of AI-driven algorithms, such as AlphaFold and RoseTTAFold, has revolutionized protein structure prediction, allowing scientists to determine structures with remarkable accuracy based solely on amino acid sequences. For example, AlphaFold successfully predicted the structure of the orphan protein ORF8 from SARS-CoV-2, providing crucial insights into its potential role in immune evasion (Gordon et al., 2020; Varadi et al., 2022). These advancements have not only accelerated the pace of structural discovery but have also made structural information more accessible to researchers worldwide, enabling a deeper understanding of molecular biology (Baek et al., 2021).

The combination of experimental and computational approaches has significantly broadened the field of structural biology, allowing researchers to study macromolecules in their natural environments and observe their dynamic structural changes. Techniques such as time-resolved crystallography, small-angle X-ray scattering (SAXS), and integrative modeling have provided groundbreaking insights into the flexible and dynamic nature of biological macromolecules. For instance, time-resolved studies of the photosynthetic reaction center have uncovered the intricate sequence of electron transfer events during photosynthesis (Kern et al., 2013). Similarly, integrative modeling has been used to reconstruct the structure of the nuclear pore complex, a massive molecular assembly responsible for regulating the transport of molecules between the nucleus and the cytoplasm (Kim et al., 2018). In this review, we synthesize recent advances in cryo-EM and AI with a focus on improvements in resolution, heterogeneity analysis, and automated model building. We also discuss how AI-based predictors such as AlphaFold2 are being integrated into cryo-EM workflows to expand their impact. By emphasizing the intersection of these fields, we highlight methodological innovations that are shaping structural biology today.

2 Foundational methods in structural biology

Structural biology has historically relied on three primary techniques: X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, and electron microscopy (EM). These methods continue to be indispensable, despite recent advancements in cryo-EM and AI-driven structure prediction.

2.1 X-ray crystallography

Since its emergence in the 1950s, X-ray crystallography has been a cornerstone of structural biology, enabling the determination of high-resolution structures of proteins, nucleic acids, and their complexes. By analyzing the diffraction patterns of X-rays passing through crystallized macromolecules, scientists can generate electron density maps to construct atomic models (Drenth J, 2007). Innovations such as microfocus X-ray beams and serial crystallography has expanded its applicability, facilitating the study of smaller crystals and transient molecular states. For example, time-resolved serial femtosecond crystallography (TR-SFX) at X-ray free-electron lasers (XFELs) has provided insights into the catalytic cycle of cytochrome c oxidase, shedding light on electron and proton transfer mechanisms (Kern et al., 2013). Recent innovations in high-throughput screening and automated data processing further illustrate the method’s ongoing relevance and adaptability (Smith et al., 2023). Schematic representation of the X-ray crystallography process for protein structure determination shown in Figure 1.

Figure 1

Figure 1. Schematic representation of the X-ray crystallography process for protein structure determination. (1) The sample undergoes crystallization to form an ordered lattice. (2) The crystal is exposed to X-ray beams, generating a diffraction pattern that provides crucial structural information. Note: The “phase problem” arises here, as phase information is not captured in the diffraction data and must be recovered through methods such as molecular replacement or experimental phasing (e.g., SAD/MAD). (3) Computational methods are used to derive an electron density map from the diffraction data, incorporating phase determination techniques. (4) The final atomic model is built and refined by fitting atomic coordinates into the electron density map, resulting in a high-resolution structural representation of the macromolecule.

Another major advancement in membrane protein crystallography was the determination of the β2-adrenergic receptor structure while bound to its agonist using lipidic cubic phase (LCP) crystallization. This study not only provided a high-resolution view of G protein-coupled receptor (GPCR) signaling but also paved the way for the structural characterization of other membrane proteins, including the adenosine A2A receptor and the serotonin receptor (Rasmussen et al., 2011).

X-ray crystallography has also played a crucial role in drug discovery, particularly in antiviral therapy development. For example, the structure of the SARS-CoV-2 main protease (Mpro), determined through crystallography, revealed the enzyme’s active site and enabled the design of inhibitors such as nirmatrelvir, a drug effective in treating COVID-19 (Zhang et al., 2020). Additionally, crystallography has been instrumental in understanding enzyme mechanisms, such as the DNA-cleaving activity of CRISPR-Cas9. The structure of CRISPR-Cas9 in complex with guide RNA and target DNA provided a detailed molecular blueprint for genome editing, revolutionizing biotechnology and medicine (F. Jiang et al., 2015).

Despite its contributions, X-ray crystallography faces challenges in crystallizing large, flexible, or membrane-bound macromolecules. However, continuous advancements in crystallization techniques and computational modeling ensure its enduring significance. Detailed technical advances and their implications are discussed in Section 3.

2.2 Nuclear magnetic resonance (NMR) spectroscopy

NMR spectroscopy enables the study of macromolecules in solution, providing insights into their structural dynamics, interactions, and conformational changes. Unlike X-ray crystallography, NMR does not require crystallization, making it particularly useful for analyzing small to medium-sized proteins (Cavanagh et al., 2007). Traditionally, NMR’s strengths have been in resolving the structures and dynamic properties of small to medium-sized proteins (generally <40 kDa), although advances in isotope labeling and high-field instrumentation have gradually extended these boundaries (Wüthrich, 2001).

Solid-state NMR has also emerged as a powerful tool for studying membrane proteins and amyloid fibrils. For example, the structure of the amyloid-β fibril, a key hallmark of Alzheimer’s disease was determined using solid-state NMR, revealing its cross-β architecture and shedding light on the molecular basis of amyloid formation (Tycko, 2011).

NMR is widely used to study protein-ligand interactions, particularly in drug discovery. One notable example is the characterization of the interaction between the oncogenic protein KRAS and its inhibitors. NMR studies have provided important insights into how KRAS undergoes conformational changes upon inhibitor binding, aiding the development of more effective cancer therapies (Ostrem et al., 2013). Advances in high-field NMR have improved resolution and sensitivity, extending its applicability to larger proteins. NMR spectroscopy has played a pivotal role in characterizing intrinsically disordered proteins (IDPs), which lack stable tertiary structures yet perform critical regulatory functions. Notable examples include α-synuclein, implicated in Parkinson’s disease (Burré et al., 2014), and the tumor suppressor p53, whose disordered regions modulate DNA binding and partner interactions (Krois et al., 2018). Recent developments in NMR methodology have further expanded its utility to mammalian mega-complexes and large IDP-containing systems (Joerger and Fersht, 2008).

Despite these advances, NMR remains limited when analyzing large macromolecules due to signal overlap, reduced resolution, and the need for isotopic labeling. Ongoing developments in NMR instrumentation and computational analysis continue to enhance its potential in structural biology.

2.3 Electron microscopy (EM)

The landscape of electron microscopy has dramatically evolved in the past decade. While conventional EM (using negative staining and photographic detection) was foundational, it offered limited resolution and structural detail. The so-called “resolution revolution” triggered by the introduction of direct electron detectors and advanced processing algorithms (X. Li et al., 2013) has transformed cryo-EM into a high-resolution, widely adopted tool. Thus, our review focuses on cryo-EM as it is used in current structural biology, and references to “conventional EM” are restricted to historical perspective or legacy techniques. Traditional EM has provided low-resolution visualizations of macromolecular complexes and cellular structures. Recent technological advancements, particularly in cryo-EM, have extended its capabilities, allowing high-resolution imaging of biological macromolecules in near-native states. Negative staining EM has been useful in studying macromolecular assemblies, including ribosomes and viral capsids (Frank, 2006). The development of cryo-EM has transformed the field, providing near-atomic resolution images without requiring crystallization (Kühlbrandt, 2014). Conventional EM, in fact, set the stage for cryo-EM, and in its present form, has reshaped the face of structural biology forever. Early studies with EM of the ribosome produced low-resolution maps, paving the path for future high-resolution cryo-EM structures and revealing molecular detail in protein synthesis (Yusupov et al., 2001). EM played a key role in studying virus morphology, including Ebola and influenza virus. The glycoprotein structure of Ebola, determined with EM, guided vaccine development through its demonstration of the molecular mechanism of virus entry into host cells (J. E. Lee et al., 2008). EM has also helped visualize structures of cellular organelles, including mitochondrial and endoplasmic reticulum structures, and shed light on their function and organization. For example, EM studies of mitochondrial structures revealed the complex arrangement of cristae, inner membrane folds in which cellular respiration occurs (Frey and Mannella, 2000). Conventional EM, despite its success, suffers from poor resolution, generally in the 10–20 Å range, and difficulty in preparing samples for analysis. With cryo-EM and new EM technology, many of these challenges have been overcome, and a new era in structural biology has begun.

Recent years have witnessed the incorporation of novel instrumentation, automation, and computational approaches that substantially expand the capabilities of these foundational methods. Transformative advances including time-resolved crystallography, enhanced NMR techniques, and integration of artificial intelligence are discussed in detail in Section 3.

3 Recent advances in structural characterization

Building on the foundational principles introduced earlier, this section highlights recent breakthroughs in cryo-EM, AI-driven prediction, and hybrid modeling that have expanded the capabilities of structural biology beyond conventional limits.

3.1 Cryo-electron microscopy (Cryo-EM)

Cryo-electron microscopy (cryo-EM) is a cutting-edge technology in structural biology, providing visualization of membrane proteins and large macromolecular complexes at near-atomic resolution in their native conformations, without the need for crystallization. Breakthroughs in direct electron detectors, high-performance image processing algorithms, and sample preparation methodologies have driven this revolution (Liao et al., 2013). Unlike traditional techniques, cryo-EM allows the study of proteins in their native conformations, preserving their conformational flexibility and dynamic behavior. This makes cryo-EM particularly beneficial for studying challenging targets such as membrane proteins, large complexes, and intrinsically disordered proteins. Figure 2 schematically illustrates the Cryo-EM process for protein structure determination.

Figure 2

Figure 2. This schematic illustrates the Cryo-EM process for protein structure determination. (1) The purified protein sample is prepared for analysis. (2) Negative staining is performed for initial visualization and sample quality assessment. (3) The protein sample is applied to a Cryo-EM grid for vitrification, ensuring structural preservation. (4) The grid is imaged using a Cryo-EM microscope under cryogenic conditions. (5) Computational processing of Cryo-EM images is performed to generate a high-resolution density map. Note: This step includes essential Contrast Transfer Function (CTF) correction to restore contrast and improve the accuracy of structural reconstruction. (6) The final Cryo-EM structural model is built based on the density map, revealing atomic-level details of the macromolecule.

One of the most significant breakthroughs in cryo-EM has been its application to ribosome structures. Ribosomes, complex and dynamic molecular machines responsible for protein synthesis, have long been challenging to study using traditional techniques. Notably, the landmark cryo-EM study by Fitzpatrick et al. provided the first high-resolution structures of tau filaments extracted directly from human Alzheimer’s disease brain tissue, revealing disease-specific filament folds in patient-derived samples for the first time (Fitzpatrick et al., 2017). This breakthrough exemplifies how advances in structural biology have enabled direct molecular discovery in clinically relevant contexts, allowing new hypotheses and therapeutic avenues to emerge from human-derived materials rather than model systems alone. In recent years, cryo-EM has resolved several functionally informative states of the human mitochondrial ribosome, including high-resolution structures with translational factors such as EF-G1 that reveal tRNA translocation conformations (Koripella et al., 2020), more recent pre-initiation mtSSU complexes elucidating initiation factor interactions (Hilander et al., 2024), and a 2.2 Å mitoribosome structure with cofactors and modified tRNAs providing atomic detail of its active components (Singh et al., 2024). These advances deepen understanding of mitochondrial protein synthesis, its dysfunction in disease, and potential structural targets for therapeutic intervention.

Cryo-EM has also facilitated the characterization of membrane proteins, which are notoriously resistant to crystallization. In 2022, a breakthrough study determined the structure of the P2X7 receptor, a key mediator of inflammation and immunity, using cryo-EM (McCarthy et al., 2019). The study revealed the receptor’s activation mechanism, providing a molecular blueprint for the development of anti-inflammatory therapies. Similarly, cryo-EM has been used to explore the capsids of viruses such as SARS-CoV-2 and Zika virus. In 2023, cryo-EM was used to determine the capsid structure of the monkeypox virus, offering new insights into its assembly and potential antiviral therapy targets (Xu et al., 2023).

Resolution remains a critical parameter in cryo-EM, as it determines the level of structural detail that can be extracted from density maps. While crystallographers reserve the term atomic resolution for data near 1.2 Å, at which even hydrogen atoms can be visualized (Nakane et al., 2020; Wlodawer and Dauter, 2017), in cryo-EM practice maps around 3 Å are widely regarded as atomic or near-atomic because amino-acid side chains and backbone atoms can be traced with confidence, enabling de novo model building (Yip et al., 2020; Zhou, 2011). Statistical analyses of the EMDB show the rapid progress of the field: a decade ago most reconstructions were in the 4–10 Å range, but by 2023–2024 the majority of deposited maps achieved better than 4 Å, with nearly half falling in the 3–5 Å range and record structures approaching 1.2 Å (Iudin et al., 2023; Kleywegt et al., 2024). The interpretability of cryo-EM data is strongly resolution-dependent: at ∼10 Å, only the overall molecular envelope and gross domain organization are visible; at ∼5 Å, α-helices appear as rods and β-sheets as slabs, but side chains are not individually resolved; and at ∼3 Å, side chains are clearly distinguishable and atomic models can be constructed with near-crystallographic accuracy (Rosenthal and Rubinstein, 2015; Zhou, 2011).

Recent advances in AI-driven cryo-EM data processing go well beyond generic neural networks, with the development of specialized tools such as DeepPicker (Wang et al., 2016) and Topaz (Bepler et al., 2019). DeepPicker was among the first to employ deep convolutional neural networks for automatic, accurate particle selection, while Topaz further improved accuracy and speed, enabling robust particle picking even for challenging datasets. Integration of these tools into platforms like cryoSPARC and RELION has streamlined high-throughput structure determination and significantly elevated reconstruction quality.

The establishment of large-scale standardized datasets has been pivotal for advancing AI-based particle picking in cryo-EM. CryoPPP, developed by Dhakal et al. (2023), is the largest expert-curated resource to date, encompassing 9,893 micrographs and more than 400,000 annotated particles across 34 protein datasets. It now serves as a benchmark platform for training and evaluating new particle-picking algorithms under consistent conditions. Building directly on this foundation, CryoTransformer was introduced as a state-of-the-art transformer-based model that employs attention mechanisms to capture long-range dependencies in cryo-EM micrographs (Dhakal et al., 2024). Comprehensive benchmarking on CryoPPP and EMPIAR datasets showed that CryoTransformer achieved F1-scores ranging from 0.65 to 0.85 and consistently produced 3D reconstructions in the 4–6 Å resolution range, outperforming established methods such as Topaz and crYOLO. Together, CryoPPP and CryoTransformer exemplify how standardized datasets and next-generation deep learning architectures are reshaping one of the most labor-intensive steps in cryo-EM image analysis.

The impact of cryo-EM on structural biology cannot be overstated. By enabling the study of previously intractable targets, cryo-EM has democratized structural biology, making it accessible to researchers worldwide. Its ability to determine multiple conformational states has provided unprecedented insights into macromolecular dynamics, revealing molecular processes such as enzyme catalysis, signal transduction, and viral infection. As cryo-EM continues to evolve, it will have an even greater impact on drug development, biotechnology, and our understanding of fundamental biological processes.

While cryo-EM has expanded access to high-resolution structure determination beyond crystallography, significant barriers remain, including high capital and maintenance costs of instrumentation, the need for specialized technical expertise in sample preparation and data collection, and the requirement for substantial computational infrastructure for data processing and reconstruction (Carroni and Saibil, 2016; Cheng et al., 2015). These factors can limit accessibility, particularly for smaller labs or in low-resource settings.

3.2 Artificial intelligence (AI) in structure prediction

The development of AI algorithms, such as AlphaFold and RoseTTAFold, has revolutionized structural biology by enabling the prediction of high-accuracy protein structures directly from amino acid sequences. These tools are based on deep learning and leverage large databases of high-quality protein structures to predict 3D structures with remarkable accuracy, often surpassing experimental approaches (Jumper et al., 2021). This has not only accelerated the pace of discovery but has also democratized access to structural information, allowing researchers worldwide to explore the molecular basis of life.

One of the most significant breakthroughs in AI-driven structure prediction occurred in 2021 with the launch of the AlphaFold Protein Structure Database, which contains predicted structures for nearly the entire human proteome. This database serves as a valuable resource for functional annotation and drug discovery (Varadi et al., 2022). For example, AlphaFold’s prediction of the SARS-CoV-2 orphan protein ORF8 provided crucial insights into its role in immune evasion (Gordon et al., 2020). This breakthrough confirmed the potential of AI to drive rapid advancements in structural discovery and therapeutic development.

While AI-based methods such as AlphaFold and RoseTTAFold have revolutionized protein structure prediction, significant limitations remain especially for intrinsically disordered regions (IDRs), large multiprotein complexes, and novel folds. Quantitative benchmarks show that for well-ordered protein domains, AlphaFold2 achieves atomic-level accuracy (RMSD <1.5 Å), but for IDRs and flexible linkers, RMSD often exceeds 5–10 Å and structural predictions can be unreliable or misleading (Robin et al., 2021; Tunyasuvunakool et al., 2021). For example, results from CASP14 and CASP15 showed consistent declines in AlphaFold’s accuracy when modeling highly dynamic regions or novel folds not represented in the training data (Kryshtafovych et al., 2023). Tools for probing intrinsically disordered proteins (IDPs) are advancing rapidly. For example, IDPConformerGenerator generates large and diverse conformational ensembles of disordered states, enabling systematic sampling of backbone and side-chain variation (Teixeira et al., 2022). In parallel, deep-learning-based methods such as ALBATROSS directly predict ensemble-averaged dimensions including radius of gyration, end-to-end distance, and polymer scaling exponents from sequence information (Lotthammer et al., 2024). These computational benchmarks enable comparison of AI-predicted structural models of IDRs. Yet challenges remain: studies have shown that flexible termini (e.g., in p53) are frequently mis-modeled; multi-subunit complexes may omit relevant dynamic conformers; and protein–protein or protein-nucleic acid interfaces in disordered regions are often only partially captured (Evans et al., 2022; Jumper et al., 2021; Ruff and Pappu, 2021). Together, these observations emphasize the need for integrating ensemble-based computational approaches with experimental validation (such as NMR, SAXS, or HDX-MS) to achieve realistic representations of IDPs. Additionally, while AlphaFold’s error metrics (e.g., pLDDT, PAE) provide confidence estimates, they can be misinterpreted, and high scores do not always correlate with biological accuracy, particularly in overfitted or underrepresented regions (Akdel et al., 2022). Addressing these limitations will require improved datasets, better calibration of confidence metrics, and integration with experimental data for challenging targets.

A key factor underlying the success of AlphaFold is its use of multiple sequence alignments (MSAs) to capture evolutionary covariation between residues, which provides constraints for predicting inter-residue distances and orientations. The depth and diversity of the MSA strongly influence predictive accuracy. When large, diverse sets of homologous sequences are available, AlphaFold2 achieves near-atomic accuracy for many protein domains, often with root mean square deviations (RMSDs) of less than 1.5 Å relative to experimental structures (Jumper et al., 2021). However, when only a limited number of homologs can be identified, such as for orphan proteins or rapidly evolving viral proteins, predictive performance drops substantially. Benchmarking in CASP14 and CASP15 consistently showed that proteins with shallow MSAs yielded lower-confidence models, with errors particularly pronounced in flexible loops, novel folds, or intrinsically disordered regions (Akdel et al., 2022; Kryshtafovych et al., 2023). In such cases, AlphaFold’s confidence metrics (pLDDT, PAE) may overestimate structural reliability, and experimental validation or hybrid modeling approaches remain essential. This limitation highlights that AlphaFold’s power is fundamentally coupled to the availability of evolutionary information, and regions of sequence space lacking homologs remain challenging.

In addition to sequence-based predictors such as AlphaFold and RoseTTAFold, artificial intelligence is increasingly applied directly to cryo-EM data analysis, spanning particle picking, density map interpretation, and heterogeneity reconstruction. At the front end of the workflow, AI-driven particle picking methods such as Topaz, crYOLO, CryoSegNet, and CryoTransformer have greatly improved the accuracy and throughput of identifying particles in noisy micrographs, thereby reducing one of the most labor-intensive bottlenecks in single-particle analysis (Dhakal et al., 2024). For atomic model building, tools like DeepTracer, DeepMainmast, and ModelAngelo leverage convolutional and graph neural networks to trace main-chain coordinates and assign residues directly from cryo-EM density maps, producing models that rival expert-curated reconstructions (Jamali et al., 2024; Terashi et al., 2024). Supporting this, datasets such as Cryo2StructData, comprising over 7,600 labeled experimental cryo-EM density maps, now provide the large-scale training resources needed to train next-generation AI models for reliable automated structure determination (Giri et al., 2024). AI has also advanced heterogeneity analysis, with CryoDRGN, a deep generative network that learns continuous distributions of 3D structures to reveal conformational variability across particle ensembles (Zhong et al., 2021), and CryoFIRE, which applies amortized inference to jointly estimate particle orientations and conformational states, enabling efficient reconstruction of flexible complexes without exhaustive alignment (Levy et al., 2022). Collectively, these developments demonstrate how AI is reshaping cryo-EM pipelines end-to-end, from raw image preprocessing through to final atomic models and ensemble characterization. The role of AI in particle picking, reconstruction, and heterogeneity analysis, since the Figure 3 visually demonstrates exactly that workflow.

Figure 3

Figure 3. Integration of AI into cryo-EM data processing.

Schematic overview showing how deep learning methods are applied across cryo-EM workflows, including motion correction, CTF estimation, particle picking (e.g., crYOLO, Topaz, DeepPicker), 2D/3D classification and selection (e.g., Cinderella), and final map interpretation and model building (e.g., DeepTracer, cryoDRGN). AI tools enhance accuracy, speed, and automation throughout the cryo-EM pipeline.

Another important limitation of current AI-based structure prediction tools is their limited consideration of experimental and environmental conditions. Most methods, including AlphaFold2/3 and RoseTTAFold, generate a single (or dominant) structure based on sequence and evolutionary data, effectively modeling the protein in a “standard” context and neglecting factors such as pH, ionic strength, redox environment, ligand or cofactor presence, and post-translational modifications. However, many proteins adopt different conformations depending on local conditions for example, a protein’s structure at pH 5 can differ substantially from its structure at pH 7 (Jumper et al., 2021; Madhavi Sastry et al., 2013). Addressing these real-world biochemical variables is an important area for future AI model development, ideally through explicit conditioning on environmental parameters or integration with experimental datasets.

The rapid adoption of AI-predicted models in biomedical research also raises important ethical and practical considerations. Overreliance on low-confidence or unvalidated structural models in drug discovery campaigns, for example, risks misguiding compound design or screening, potentially leading to wasted resources or misleading biological conclusions. It is critical for practitioners to interpret AI-generated confidence metrics cautiously, validate key findings with experimental data where possible, and transparently communicate uncertainty. Community standards and guidelines for responsible AI structure use are needed to ensure scientific integrity and reproducibility (Akdel et al., 2022).

AI techniques have also been applied to predict the conformational ensembles and transient structural propensities of intrinsically disordered proteins (IDPs), rather than fixed, static atomic structures, advanced methods such as NMR spectroscopy and integrative computational modeling are employed. These approaches characterize the range of accessible conformations and dynamic behavior in solution, reflecting the intrinsic heterogeneity that distinguishes IDPs from structured proteins (Jensen et al., 2013; Uversky, 2019). In 2022, RoseTTAFold predicted the structures of neurodegenerative disease-related proteins such as tau and α-synuclein, shedding new light on the molecular basis of protein aggregation and its role in Alzheimer’s and Parkinson’s diseases (Šali and Blundell, 1993). Similarly, in 2023, AlphaFold-Multimer predicted the structure of the SARS-CoV-2 spike protein in complex with human ACE2, revealing key interactions relevant for therapeutic development (Baek et al., 2021).

Deep learning-based approaches such as AlphaFold 2 (AF2) have transformed protein structure prediction by leveraging attention-based neural networks to achieve near-experimental accuracy for globular proteins (Jumper et al., 2021). Its performance in CASP14 established a new benchmark, particularly for monomeric protein folds (Figure 3). However, AlphaFold 3 (AF3) represents a major step forward, capable of predicting not just protein structures but also protein–protein, protein–nucleic acid, and protein–ligand interactions using a unified framework. AF3 integrates structural, chemical, and biophysical constraints, enabling the modeling of functional assemblies and molecular recognition events (Fang et al., 2025). The capabilities of AF3 are summarized in Table 1, distinguishing currently supported applications from prospective claims.

Table 1

Table 1. Scope of AlphaFold3 Predictions: Current vs Emerging Capabilities.

AlphaFold2 (AF2) employs an attention-based architecture with two core components: the MSA track, which captures evolutionary covariation, and the pair representation track, which encodes residue–residue geometry. These interact iteratively through the Evoformer module, and the Structure Module outputs 3D coordinates. This design explains AF2’s breakthrough accuracy in CASP14, achieving near-atomic resolution for most globular proteins. AlphaFold3 (AF3) extends the framework by incorporating multimodal inputs (protein sequences, nucleic acids, ligands) and graph-based chemical embeddings, enabling unified modeling of protein–protein, protein–nucleic acid, and protein–ligand interactions. While still emerging, AF3 benchmarks show improved complex prediction, though ligand-binding remains qualitative. Figures 4, 5 schematically illustrate the AF2 Evoformer–Structure Module pipeline (Figure 4) and AF3’s expanded multimodal framework (Figure 5).

Figure 4

Figure 4. Schematic representation of the AlphaFold 2 architecture for protein structure prediction.

Figure 5

Figure 5. Schematic representation of the AlphaFold 3 architecture for protein structure prediction.

The contribution of AI-driven structure prediction to structural biology has been profound. By providing accurate models for challenging targets such as IDPs and large complexes, AI has complemented experimental approaches and expanded the scope of structural biology. AI has also accelerated drug discovery processes, enabling researchers to identify potential drug targets and design new drugs with greater efficiency. As AI continues to evolve, it will play an increasingly important role in structural biology, opening new directions for future research.

While AI-based predictions have shown transformative accuracy, their responsible use requires careful interpretation of confidence metrics, cross-validation with experimental data, and awareness of pitfalls such as over-interpretation or training leakage. These considerations are summarized in Box 1: Responsible Use of AI-Predicted Structures. Despite their transformative impact, AI-based protein structure prediction tools remain susceptible to reproducibility and generalizability challenges, primarily due to potential biases and limitations in the training datasets. Recent benchmarking efforts have revealed that models like RoseTTAFold and AlphaFold2 may produce less reliable or less reproducible predictions for protein families, domains, or assemblies that are underrepresented in current structural databases. This highlights the ongoing need for rigorous benchmarking, transparency in reporting, and continued expansion and curation of training data sets (Akdel et al., 2022; Kryshtafovych et al., 2023).

3.3 Integrative and hybrid methods

Integrative and hybrid methods combine data from multiple techniques, such as cryo-EM, NMR, and small-angle X-ray scattering (SAXS), to build comprehensive models of macromolecular structures. These approaches are particularly useful for studying complex and heterogeneous systems that cannot be fully characterized using a single technique (Maeshima et al., 2014). By integrating data from multiple sources, researchers can generate detailed models that provide a complete picture of macromolecular structures in their biological contexts.

One of the most significant applications of integrative methods has been in the study of the nuclear pore complex (NPC), a massive molecular assembly that regulates the transport of molecules between the nucleus and the cytoplasm. An integrative model of the NPC was reconstructed using cryo-EM, NMR, and biochemical data, providing unprecedented insights into its structure and function (Schotte et al., 2003). This model revealed the molecular mechanisms underlying the NPC’s ability to control transport across the nuclear envelope.

Integrative methods have also been used to study chromatin structure, the complex of DNA and proteins that packages the genome. In 2023, cryo-EM, SAXS, and computational modeling were combined to investigate the arrangement and dynamics of chromatin, shedding light on how chromatin structure regulates gene expression and epigenetic regulation (Mosalaganti et al., 2022).

Mass spectrometry (MS) has emerged as a key structural tool that complements traditional and advanced techniques in structural biology. Approaches such as cross-linking mass spectrometry (XL-MS), native MS, and hydrogen–deuterium exchange MS provide critical information on the architecture, stoichiometry, dynamics, and interaction networks of macromolecular assemblies often in conditions closer to physiological environments (Lenz et al., 2021; Sinz, 2018). When integrated with cryo-EM, NMR, or computational modeling, MS-derived constraints facilitate interpretation of ambiguous regions and help to characterize systems challenging for high-resolution methods alone.

The impact of integrative and hybrid approaches on structural biology has been significant. By enabling the study of complex systems that cannot be fully characterized using a single technique, these methods have expanded the scope of structural biology and provided new tools for addressing challenging biological questions. They have also provided new insights into the dynamic nature of macromolecular biology, revealing the molecular mechanisms underlying complex processes such as gene expression and cellular signaling.

Box 1 Responsible use of AI-predicted structures.
AI-based tools such as AlphaFold2 (AF2) and AlphaFold3 (AF3) have accelerated structural biology, but responsible use requires caution. Below are practical guidelines for researchers and drug discovery practitioners.

Confidence metrics

• pLDDT thresholds:

• >90 → very high confidence (atomic-level reliability).

• 70–90 → moderate confidence (secondary structure generally correct, side chains less certain).

• <70 → treat cautiously (often disordered or misfolded).

• PAE (Predicted aligned error):

• <5 Å → reliable domain/domain positioning.

• >10 Å → relative orientations uncertain, especially in multi-domain or multimeric complexes.

Cross-validation with experiments

• XL-MS: checks residue–residue distances.

• HDX-MS: validates flexible or protected regions.

• SAXS: confirms overall shape/dimensions.

• Mutagenesis & biochemical assays: test pocket or interface predictions.

IDPs and flexible regions

• AI models of intrinsically disordered proteins (IDPs) represent hypotheses, not fixed structures.

• Use ensemble-based approaches (e.g., IDPConformerGenerator, ALBATROSS) + NMR/smFRET for realistic modeling.

Pitfalls to avoid

• Training leakage: well-studied proteins may appear artificially “predicted.”

• Over-interpretation: confident scores can still misplace domains or interfaces.

• Environmental neglect: AF2/AF3 predictions usually ignore pH, ions, ligands, PTMs.

✓ Checklist for drug discovery applications

Before applying AF2/AF3 models in drug discovery, ensure:

• Binding pocket pLDDT >80.

• Cross-validation with mutagenesis, soaking, or NMR CSPs.

• PAE maps confirm correct inter-domain geometry.

• Comparison with orthogonal data (SAXS, cryo-EM, crystallography, homology).

• Flexible loops/IDRs are not solely used for druggability.

• Iterative refinement with MD, docking, MM/GBSA is performed.

3.4 Time-resolved structural biology

The term “time-resolved techniques” in structural biology encompasses a diverse set of experimental approaches that operate over distinct temporal scales, each revealing different types of molecular motion or reactivity. For example, time-resolved X-ray crystallography methods including serial femtosecond crystallography at X-ray free-electron lasers (XFELs) capture atomic and electronic changes on the femtosecond to picosecond (10⁻¹⁵–10⁻¹²s) timescale, enabling visualization of ultrafast chemical reactions and bond rearrangements. By contrast, time-resolved cryo-EM is typically limited to millisecond to second (10⁻³–1s) resolution, which is suitable for observing large conformational changes, domain motions, or assembly/disassembly processes in complex macromolecular machines. Techniques such as NMR relaxation and stopped-flow spectroscopies probe dynamics from microseconds to seconds, enabling characterization of backbone motions, folding, or ligand binding events. Because these time scales differ by many orders of magnitude, their applications to biological questions and their technical implementations are fundamentally distinct (Chapman et al., 2011; Fischer et al., 2015; Schanda and Brutscher, 2005).

Time-resolved structural biology aims to capture macromolecular processes in real-time, such as enzyme catalysis, protein folding, and conformational transitions. Techniques such as time-resolved crystallography, time-resolved cryo-EM, and ultrafast spectroscopy allow researchers to visualize macromolecules in motion and understand the molecular mechanisms underlying complex biological events (Aitken et al., 2008). Although time-resolved methods promise to reveal ultrafast dynamics, current implementation in TR-crystallography and TR-cryo-EM remain bounded by mixing, initiation, and data collection constraints. For example, Tenboer et al. (Tenboer et al., 2014) used serial femtosecond crystallography on microcrystals of photoactive yellow protein (PYP), initiating the reaction with light and resolving intermediates to 1.6 Å at the 10 ns to 100 ms timescale using XFEL pulses (Tenboer et al., 2014)this demonstrates what TR crystallography can achieve under ideal conditions. In cryo-EM, recent work using a PDMS-based microfluidic chip achieved tens of milliseconds mixing and freezing, enabling capture of early reaction intermediates though at lower resolution and with stringent demands on sample prep and grid quality (Bhattacharjee et al., 2023). These studies highlight major bottlenecks: reaction initiation speed, reproducible grid manufacture, dose control, and sample thickness.

One of the most significant examples of time-resolved structural biology is the study of the photosynthetic reaction center. In recent years, time-resolved crystallography and serial femtosecond X-ray crystallography (TR-SFX) have been used to map the sequence of electron transfer and water oxidation events in photosystem II. For example, Li et al. (2021) captured the S₁→S₂ transition at ambient temperature, revealing water movements and channel rearrangements, while Bhowmick et al. (2023) provided snapshots of the S₃→[S₄]→S₀ transition including ligand rearrangement, the Mn₄CaO₅ cluster geometry, and proton/electron release pathways offering critical insight into the final steps of O₂ formation. Li et al. (2024) revealed structural dynamics of photosystem II during S-state transitions tracking water insertion, proton release, and O-O bond formation that deepen our understanding of photosynthetic energy conversion and inform designs of artificial photosynthetic systems.

Time-resolved cryo-EM has also been used to study GPCR signaling. In 2024, conformational changes in GPCRs during ligand binding and subsequent G protein activation were resolved using time-resolved cryo-EM, providing new insights into the molecular mechanism of signal initiation by GPCRs (Papasergi-Scott et al., 2024). Beyond the application to GPCR conformational dynamics, cryo-EM has enabled visualization of intermediate states in diverse systems. Milestones include sub-second capture of ribosomal translocation steps (Fischer et al., 2015), post-DNA packaging conformational transitions in viral connector/portal complexes (Orlov et al., 2022) and remodeling during capsid maturation (Li et al., 2023). These studies showcase time-resolved cryo-EM’s expanding capacity for mapping transient, functionally relevant conformations in previously inaccessible targets.

The impact of time-resolved structural biology on our understanding of biological processes has been profound. By capturing processes in real-time, these techniques have provided unprecedented insights into the molecular mechanisms underlying complex biological events. They have also informed the development of new drugs and biomaterials, opening new avenues for future research.

3.5 Emerging techniques

In addition to the methodologies discussed above, several emerging techniques are expanding the scope of structural biology. These include single-molecule microscopy, mass photometry, and correlative light and electron microscopy (CLEM). These tools provide new ways to study macromolecular structures and dynamics, offering unprecedented insights into biological processes. While single-molecule microscopy and CLEM are now widely adopted in cell biology and have a substantial methodological pedigree, they remain “emerging” within the mainstream of high-resolution, integrative structural biology, where incorporation into multi-modal workflows and large-scale studies is still expanding (Beck and Baumeister, 2016; Hoffman et al., 2020). Recent technical advances continue to broaden their impact and accessibility for complex biological investigations.

Single-molecule microscopy has been used to probe the motions of individual proteins and nucleic acids during translation. For example, in 2023 a smFRET study monitored inter-subunit rotation of ribosomes during translation which contributes to our understanding of how ribosomal dynamics relate to folding and function (Das et al., 2023). This technique is particularly useful for studying intrinsically disordered proteins and their interactions with ligands and binding partners.

Mass photometry is a technique that measures the mass of individual molecules in solution, providing insight into assembly and stoichiometry of macromolecular complexes. For example, in 2021 mass photometry was applied to SpCas12f1 and AsCas12f1 CRISPR effectors to resolve their stepwise assembly: first as apo-protein, then forming binary complexes with guide RNA, and then ternary complexes with target DNA (Bigelyte et al., 2021). This technique is powerful for studying protein-nucleic acid interactions and complex assembly (Asor and Kukura, 2022).

Correlative light and electron microscopy (CLEM) combines the spatial resolution of electron microscopy with the specificity of fluorescence microscopy. In recent years, CLEM has been successfully used to study the structure and function of mitochondria in mammalian cells, revealing the dynamic arrangement of cristae and their interactions with other organelles (Jung and Mun, 2019). CLEM is a powerful tool for studying cellular organelles and macromolecular complexes in their native environments. Optical super-resolution microscopy techniques such as STED, PALM, and STORM have enabled fluorescence imaging at nanometer-scale precision. Their integration with electron microscopy through correlative light and electron microscopy (CLEM) workflows known as super-resolution CLEM (SR-CLEM) has become a powerful approach in structural biology. These workflows allow the molecular specificity of fluorescence labeling to be directly correlated with the ultrastructural resolution of EM, enabling the detailed localization of proteins within native cellular environments. Early conceptual development of SR-CLEM highlighted its promise for bridging the resolution gap between light and electron microscopy (de Beer et al., 2023), and later applications, such as the work by Joosten et al. (2018), demonstrated its utility in mapping subcellular features like dendritic cell podosomes. Most recently, Marshall et al. (2023) emphasized how advances in multimodal probes and cryo-fixation technologies have expanded the versatility and resolution of CLEM, making it indispensable for dynamic structural investigations in situ. Although super-resolution microscopy can be used independently for structural insights, its primary value in structural biology is as part of multimodal CLEM studies that bridge the resolution gap between molecular imaging and EM ultrastructure.

The impact of these emerging techniques on structural biology has been profound. By providing new ways to study complex biological structures, these tools have expanded the scope of structural biology and opened exciting new avenues for future research. The strengths and limitations of various structural characterization techniques are summarized in Table 2.

Table 2

Table 2. Overview of major structural biology techniques.

4 Applications of high-resolution structural characterization

The development of advanced techniques for characterizing macromolecular structures has transformed our ability to study biological processes at the molecular level. These advancements have facilitated breakthroughs in drug discovery, enzymology, nanotechnology, and the study of disease mechanisms. By providing atomistic insights into the structure and function of proteins, nucleic acids, and other macromolecules, these techniques have enabled the development of new therapeutics, engineered enzymes, biomaterials, and a deeper understanding of disease at the molecular level.

4.1 Drug discovery

In drug discovery, structure-based drug design (SBDD) is a cornerstone of modern pharmacology. By leveraging high-resolution structures of macromolecules, researchers can identify binding pockets and design high-affinity, high-efficacy inhibitors. For example, the X-ray crystallographic determination of the BRAF kinase structure revealed the molecular mechanism of its activation in melanoma, leading to the development of vemurafenib, a targeted therapy for BRAF-mutant melanoma (Long et al., 2014). More recently, cryo-EM was used to resolve the structure of HER2 kinase in complex with a novel inhibitor, paving the way for next-generation therapies for HER2-positive breast cancer (Bornscheuer et al., 2012).

While structure-based drug design (SBDD) and AI-driven platforms have accelerated the identification of therapeutic candidates, several real-world limitations persist. For example, although SBDD enabled the development of vemurafenib for BRAF V600E-mutant melanoma, rapid emergence of resistance driven by MAPK pathway reactivation or alternative mutations highlights the ongoing gap between structural prediction and durable clinical efficacy (Long et al., 2014). Similarly, commercial AI drug design efforts, such as Isomorphic Labs’ reported pipelines, face challenges including dependency on the depth and diversity of training datasets, potential model bias, and limited interpretability of predictions, with few compounds yet reaching advanced clinical stages (Walters and Murcko, 2020). Finally, although cryo-EM has expanded opportunities for structure-guided development of biologics like antibodies, the field faces scalability constraints due to high equipment costs, specialized labor, and throughput bottlenecks in sample preparation and data processing, which currently limit high-volume industrial application (Mao, 2025; Punjani et al., 2017; Renaud et al., 2018).

Structural biology has also played a critical role in antiviral drug development. The structure of the SARS-CoV-2 main protease (Mpro), determined using cryo-EM and X-ray crystallography, enabled the design of nirmatrelvir, an antiviral therapeutic that inhibits viral replication (Owen et al., 2021; Zhang et al., 2020). Similarly, NMR spectroscopy has been used to characterize the dynamics of HIV-1 protease, leading to the development of a new class of drugs with reduced drug resistance (Zeltins, 2013). These advancements have accelerated the development of targeted therapies, reducing the cost and time required for drug discovery while improving efficacy and reducing side effects. The integration of AI-driven structure prediction tools, such as AlphaFold, has further enhanced these efforts by providing high-fidelity models of drug targets and their interactions with small molecules (Jumper et al., 2021).

4.2 Enzymology

In enzymology, structural characterization has enabled the engineering of enzymes for industrial applications, including biofuel production, waste management, and pharmaceutical manufacturing. For example, high-resolution X-ray structures of cellulases have directly enabled enzyme engineering for biofuels: structure-guided recombination and crystallography of GH5 endoglucanases produced chimeras with higher thermostability and activity (Chang et al., 2016), and comparative/structure-based redesign of GH7 cellobiohydrolases improved processive activity on cellulose (Taylor et al., 2018). Although cryo-EM is increasingly applied to thermostable enzymes, definitive cryo-EM studies linking loop stabilization to simultaneous gains in cellulase stability and catalysis are not yet established; instead, cryo-EM has provided high-resolution models for other thermostable enzymes, underscoring feasibility for future cellulase work (Sobhy et al., 2022).

Lipases, industrially important enzymes used in detergents and pharmaceuticals, have been engineered using structural and dynamics insights. Rather than a single NMR-only mapping study, recent work combines structural analysis with computation to target flexible/functional regions (e.g., lids/tunnels) and improve specificity or stability: a 2023 review documents successful computer-aided lipase engineering strategies spanning structure prediction, docking, and MD-guided mutagenesis (Cheng and Nian, 2023). For operation in organic media, CALB variants were evaluated in organic solvent with combined experiments and computation; mutations near the active site reduced sensitivity to water activity and preserved activity in non-aqueous conditions (Tjørnelund et al., 2023). Mechanistic QM/MM MD on CALB further clarified how environment (pH) and catalytic geometry govern reactivity and regioselectivity principles that guide rational variant design (Świderek et al., 2023). Together, these studies show that integrative, computation-informed engineering (rather than AI alone) is currently the most evidenced path to lipases with broadened specificity and improved performance in challenging solvent systems.

Hemoglobin has been well resolved by cryo-EM alone, but no peer-reviewed studies have yet demonstrated a direct AI–cryo-EM synergy in its structural analysis. In contrast, cytochrome P450 has been examined by combining AlphaFold2 predictions with cryo-EM density maps to explore alternative conformational states, underscoring how AI can complement experimental data (Urban and Pompon, 2022). To better represent the scope of AI in cryo-EM, more robust examples include the Fanconi anemia core complex, where deep learning predictions enabled de novo model building into a 4.6 Å cryo-EM map that was otherwise uninterpretable (Farrell et al., 2020), CryoDRGN, which applied generative neural networks to uncover continuous conformational heterogeneity in ribosomes and spliceosomes (Zhong et al., 2021), and the nuclear pore complex, where AlphaFold2-derived models filled unresolved regions in cryo-EM maps to achieve near-complete reconstruction of this ∼120 MDa. Together, these cases provide clearer evidence of how AI directly enhances cryo-EM, from improving model building to revealing hidden structural states.

4.3 Nanotechnology

The design of nanodevices and biomaterials derived from macromolecular structures has opened new frontiers in nanotechnology. For example, cryo-EM and crystallographic studies of ferritin nanocages have enabled the development of ferritin-based nanoparticles for targeted drug delivery and imaging, exploiting their highly stable cage architecture and ability to encapsulate diverse cargos (Lee et al., 2022; Mohanty et al., 2022). Viral-like particles (VLPs) are another emerging class of nanomaterials: structural studies of bacteriophage Qβ VLPs have clarified their assembly pathways and stability (Shaw et al., 2022),and such scaffolds are now being investigated as potential platforms for mRNA vaccine delivery (Lin et al., 2024). Structural insights have also accelerated progress in biosensing. The crystallographic determination of glucose oxidase provided the basis for the first highly sensitive blood glucose biosensors in diabetes care, while more recent NMR analyses of glucose oxidase variants revealed conformational dynamics that guided the design of improved, durable biosensors (Alatzoglou et al., 2023; Mahamid et al., 2016).

In tissue engineering, structural studies have informed the design of biomaterials for regenerative medicine. The cryo-EM determination of the structure of collagen has enabled the development of collagen scaffolds for tissue regeneration (Neutze et al., 2000). In 2021, an integrative model of the interaction between collagen and fibronectin led to the development of a biomaterial for wound healing (Frantz et al., 2010). These advancements have transformed medicine and materials engineering, enabling the development of new drugs, diagnostics, and regenerative therapies.

4.4 Understanding disease mechanisms

Structural characterization has provided critical insights into the molecular basis of disease, facilitating the development of targeted therapies and diagnostics. For example, NMR determination of the structure of the prion protein revealed the molecular basis of its misfolding in prion diseases such as Creutzfeldt-Jakob disease (Prusiner, 1998). Cryo-EM resolution of misfolded tau protein structures has shed light on their role in neurodegenerative diseases (Fitzpatrick et al., 2017), while cryo-EM determination of amyloid-β fibrils has guided therapeutic strategies targeting aggregation in Alzheimer’s disease (Gremer et al., 2017). Recently, solid-state NMR revealed the structure of α-synuclein fibrils, offering insights into their toxicity in Parkinson’s disease (Tuttle et al., 2016). Beyond elucidating fibril morphologies, high-resolution cryo-EM structures of tau aggregates have directly facilitated rational PET tracer design with improved selectivity for disease-associated folds. For example, the conformation-specific features of AD tau fibrils guided the development and optimization of PET ligands such as [18F] PI-2620, enhancing early diagnosis and monitoring of tauopathies (Fitzpatrick et al., 2017; Kroth et al., 2019).

Structural studies have also played a critical role in combating viral infections. The cryo-EM determination of the SARS-CoV-2 spike protein structure revealed the molecular basis of viral entry into host cells, guiding the development of vaccines and antiviral therapies (Wrapp et al., 2020). In 2021, cryo-EM resolved the structure of the Ebola virus glycoprotein, informing the development of a novel antiviral drug (Lee et al., 2008). These advancements have revolutionized our understanding of complex diseases, enabling the development of targeted therapies and diagnostics. The integration of AI-driven structure prediction and integrative modeling has further enhanced our ability to study disease mechanisms and develop effective treatments (Jumper et al., 2021).

Structural biology has revolutionized drug discovery, enabling the development of targeted therapies such as vemurafenib for BRAF-mutant melanoma and nirmatrelvir for COVID-19 (Table 3). Recent research has demonstrated the power of structural biology in addressing global health challenges. For example, the structure of the SARS-CoV-2 main protease, determined using Cryo-EM and X-ray crystallography, enabled the rapid development of nirmatrelvir, an antiviral drug for COVID-19 (Table 4).

Table 3

Table 3. Applications of structural characterization.

Table 4

Table 4. Seminal contributions and recent advances in structural biology.

5 Challenges and future directions

Notwithstanding significant advances in structural characterization techniques, a range of challenges still impedes our complete understanding of macromolecular structure and function in living matter. Overcoming them will require new thinking and new technology creation. In parallel, integration of new tools, such as artificial intelligence (AI) and machine learning, holds out hope for widening horizons in structural biology. In the following, we review significant obstacles and future trends in the field.

5.1 Overcoming challenges in membrane proteins and intrinsically disordered proteins

Membrane proteins and intrinsically disordered proteins (IDPs) remain among the most difficult targets in structural biology due to their inherent dynamics, instability, and, for membrane proteins, pronounced obstacles in crystallization and sample preparation. Recent years have witnessed notable advances that address these barriers. For membrane proteins, innovations in lipidic cubic phase (LCP) crystallization, including the introduction of novel lipids such as 7.10 MAG, have significantly expanded the range of targets amenable to in meso crystallization and improved success rates in obtaining high-resolution structures (Krawinski et al., 2024). In parallel, methods such as the VIALS approach now enable the high-throughput generation of dense microcrystals for serial crystallography, streamlining the structural analysis of challenging membrane proteins (Birch et al., 2023). The rapid adoption of single-particle cryo-EM with improved detectors and computational tools has also led to a dramatic increase in the number and resolution of membrane protein structures, now routinely achieving resolutions better than 3 Å for complex assemblies (Thangaratnarajah et al., 2022).

The field of IDP research has similarly advanced through new experimental and computational methodologies. High-throughput platforms based on cell-free protein crystallization (CFPC) are enabling rapid structure determination and systematic assessment of factors that stabilize or modulate IDP conformational ensembles (Bastida et al., 2023; Kojima et al., 2024). Analytical methods that combine molecular dynamics, single-molecule FRET, and advanced NMR spectroscopy now routinely characterize the residue-level dynamics and binding-competent conformations of disordered regions, illuminating their mechanisms in signal transduction and disease (Bastida et al., 2023; Orand and Jensen, 2025). In addition, “proteomimetic” scaffolds and targeted biophysical screens are being developed to selectively trap or modulate functional IDP conformations, opening new avenues for therapeutic targeting of these previously intractable proteins (Nguyen et al., 2025). Collectively, these innovations continue to break down longstanding technical barriers, deepening our understanding of these essential biomolecules in health and disease.

5.2 Integrating structural information with functional and dynamical analysis

Whereas macromolecular structure reveals significant information regarding macromolecular topology, a complete understanding of function requires integration of information regarding structure, function, and dynamics. Most processes in living cells, such as enzyme catalysis, transmembrane signal transduction, and protein folding, entail complex conformational processes, whose complete description cannot be achieved with static structures alone. Time-resolved techniques, such as time-resolved crystallography and cryo-EM, have begun to circumvent such difficulty by capturing snapshots of processes in motion at an atomic level. In 2010, time-resolved crystallography was used to study the catalytic cycle of cytochrome c oxidase, mapping the sequence of electron and proton transfers during oxygen reduction (Kaila et al., 2010). Recent advances in single-molecule FRET have also illuminated protein folding dynamics, yielding new insights into macromolecular interactions (Hartmann et al., 2023; Schuler et al., 2016).

5.3 Expanding the role of AI and machine learning in structure prediction and refinement

The use of AI and machine learning in structural biology has already seen a transformational impact, with examples including the success of AlphaFold and RoseTTAFold in predicting structures with high accuracy (Jumper et al., 2021). Not only have these tools accelerated the pace of structure discovery, but access to structures has democratized, and researchers worldwide can explore the molecular basis of life. In 2021, AlphaFold achieved high accuracy in predicting the structures of folded proteins, but its performance on IDPs and macromolecular complexes remained limited (Ruff and Pappu, 2021). In 2023, machine learning methods such as EMReady were applied to cryo-EM density maps to refine local and non-local features and significantly improve map-model correlation and interpretability, especially for maps in the 3–6 Å resolution range (He et al., 2023).

5.4 Developing new technologies for in situ structural characterization

One of the most challenging and exciting frontiers in structural biology is developing tools for studying macromolecules in their native environment within living cells. Most conventional methodologies in structural biology require isolating and purifying macromolecules, a manipulation that can distort function and structure. In situ characterization of structure, in which macromolecules are analyzed in whole cells, holds out hope for overcoming such restrictions and providing a truer view of the environment in life. Recent advances in cryo-electron tomography (cryo-ET) have made it possible to visualize macromolecular machines directly in cells, providing critical insights into their native organization. Although cryo-ET delivers molecular or mesoscale detail rather than near-atomic resolution, most cellular tomograms are reconstructed at 3–8 nm, limited by lamella thickness, signal-to-noise ratios, and radiation damage. Sub-nanometer resolution in intact cells is not yet realistic, although subtomogram averaging can improve detail for abundant complexes. Recent advances such as focused ion beam (FIB) milling for lamella thinning and Volta phase plates for contrast enhancement have further improved data quality. For example, Balyschew et al. (Balyschew et al., 2023) demonstrated ∼3 Å resolution for favorable targets using FIB-milled lamellae and subtomogram averaging, while Berger et al. (2023) showed how automated FIB workflows facilitate higher-resolution tomography in crowded environments. Fung et al. (2023) applied genetically encoded tags with AI-based detection to improve protein localization, and Zhou and Lok (2024) highlighted both the promise and limits of visualizing viral assembly in situ. Together, these studies underscore that atomic detail is largely restricted to isolated complexes or averaged subtomograms, while whole-cell reconstructions provide mesoscale structural insights.

Recent advances in correlative light and electron microscopy (CLEM) have enabled detailed visualization of mitochondrial ultrastructure and dynamic interactions with other organelles in mammalian cells, linking molecular specificity to the in situ ultrastructural context (Jiang et al., 2022; Jung and Mun, 2024). Progress moving forward will depend on developing new modalities such as super-resolution CLEM workflows and improved embedding/fiducial labeling protocols and enhancing algorithms for registration, segmentation, and interpretation of complex in situ data (Franek et al., 2024; Jung and Mun, 2024).

6 Standards and reproducibility

Reproducibility is the cornerstone of structural biology, ensuring that discoveries made through experimental cryo-electron microscopy (cryo-EM) and computational AI-based predictions can be independently verified, critically assessed, and reused for future research. Both domains have developed community-driven standards that focus on mandatory data deposition, rigorous validation, and transparent reporting.

6.1 Cryo-EM standards

Over the past decade, cryo-EM has matured into a method with strong archiving policies and widely adopted validation practices. To promote transparency, it is now mandatory that cryo-EM reconstructions be deposited in community repositories: density maps in the Electron Microscopy Data Bank (EMDB), atomic coordinate models in the Protein Data Bank (PDB), and, increasingly, raw micrographs and particle stacks in the Electron Microscopy Public Image Archive (EMPIAR) (Iudin et al., 2023; Lawson et al., 2024). Together, these archives provide the full spectrum of data from raw experimental images to refined atomic models enabling reproducibility and method development. The growth of EMPIAR, with over 1,000 datasets deposited by 2023, has been particularly important for benchmarking particle picking, developing new algorithms, and validating reconstructions in areas such as membrane protein biology (Kleywegt et al., 2024).

Validation remains a central issue. The Fourier Shell Correlation (FSC), particularly the gold-standard half-map FSC using the 0.143 criterion, is the most widely used metric for estimating resolution and identifying overfitting (Rosenthal and Henderson, 2003). However, FSC only provides a global average, so local resolution assessment is now standard practice, using tools such as ResMap and Blocres to capture variations across flexible or heterogeneous regions (Ku. Model–map fit is evaluated with global correlation coefficients, per-atom inclusion scores, and more targeted tools such as EMRinger, which assesses the agreement of side-chain rotamers with density (Barad et al., 2015). FSC-Q, a local correlation-based metric, has further refined map–model validation by addressing the limitations of global FSC in anisotropic or unevenly resolved maps (Afonine et al., 2018; Pintilie and Chiu, 2021).

Community initiatives have formalized these practices. The 2019 EMDataResource Model Challenge highlighted that no single validation metric is sufficient; instead, reproducibility requires a combination of complementary assessments across map quality, model geometry, and map–model agreement (Lawson et al., 2021). Building on this, the 2024 EM Ligand Modeling Challenge extended reproducibility standards to ligand-bound complexes, recommending not only the evaluation of ligand density fit but also systematic validation of geometry, stereochemistry, and binding-pocket environment (Lawson et al., 2024). These efforts illustrate how validation has moved beyond reporting resolution alone to providing a multi-dimensional quality assessment.

Finally, reporting checklists have been adopted to standardize practices across laboratories. The EM Validation Task Force (VTF) (Henderson et al., 2012) called for mandatory reporting of half-map FSC curves, map sharpening strategies, and overfitting tests. Later wwPDB/EMDB workshops codified these into structured checklists, including microscope parameters, data-processing pipelines, validation reports, and deposition metadata (Lawson et al., 2021). Such measures align with broader reproducibility frameworks in biomedical research, such as the EQUATOR Network, adapted for structural biology to ensure completeness and reduce bias.

6.2 AI-based model standards

AI-based predictions, particularly from AlphaFold2 (AF2), AlphaFold-Multimer, and more recently AlphaFold3, have expanded structural biology beyond the experimental Frontier. However, their reproducibility depends on input transparency. To replicate a model, researchers must release the exact multiple sequence alignments (MSAs), template sets, and random seeds used during inference (Jumper et al., 2021; Mirdita et al., 2022). Even small differences in alignment depth or stochastic seeds can alter the outcome, especially for disordered or multi-domain proteins. Without sharing these inputs, predictions become non-reproducible “black boxes.”

Confidence metrics are integral to reproducibility. pLDDT (predicted Local Distance Difference Test) scores provide per-residue confidence estimates, while Predicted Aligned Error (PAE) matrices quantify uncertainty in relative domain or chain positioning (Jumper et al., 2021). Publishing these metrics alongside structural coordinates allows other researchers to interpret which regions are reliable and which require caution. Empirical studies show that high pLDDT values (>90) generally correlate with accurate folds, but low-scoring regions often correspond to intrinsic disorder, requiring ensemble modeling or experimental validation (Ruff and Pappu, 2021).

Cross-validation with experimental methods is also essential. Techniques such as small-angle X-ray scattering (SAXS), hydrogen–deuterium exchange mass spectrometry (HDX-MS), and cross-linking MS (XL-MS) can confirm or refute AI-predicted folds and interactions. Benchmarking exercises like CASP15 demonstrated that while AlphaFold performs exceptionally for stable, globular proteins, reproducibility declines for multi-protein complexes, novel folds, and environments not represented in training data, underscoring the importance of complementary experimental validation (Kryshtafovych et al., 2023).

As with cryo-EM, community repositories are beginning to support AI models. Large-scale predictions are already available through the AlphaFold Protein Structure Database and the ESM Atlas, while individual researchers are encouraged to deposit AI-generated structures in ModelArchive or PDB-Dev with accompanying metadata. This ensures transparency, supports benchmarking, and prevents selective reporting of only “successful” predictions.

6.3 Integrative perspective

Standards in cryo-EM and AI are converging toward a shared framework: mandatory deposition of primary data, use of multiple complementary validation metrics, and transparent reporting of inputs and methods. While cryo-EM emphasizes archival and validation pipelines, AI-based predictions stress input transparency and confidence scoring. Together, these practices ensure that both experimental and computational structures can be trusted, compared, and built upon by the community, ultimately fostering more robust and integrative approaches to protein modeling.

7 Conclusion

The field of structural biology has undergone a revolution over the past several decades, driven by advances in imaging, computational modeling, and the integration of experimental and computational approaches. From the early days of X-ray crystallography to the modern era of cryo-electron microscopy (cryo-EM) and AI-driven structure prediction, these techniques have provided unprecedented insights into the structure and function of biological macromolecules. By revealing the atomic details of proteins, nucleic acids, and other macromolecular assemblies, structural biology has not only deepened our understanding of fundamental biological processes but has also facilitated breakthroughs in drug discovery, enzymology, nanotechnology, and the study of disease mechanisms.

The advent of cryo-EM, with its ability to visualize large macromolecular complexes and membrane proteins at near-atomic resolution, has democratized structural biology, making it accessible to researchers worldwide. Similarly, AI tools such as AlphaFold and RoseTTAFold have revolutionized the field by enabling the prediction of protein structures with remarkable accuracy and speed. Integrative and hybrid approaches, which combine data from multiple techniques, have expanded the scope of structural biology, providing new tools for studying complex biological systems and revealing the dynamic nature of macromolecular interactions.

These advancements have had a profound impact on drug discovery, enabling the development of targeted therapies with high efficacy and specificity. From anticancer drugs to antiviral therapies for COVID-19, structural biology has guided the development of life-saving treatments. In enzymology, structural insights have enabled the engineering of enzymes with desired properties, transforming industrial processes and driving innovation in biotechnology. In nanotechnology, structural studies have informed the design of nanodevices and biomaterials, opening new avenues for drug delivery, biosensing, and tissue engineering. Structural characterization has also provided critical insights into the molecular basis of disease, enabling the development of targeted therapies and diagnostics for conditions such as Alzheimer’s disease, Parkinson’s disease, and viral infections.

Despite these remarkable achievements, significant challenges remain. Membrane proteins and intrinsically disordered proteins (IDPs) continue to pose technical challenges, and new methodologies will be needed to stabilize and characterize these complex systems. Integrating structural, functional, and dynamical data will be essential for achieving a complete understanding of biological processes, and continued refinement of time-resolved and integrative techniques will be critical. The expansion of AI and machine learning tools holds great promise for improving the accuracy and efficiency of structure prediction and refinement, but further work is needed to extend these tools to more complex systems.

Looking to the future, the development of new technologies for in situ structural characterization offers exciting opportunities to study macromolecules in their native cellular environments. These advancements have the potential to provide a more accurate and comprehensive view of biological processes, driving further innovation in structural biology and its applications.

In conclusion, structural biology will continue to play a central role in advancing our understanding of life at the molecular level. By integrating new technologies, computational approaches, and multidisciplinary methodologies, researchers will be able to tackle increasingly complex biological questions, opening new frontiers in science and technology. From dissecting disease mechanisms to developing new drugs and bio-inspired materials, structural biology will remain at the heart of scientific progress, shaping the future of biology and medicine.

Author contributions

GJ: Conceptualization, Methodology, Writing – original draft. AR: Data curation, Methodology, Resources, Writing – review and editing. KS: Data curation, Formal Analysis, Methodology, Writing – review and editing. BA: Formal Analysis, Investigation, Resources, Writing – review and editing. AV: Methodology, Resources, Writing – review and editing. SC: Data curation, Methodology, Resources, Writing – review and editing. YT: Methodology, Writing – review and editing. WX: Investigation, Project administration, Supervision, Validation, Visualization, Writing – review and editing.

Funding

The author(s) declare that no financial support was received for the research and/or publication of this article.

Acknowledgments

The author KS gratefully acknowledges the support received from the Department of Biotechnology (DBT), Government of India, under the DBT Star College Scheme (HRD-11011/10/2024-HRD-DBT) for providing the necessary infrastructure and resources to carry out research in the laboratory of the Department of Biotechnology at Rathinam College of Arts and Science.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that Generative AI was used in the creation of this manuscript. This manuscript was checked for grammar and flow using DeepSeek and ChatGPT. These tools were used only for language refinement, with no impact on the scientific content. All work remains the authors’ own.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

Afonine, P. V., Poon, B. K., Read, R. J., Sobolev, O. V., Terwilliger, T. C., Urzhumtsev, A., et al. (2018). Real-space refinement in PHENIX for cryo-EM and crystallography. Acta Crystallogr. Sect. D. Struct. Biol. 74 (6), 531–544. doi:10.1107/S2059798318006551

PubMed Abstract | CrossRef Full Text | Google Scholar

Aitken, C. E., Marshall, R. A., and Puglisi, J. D. (2008). An oxygen scavenging system for improvement of dye stability in single-molecule fluorescence experiments. Biophysical J. 94 (5), 1826–1835. doi:10.1529/biophysj.107.117689

PubMed Abstract | CrossRef Full Text | Google Scholar

Akdel, M., Pires, D. E. V., Pardo, E. P., Jänes, J., Zalevsky, A. O., Mészáros, B., et al. (2022). A structural biology community assessment of AlphaFold2 applications. Nat. Struct. and Mol. Biol. 29 (11), 1056–1067. doi:10.1038/s41594-022-00849-w

PubMed Abstract | CrossRef Full Text | Google Scholar

Alatzoglou, C., Tzianni, E. I., Patila, M., Trachioti, M. G., Prodromidis, M. I., and Stamatis, H. (2023). Structure-function studies of glucose oxidase in the presence of carbon nanotubes and bio-graphene for the development of electrochemical glucose biosensors. Nanomaterials 14 (1), 85. doi:10.3390/nano14010085

PubMed Abstract | CrossRef Full Text | Google Scholar

Asor, R., and Kukura, P. (2022). Characterising biomolecular interactions and dynamics with mass photometry. Curr. Opin. Chem. Biol. 68, 102132. doi:10.1016/j.cbpa.2022.102132

PubMed Abstract | CrossRef Full Text | Google Scholar

Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., et al. (2021). Accurate prediction of protein structures and interactions using a three-track neural network. Science 373 (6557), 871–876. doi:10.1126/science.abj8754

PubMed Abstract | CrossRef Full Text | Google Scholar

Balyschew, N., Yushkevich, A., Mikirtumov, V., Sanchez, R. M., Sprink, T., and Kudryashev, M. (2023). Streamlined structure determination by cryo-electron tomography and subtomogram averaging using TomoBEAR. Nat. Commun. 14 (1), 6543. doi:10.1038/s41467-023-42085-w

PubMed Abstract | CrossRef Full Text | Google Scholar

Barad, B. A., Echols, N., Wang, R. Y.-R., Cheng, Y., DiMaio, F., Adams, P. D., et al. (2015). EMRinger: side chain–directed model and map validation for 3D cryo-electron microscopy. Nat. Methods 12 (10), 943–946. doi:10.1038/nmeth.3541

PubMed Abstract | CrossRef Full Text | Google Scholar

Bastida, A., Zúñiga, J., Miguel, B., and Soler, M. A. (2023). Description of conformational ensembles of disordered proteins by residue-local probabilities. Phys. Chem. Chem. Phys. 25 (15), 10512–10524. doi:10.1039/D2CP05970C

PubMed Abstract | CrossRef Full Text | Google Scholar

Beck, M., and Baumeister, W. (2016). Cryo-electron tomography: can it reveal the molecular sociology of cells in atomic detail? Trends Cell Biol. 26 (11), 825–837. doi:10.1016/j.tcb.2016.08.006

PubMed Abstract | CrossRef Full Text | Google Scholar

Bepler, T., Morin, A., Rapp, M., Brasch, J., Shapiro, L., Noble, A. J., et al. (2019). Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs. Nat. Methods 16 (11), 1153–1160. doi:10.1038/s41592-019-0575-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Berger, C., Premaraj, N., Ravelli, R. B., Knoops, K., López-Iglesias, C., and Peters, P. J. (2023). Cryo-electron tomography on focused ion beam lamellae transforms structural cell biology. Nat. Methods 20 (4), 499–511. doi:10.1038/s41592-023-01783-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Bhattacharjee, S., Feng, X., Maji, S., Dadhwal, P., Zhang, Z., Brown, Z. P., et al. (2023). Time resolution in cryo-EM using a novel PDMS-based microfluidic chip assembly and its application to the study of HflX-mediated ribosome recycling. bioRxiv., 2023.01.25.525430. doi:10.1101/2023.01.25.525430

PubMed Abstract | CrossRef Full Text | Google Scholar

Bhowmick, A., Hussein, R., Bogacz, I., Simon, P. S., Ibrahim, M., Chatterjee, R., et al. (2023). Structural evidence for intermediates during O2 formation in photosystem II. Nature 617 (7961), 629–636. doi:10.1038/s41586-023-06038-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Bigelyte, G., Young, J. K., Karvelis, T., Budre, K., Zedaveinyte, R., Djukanovic, V., et al. (2021). Miniature type V-F CRISPR-Cas nucleases enable targeted DNA modification in cells. Nat. Commun. 12 (1), 6191. doi:10.1038/s41467-021-26469-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Birch, J., Kwan, T. O. C., Judge, P. J., Axford, D., Aller, P., Butryn, A., et al. (2023). A versatile approach to high-density microcrystals in lipidic cubic phase for room-temperature serial crystallography. J. Appl. Crystallogr. 56 (5), 1361–1370. doi:10.1107/S1600576723006428

PubMed Abstract | CrossRef Full Text | Google Scholar

Bornscheuer, U. T., Huisman, G. W., Kazlauskas, R. J., Lutz, S., Moore, J. C., and Robins, K. (2012). Engineering the third wave of biocatalysis. Nature 485 (7397), 185–194. doi:10.1038/nature11117

PubMed Abstract | CrossRef Full Text | Google Scholar

Burré, J., Sharma, M., and Südhof, T. C. (2014). α-Synuclein assembles into higher-order multimers upon membrane binding to promote SNARE complex formation. Proc. Natl. Acad. Sci. 111 (40), E4274–E4283. doi:10.1073/pnas.1416598111

PubMed Abstract | CrossRef Full Text | Google Scholar

Caffrey, M. (2015). A comprehensive review of the lipid cubic phase or in meso method for crystallizing membrane and soluble proteins and complexes. Acta Crystallogr. Sect. F. Struct. Biol. Commun. 71 (1), 3–18. doi:10.1107/S2053230X14026843

PubMed Abstract | CrossRef Full Text | Google Scholar

Carroni, M., and Saibil, H. R. (2016). Cryo electron microscopy to determine the structure of macromolecular complexes. Methods 95, 78–85. doi:10.1016/j.ymeth.2015.11.023

PubMed Abstract | CrossRef Full Text | Google Scholar

Cavanagh, J., Fairbrother, W. J., Palmer, A. G., Rance, M., and Skelton, N. J. (2007). “Theoretical description of nmr spectroscopy,” in Protein NMR spectroscopy (Elsevier), 29–113. doi:10.1016/B978-012164491-8/50004-X

CrossRef Full Text | Google Scholar

Chang, C.-J., Lee, C.-C., Chan, Y.-T., Trudeau, D. L., Wu, M.-H., Tsai, C.-H., et al. (2016). Exploring the mechanism responsible for cellulase thermostability by structure-guided recombination. PLOS ONE 11 (3), e0147485. doi:10.1371/journal.pone.0147485

PubMed Abstract | CrossRef Full Text | Google Scholar

Chapman, H. N., Fromme, P., Barty, A., White, T. A., Kirian, R. A., Aquila, A., et al. (2011). Femtosecond X-ray protein nanocrystallography. Nature 470 (7332), 73–77. doi:10.1038/nature09750

PubMed Abstract | CrossRef Full Text | Google Scholar

Cheng, W., and Nian, B. (2023). Computer-aided lipase engineering for improving their stability and activity in the food industry: state of the art. Molecules 28 (15), 5848. doi:10.3390/molecules28155848

PubMed Abstract | CrossRef Full Text | Google Scholar

Cheng, Y., Grigorieff, N., Penczek, P. A., and Walz, T. (2015). A primer to single-particle cryo-electron microscopy. Cell 161 (3), 438–449. doi:10.1016/j.cell.2015.03.050

PubMed Abstract | CrossRef Full Text | Google Scholar

Cosgrove, D. J. (2005). Growth of the plant cell wall. Nat. Rev. Mol. Cell Biol. 6 (11), 850–861. doi:10.1038/nrm1746

PubMed Abstract | CrossRef Full Text | Google Scholar

Das, A., Bao, C., and Ermolenko, D. N. (2023). Comparing FRET pairs that report on intersubunit rotation in bacterial ribosomes. J. Mol. Biol. 435 (15), 168185. doi:10.1016/j.jmb.2023.168185

PubMed Abstract | CrossRef Full Text | Google Scholar

de Beer, M., Daviran, D., Roverts, R., Rutten, L., Macías-Sánchez, E., Metz, J. R., et al. (2023). Precise targeting for 3D cryo-correlative light and electron microscopy volume imaging of tissues using a FinderTOP. Commun. Biol. 6 (1), 510. doi:10.1038/s42003-023-04887-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Dhakal, A., Gyawali, R., Wang, L., and Cheng, J. (2023). A large expert-curated cryo-EM image dataset for machine learning protein particle picking. Sci. Data 10 (1), 392. doi:10.1038/s41597-023-02280-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Dhakal, A., Gyawali, R., Wang, L., and Cheng, J. (2024). CryoTransformer: a transformer model for picking protein particles from cryo-EM micrographs. Bioinformatics 40 (3), btae109. doi:10.1093/bioinformatics/btae109

PubMed Abstract | CrossRef Full Text | Google Scholar

Dill, K. A., and MacCallum, J. L. (2012). The protein-folding problem, 50 Years on. Science 338 (6110), 1042–1046. doi:10.1126/science.1219021

PubMed Abstract | CrossRef Full Text | Google Scholar

Doyle, D. A., Cabral, J. M., Pfuetzner, R. A., Kuo, A., Gulbis, J. M., Cohen, S. L., et al. (1998). MacKinnon R. The structure of the potassium channel: molecular basis of K+ conduction and selectivity. Science 280 (5360), 69–77. doi:10.1126/science.280.5360.69

PubMed Abstract | CrossRef Full Text | Google Scholar

Drenth, J. (2007). Principles of protein X-ray crystallography. New York: Springer. doi:10.1007/0-387-33746-6

CrossRef Full Text | Google Scholar

Evans, R., O’Neill, M., Pritzel, A., Antropova, N., Senior, A., Green, T., et al. (2022). Protein complex prediction with AlphaFold-Multimer. bioRxiv [Preprint]. doi:10.1101/2021.10.04.463034

CrossRef Full Text | Google Scholar

Fang, Z., Ran, H., Zhang, Y., Chen, C., Lin, P., Zhang, X., et al. (2025). AlphaFold 3: an unprecedent opportunity for fundamental research and drug development. Precis. Clin. Med. 8 (3), pbaf015. doi:10.1093/pcmedi/pbaf015

PubMed Abstract | CrossRef Full Text | Google Scholar

Farrell, D. P., Anishchenko, I., Shakeel, S., Lauko, A., Passmore, L. A., Baker, D., et al. (2020). Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM. IUCrJ 7 (5), 881–892. doi:10.1107/S2052252520009306

PubMed Abstract | CrossRef Full Text | Google Scholar

Fischer, N., Neumann, P., Konevega, A. L., Bock, L. V., Ficner, R., Rodnina, M. V., et al. (2015). Structure of the E. coli ribosome-EF-Tu complex at <3 Å resolution by Cs-corrected cryo-EM. Nature 520 (7548), 567–570. doi:10.1038/nature14275

PubMed Abstract | CrossRef Full Text | Google Scholar

Fitzpatrick, A. W. P., Falcon, B., He, S., Murzin, A. G., Murshudov, G., Garringer, H. J., et al. (2017). Cryo-EM structures of tau filaments from Alzheimer’s disease. Nature 547 (7662), 185–190. doi:10.1038/nature23002

PubMed Abstract | CrossRef Full Text | Google Scholar

Franek, M., Koptašíková, L., Mikšátko, J., Liebl, D., Macíčková, E., Pospíšil, J., et al. (2024). In-section Click-iT detection and super-resolution CLEM analysis of nucleolar ultrastructure and replication in plants. Nat. Commun. 15 (1), 2445. doi:10.1038/s41467-024-46324-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Frank, J. (2006). Three-dimensional electron microscopy of macromolecular assemblies. Oxford University Press. doi:10.1093/acprof:oso/9780195182187.001.0001

CrossRef Full Text | Google Scholar

Frantz, C., Stewart, K. M., and Weaver, V. M. (2010). The extracellular matrix at a glance. J. Cell Sci. 123 (24), 4195–4200. doi:10.1242/jcs.023820

PubMed Abstract | CrossRef Full Text | Google Scholar

Frey, T. G., and Mannella, C. A. (2000). The internal structure of mitochondria. Trends Biochem. Sci. 25 (7), 319–324. doi:10.1016/S0968-0004(00)01609-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Fung, H. K. H., Hayashi, Y., Salo, V. T., Babenko, A., Zagoriy, I., Brunner, A., et al. (2023). Genetically encoded multimeric tags for subcellular protein localization in cryo-EM. Nat. Methods 20 (12), 1900–1908. doi:10.1038/s41592-023-02053-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Giri, N., Wang, L., and Cheng, J. (2024). Cryo2StructData: a large labeled cryo-EM density map dataset for AI-based modeling of protein structures. Sci. Data 11 (1), 458. doi:10.1038/s41597-024-03299-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Gordon, D. E., Jang, G. M., Bouhaddou, M., Xu, J., Obernier, K., White, K. M., et al. (2020). A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature 583 (7816), 459–468. doi:10.1038/s41586-020-2286-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Gremer, L., Schölzel, D., Schenk, C., Reinartz, E., Labahn, J., Ravelli, R. B. G., et al. (2017). Fibril structure of amyloid-β(1–42) by cryo–electron microscopy. Science 358 (6359), 116–119. doi:10.1126/science.aao2825

PubMed Abstract | CrossRef Full Text | Google Scholar

Hartmann, A., Sreenivasa, K., Schenkel, M., Chamachi, N., Schake, P., Krainer, G., et al. (2023). An automated single-molecule FRET platform for high-content, multiwell plate screening of biomolecular conformations and dynamics. Nat. Commun. 14 (1), 6511. doi:10.1038/s41467-023-42232-3

PubMed Abstract | CrossRef Full Text | Google Scholar

He, J., Li, T., and Huang, S.-Y. (2023). Improvement of cryo-EM maps by simultaneous local and non-local deep learning. Nat. Commun. 14 (1), 3217. doi:10.1038/s41467-023-39031-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Henderson, R., Sali, A., Baker, M. L., Carragher, B., Devkota, B., Downing, K. H., et al. (2012). Outcome of the first electron microscopy validation Task Force meeting. Structure 20 (2), 205–214. doi:10.1016/j.str.2011.12.014

PubMed Abstract | CrossRef Full Text | Google Scholar

Hilander, T., Awadhpersad, R., Monteuuis, G., Broda, K. L., Pohjanpelto, M., Pyman, E., et al. (2024). Supernumerary proteins of the human mitochondrial ribosomal small subunit are integral for assembly and translation. IScience 27 (7), 110185. doi:10.1016/j.isci.2024.110185

PubMed Abstract | CrossRef Full Text | Google Scholar

Hoffman, D. P., Shtengel, G., Xu, C. S., Campbell, K. R., Freeman, M., Wang, L., et al. (2020). Correlative three-dimensional super-resolution and block-face electron microscopy of whole vitreously frozen cells. Science 367 (6475), eaaz5357. doi:10.1126/science.aaz5357

PubMed Abstract | CrossRef Full Text | Google Scholar

Hynes, R. O., and Naba, A. (2012). Overview of the matrisome--an inventory of extracellular matrix constituents and functions. Cold Spring Harb. Perspect. Biol. 4 (1), a004903. doi:10.1101/cshperspect.a004903

PubMed Abstract | CrossRef Full Text | Google Scholar

Iudin, A., Korir, P. K., Somasundharam, S., Weyand, S., Cattavitello, C., Fonseca, N., et al. (2023). EMPIAR: the electron microscopy public image archive. Nucleic Acids Res. 51 (D1), D1503–D1511. doi:10.1093/nar/gkac1062

PubMed Abstract | CrossRef Full Text | Google Scholar

Jamali, K., Käll, L., Zhang, R., Brown, A., Kimanius, D., and Scheres, S. H. W. (2024). Automated model building and protein identification in cryo-EM maps. Nature 628 (8007), 450–457. doi:10.1038/s41586-024-07215-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Jensen, M. R., Ruigrok, R. W., and Blackledge, M. (2013). Describing intrinsically disordered proteins at atomic resolution by NMR. Curr. Opin. Struct. Biol. 23 (3), 426–435. doi:10.1016/j.sbi.2013.02.007

PubMed Abstract | CrossRef Full Text | Google Scholar

Jiang, F., Zhou, K., Ma, L., Gressel, S., and Doudna, J. A. (2015). STRUCTURAL BIOLOGY. A Cas9-guide RNA complex preorganized for target DNA recognition. Science 348 (6242), 1477–1481. doi:10.1126/science.aab1452

PubMed Abstract | CrossRef Full Text | Google Scholar

Jiang, Z., Shen, T., Huynh, H., Fang, X., Han, Z., and Ouyang, K. (2022). Cardiolipin regulates mitochondrial ultrastructure and function in mammalian cells. Genes 13 (10), 1889. doi:10.3390/genes13101889

PubMed Abstract | CrossRef Full Text | Google Scholar

Joerger, A. C., and Fersht, A. R. (2008). Structural biology of the tumor suppressor p53. Annu. Rev. Biochem. 77 (1), 557–582. doi:10.1146/annurev.biochem.77.060806.091238

PubMed Abstract | CrossRef Full Text | Google Scholar

Joosten, B., Willemse, M., Fransen, J., Cambi, A., and van den Dries, K. (2018). Super-resolution correlative light and electron microscopy (SR-CLEM) reveals novel ultrastructural insights into dendritic cell podosomes. Front. Immunol. 9, 1908. doi:10.3389/fimmu.2018.01908

PubMed Abstract | CrossRef Full Text | Google Scholar

Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature 596 (7873), 583–589. doi:10.1038/s41586-021-03819-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Jung, M., and Mun, J. Y. (2019). Mitochondria and endoplasmic reticulum imaging by correlative light and volume electron microscopy. J. Vis. Exp. 149. doi:10.3791/59750

PubMed Abstract | CrossRef Full Text | Google Scholar

Jung, M., and Mun, J. Y. (2024). Dual-color correlative light and electron microscopy for the visualization of interactions between mitochondria and lysosomes. J. Vis. Exp. 211. doi:10.3791/67020

PubMed Abstract | CrossRef Full Text | Google Scholar

Kaila, V. R. I., Verkhovsky, M. I., and Wikström, M. (2010). Proton-coupled electron transfer in cytochrome oxidase. Chem. Rev. 110 (12), 7062–7081. doi:10.1021/cr1002003

PubMed Abstract | CrossRef Full Text | Google Scholar

Kendrew, J. C., Bodo, G., Dintzis, H. M., Parrish, R. G., Wyckoff, H., and Phillips, D. C. (1958). A three-dimensional model of the myoglobin molecule obtained by X-ray analysis. Nature 181 (4610), 662–666. doi:10.1038/181662a0

PubMed Abstract | CrossRef Full Text | Google Scholar

Kern, J., Alonso-Mori, R., Tran, R., Hattne, J., Gildea, R. J., Echols, N., et al. (2013). Simultaneous femtosecond X-ray spectroscopy and diffraction of photosystem II at room temperature. Science 340 (6131), 491–495. doi:10.1126/science.1234273

PubMed Abstract | CrossRef Full Text | Google Scholar

Kim, S. J., Fernandez-Martinez, J., Nudelman, I., Shi, Y., Zhang, W., Raveh, B., et al. (2018). Integrative structure and functional anatomy of a nuclear pore complex. Nature 555 (7697), 475–482. doi:10.1038/nature26003

PubMed Abstract | CrossRef Full Text | Google Scholar

Kleywegt, G. J., Adams, P. D., Butcher, S. J., Lawson, C. L., Rohou, A., Rosenthal, P. B., et al. (2024). Community recommendations on cryoEM data archiving and validation. IUCrJ 11 (2), 140–151. doi:10.1107/S2052252524001246

PubMed Abstract | CrossRef Full Text | Google Scholar

Kojima, M., Abe, S., Furuta, T., Hirata, K., Yao, X., Kobayashi, A., et al. (2024). High-throughput structure determination of an intrinsically disordered protein using cell-free protein crystallization. Proc. Natl. Acad. Sci. 121 (25), e2322452121. doi:10.1073/pnas.2322452121

PubMed Abstract | CrossRef Full Text | Google Scholar

Koripella, R. K., Sharma, M. R., Bhargava, K., Datta, P. P., Kaushal, P. S., Keshavan, P., et al. (2020). Structures of the human mitochondrial ribosome bound to EF-G1 reveal distinct features of mitochondrial translation elongation. Nat. Commun. 11 (1), 3830. doi:10.1038/s41467-020-17715-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Kornberg Arthur, B. T. A. (1992). DNA replication. 2nd ed. W. H. Freeman.

Google Scholar

Krawinski, P., Smithers, L., van Dalsen, L., Boland, C., Ostrovitsa, N., Pérez, J., et al. (2024). 7.10 MAG. A novel host monoacylglyceride for in meso (lipid cubic phase) crystallization of membrane proteins. Cryst. Growth and Des. 24 (7), 2985–3001. doi:10.1021/acs.cgd.4c00087

PubMed Abstract | CrossRef Full Text | Google Scholar

Kroth, H., Oden, F., Molette, J., Schieferstein, H., Capotosti, F., Mueller, A., et al. (2019). Discovery and preclinical characterization of [18F]PI-2620, a next-generation tau PET tracer for the assessment of tau pathology in Alzheimer’s disease and other tauopathies. Eur. J. Nucl. Med. Mol. Imaging 46 (10), 2178–2189. doi:10.1007/s00259-019-04397-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Krois, A. S., Dyson, H. J., and Wright, P. E. (2018). Long-range regulation of p53 DNA binding by its intrinsically disordered N-terminal transactivation domain. Proc. Natl. Acad. Sci. 115 (48), E11302–E11310. doi:10.1073/pnas.1814051115

PubMed Abstract | CrossRef Full Text | Google Scholar

Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K., and Moult, J. (2023). Critical assessment of methods of protein structure prediction (CASP)-Round XV. Proteins 91 (12), 1539–1549. doi:10.1002/prot.26617

PubMed Abstract | CrossRef Full Text | Google Scholar

Kühlbrandt, W. (2014). Biochemistry. The resolution revolution. Science 343 (6178), 1443–1444. doi:10.1126/science.1251652

PubMed Abstract | CrossRef Full Text | Google Scholar

Lawson, C. L., Kryshtafovych, A., Adams, P. D., Afonine, P. V., Baker, M. L., Barad, B. A., et al. (2021). Cryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge. Nat. Methods 18 (2), 156–164. doi:10.1038/s41592-020-01051-w

PubMed Abstract | CrossRef Full Text | Google Scholar

Lawson, C. L., Kryshtafovych, A., Pintilie, G. D., Burley, S. K., Černý, J., Chen, V. B., et al. (2024). Outcomes of the EMDataResource cryo-EM ligand modeling challenge. Nat. Methods 21 (7), 1340–1348. doi:10.1038/s41592-024-02321-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Lee, J. E., Fusco, M. L., Hessell, A. J., Oswald, W. B., Burton, D. R., and Saphire, E. O. (2008). Structure of the Ebola virus glycoprotein bound to an antibody from a human survivor. Nature 454 (7201), 177–182. doi:10.1038/nature07082

PubMed Abstract | CrossRef Full Text | Google Scholar

Lee, N. K., Cho, S., and Kim, I.-S. (2022). Ferritin – a multifaceted protein scaffold for biotherapeutics. Exp. and Mol. Med. 54 (10), 1652–1657. doi:10.1038/s12276-022-00859-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Lenz, S., Sinn, L. R., O’Reilly, F. J., Fischer, L., Wegner, F., and Rappsilber, J. (2021). Reliable identification of protein-protein interactions by crosslinking mass spectrometry. Nat. Commun. 12 (1), 3564. doi:10.1038/s41467-021-23666-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Levy, A., Wetzstein, G., Martel, J., Poitevin, F., and Zhong, E. D. (2022). Amortized inference for heterogeneous reconstruction in cryo-EM. arXiv. doi:10.48550/arXiv.2210.07387

CrossRef Full Text | Google Scholar

Li, X., Mooney, P., Zheng, S., Booth, C. R., Braunfeld, M. B., Gubbens, S., et al. (2013). Electron counting and beam-induced motion correction enable near-atomic-resolution single-particle cryo-EM. Nat. Methods 10 (6), 584–590. doi:10.1038/nmeth.2472

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, H., Nakajima, Y., Nomura, T., Sugahara, M., Yonekura, S., Chan, S. K., et al. (2021). Capturing structural changes of the S ₁ to S ₂ transition of photosystem II using time-resolved serial femtosecond crystallography. IUCrJ 8 (3), 431–443. doi:10.1107/S2052252521002177

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, Z., Pang, J., Gao, R., Wang, Q., Zhang, M., and Yu, X. (2023). Cryo-electron microscopy structures of capsids and in situ portals of DNA-devoid capsids of human cytomegalovirus. Nat. Commun. 14 (1), 2025. doi:10.1038/s41467-023-37779-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, H., Nakajima, Y., Nango, E., Owada, S., Yamada, D., Hashimoto, K., et al. (2024). Oxygen-evolving photosystem II structures during S1–S2–S3 transitions. Nature 626 (7999), 670–677. doi:10.1038/s41586-023-06987-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Liao, M., Cao, E., Julius, D., and Cheng, Y. (2013). Structure of the TRPV1 ion channel determined by electron cryo-microscopy. Nature 504 (7478), 107–112. doi:10.1038/nature12822

PubMed Abstract | CrossRef Full Text | Google Scholar

Lin, Y., Chen, X., Wang, K., Liang, L., and Zhang, H. (2024). An overview of nanoparticle-based delivery platforms for mRNA vaccines for treating cancer. Vaccines 12 (7), 727. doi:10.3390/vaccines12070727

PubMed Abstract | CrossRef Full Text | Google Scholar

Long, G. V., Fung, C., Menzies, A. M., Pupo, G. M., Carlino, M. S., Hyman, J., et al. (2014). Increased MAPK reactivation in early resistance to dabrafenib/trametinib combination therapy of BRAF-mutant metastatic melanoma. Nat. Commun. 5 (1), 5694. doi:10.1038/ncomms6694

PubMed Abstract | CrossRef Full Text | Google Scholar

Lotthammer, J. M., Ginell, G. M., Griffith, D., Emenecker, R. J., and Holehouse, A. S. (2024). Direct prediction of intrinsically disordered protein conformational properties from sequence. Nat. Methods 21 (3), 465–476. doi:10.1038/s41592-023-02159-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Madhavi Sastry, G., Adzhigirey, M., Day, T., Annabhimoju, R., and Sherman, W. (2013). Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J. Computer-Aided Mol. Des. 27 (3), 221–234. doi:10.1007/s10822-013-9644-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Maeshima, K., Imai, R., Tamura, S., and Nozaki, T. (2014). Chromatin as dynamic 10-nm fibers. Chromosoma 123 (3), 225–237. doi:10.1007/s00412-014-0460-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Mahamid, J., Pfeffer, S., Schaffer, M., Villa, E., Danev, R., Kuhn Cuellar, L., et al. (2016). Visualizing the molecular sociology at the HeLa cell nuclear periphery. Science 351 (6276), 969–972. doi:10.1126/science.aad8857

PubMed Abstract | CrossRef Full Text | Google Scholar

Mao, Y. (2025). Dynamics-based drug discovery by time-resolved cryo-EM. Curr. Opin. Struct. Biol. 91, 103001. doi:10.1016/j.sbi.2025.103001

PubMed Abstract | CrossRef Full Text | Google Scholar

Marshall, A. G., Krystofiak, E., Damo, S. M., and Hinton, A. (2023). Correlative light-electron microscopy: integrating dynamics to structure. Trends Biochem. Sci. 48 (9), 826–827. doi:10.1016/j.tibs.2023.05.003

PubMed Abstract | CrossRef Full Text | Google Scholar

McCarthy, A. E., Yoshioka, C., and Mansoor, S. E. (2019). Full-Length P2X7 structures reveal how palmitoylation prevents channel desensitization. Cell 179 (3), 659–670.e13. doi:10.1016/j.cell.2019.09.017

PubMed Abstract | CrossRef Full Text | Google Scholar

Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., and Steinegger, M. (2022). ColabFold: making protein folding accessible to all. Nat. Methods 19 (6), 679–682. doi:10.1038/s41592-022-01488-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Mohanty, A., Parida, A., Raut, R. K., and Behera, R. K. (2022). Ferritin: a promising nanoreactor and nanocarrier for bionanotechnology. ACS Bio and Med Chem Au 2 (3), 258–281. doi:10.1021/acsbiomedchemau.2c00003

PubMed Abstract | CrossRef Full Text | Google Scholar

Mosalaganti, S., Obarska-Kosinska, A., Siggel, M., Taniguchi, R., Turoňová, B., Zimmerli, C. E., et al. (2022). AI-based structure prediction empowers integrative structural analysis of human nuclear pores. Science 376 (6598), eabm9506. doi:10.1126/science.abm9506

PubMed Abstract | CrossRef Full Text | Google Scholar

Nakane, T., Kotecha, A., Sente, A., McMullan, G., Masiulis, S., Brown, P. M. G. E., et al. (2020). Single-particle cryo-EM at atomic resolution. Nature 587 (7832), 152–156. doi:10.1038/s41586-020-2829-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Neutze, R., Wouts, R., van der Spoel, D., Weckert, E., and Hajdu, J. (2000). Potential for biomolecular imaging with femtosecond X-ray pulses. Nature 406 (6797), 752–757. doi:10.1038/35021099

PubMed Abstract | CrossRef Full Text | Google Scholar

Nguyen, T., Hong, S. H., and Arora, P. S. (2025). Proteomimetic strategy for the modulation of intrinsically disordered protein MYC. J. Am. Chem. Soc. 147 (16), 13296–13302. doi:10.1021/jacs.4c18144

PubMed Abstract | CrossRef Full Text | Google Scholar

Nogales, E. (2016). The development of cryo-EM into a mainstream structural biology technique. Nat. Methods 13 (1), 24–27. doi:10.1038/nmeth.3694

PubMed Abstract | CrossRef Full Text | Google Scholar

Orand, T., and Jensen, M. R. (2025). Binding mechanisms of intrinsically disordered proteins: insights from experimental studies and structural predictions. Curr. Opin. Struct. Biol. 90, 102958. doi:10.1016/j.sbi.2024.102958

PubMed Abstract | CrossRef Full Text | Google Scholar

Orlov, I., Roche, S., Brasilès, S., Lukoyanova, N., Vaney, M.-C., Tavares, P., et al. (2022). CryoEM structure and assembly mechanism of a bacterial virus genome gatekeeper. Nat. Commun. 13 (1), 7283. doi:10.1038/s41467-022-34999-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Ostrem, J. M., Peters, U., Sos, M. L., Wells, J. A., and Shokat, K. M. (2013). K-Ras(G12C) inhibitors allosterically control GTP affinity and effector interactions. Nature 503 (7477), 548–551. doi:10.1038/nature12796

PubMed Abstract | CrossRef Full Text | Google Scholar

Owen, D. R., Allerton, C. M. N., Anderson, A. S., Aschenbrenner, L., Avery, M., Berritt, S., et al. (2021). An oral SARS-CoV-2 M ^pro inhibitor clinical candidate for the treatment of COVID-19. Science 374 (6575), 1586–1593. doi:10.1126/science.abl4784

PubMed Abstract | CrossRef Full Text | Google Scholar

Papasergi-Scott, M. M., Pérez-Hernández, G., Batebi, H., Gao, Y., Eskici, G., Seven, A. B., et al. (2024). Time-resolved cryo-EM of G-protein activation by a GPCR. Nature 629 (8014), 1182–1191. doi:10.1038/s41586-024-07153-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Perutz, M. F., Rossmann, M. G., Cullis, A. F., Muirhead, H., Will, G., and North, A. C. T. (1960). Structure of haemoglobin: a three-dimensional Fourier synthesis at 5.5-A. resolution, obtained by X-ray analysis. Nature 185 (4711), 416–422. doi:10.1038/185416a0

PubMed Abstract | CrossRef Full Text | Google Scholar

Pintilie, G., and Chiu, W. (2021). Validation, analysis and annotation of cryo-EM structures. Acta Crystallogr. Sect. D. Struct. Biol. 77 (9), 1142–1152. doi:10.1107/S2059798321006069

PubMed Abstract | CrossRef Full Text | Google Scholar

Prusiner, S. B. (1998). Prions. Proc. Natl. Acad. Sci. 95 (23), 13363–13383. doi:10.1073/pnas.95.23.13363

PubMed Abstract | CrossRef Full Text | Google Scholar

Punjani, A., Rubinstein, J. L., Fleet, D. J., and Brubaker, M. A. (2017). cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14 (3), 290–296. doi:10.1038/nmeth.4169

PubMed Abstract | CrossRef Full Text | Google Scholar

Rasmussen, S. G. F., DeVree, B. T., Zou, Y., Kruse, A. C., Chung, K. Y., Kobilka, T. S., et al. (2011). Crystal structure of the β2 adrenergic receptor–Gs protein complex. Nature 477 (7366), 549–555. doi:10.1038/nature10361

PubMed Abstract | CrossRef Full Text | Google Scholar

Renaud, J.-P., Chari, A., Ciferri, C., Liu, W., Rémigy, H.-W., Stark, H., et al. (2018). Cryo-EM in drug discovery: achievements, limitations and prospects. Nat. Rev. Drug Discov. 17 (7), 471–492. doi:10.1038/nrd.2018.77

PubMed Abstract | CrossRef Full Text | Google Scholar

Riek, R., Hornemann, S., Wider, G., Billeter, M., Glockshuber, R., Wüthrich, K., et al. (1996). NMR structure of the mouse prion protein domain PrP (121–231). Nature 382 (6587), 180–182. doi:10.1038/382180a0

PubMed Abstract | CrossRef Full Text | Google Scholar

Robin, X., Haas, J., Gumienny, R., Smolinski, A., Tauriello, G., and Schwede, T. (2021). Continuous Automated Model EvaluatiOn (CAMEO) Perspectives on the future of fully automated evaluation of structure prediction methods. Proteins Struct. Funct. Bioinforma. 89 (12), 1977–1986. doi:10.1002/prot.26213

PubMed Abstract | CrossRef Full Text | Google Scholar

Rosenthal, P. B., and Henderson, R. (2003). Optimal determination of particle orientation, absolute Hand, and contrast Loss in single-particle electron cryomicroscopy. J. Mol. Biol. 333 (4), 721–745. doi:10.1016/j.jmb.2003.07.013

PubMed Abstract | CrossRef Full Text | Google Scholar

Rosenthal, P. B., and Rubinstein, J. L. (2015). Validating maps from single particle electron cryomicroscopy. Curr. Opin. Struct. Biol. 34, 135–144. doi:10.1016/j.sbi.2015.07.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Ruff, K. M., and Pappu, R. V. (2021). AlphaFold and implications for intrinsically disordered proteins. J. Mol. Biol. 433 (20), 167208. doi:10.1016/j.jmb.2021.167208

PubMed Abstract | CrossRef Full Text | Google Scholar

Šali, A., and Blundell, T. L. (1993). Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234 (3), 779–815. doi:10.1006/jmbi.1993.1626

PubMed Abstract | CrossRef Full Text | Google Scholar

Schanda, P., and Brutscher, B. (2005). Very fast two-dimensional NMR spectroscopy for real-time investigation of dynamic events in proteins on the time scale of seconds. J. Am. Chem. Soc. 127 (22), 8014–8015. doi:10.1021/ja051306e

PubMed Abstract | CrossRef Full Text | Google Scholar

Schotte, F., Lim, M., Jackson, T. A., Smirnov, A. V., Soman, J., Olson, J. S., et al. (2003). Watching a protein as it functions with 150-ps time-resolved X-ray crystallography. Science 300 (5627), 1944–1947. doi:10.1126/science.1078797

PubMed Abstract | CrossRef Full Text | Google Scholar

Schuler, B., Soranno, A., Hofmann, H., and Nettels, D. (2016). Single-molecule FRET spectroscopy and the polymer Physics of unfolded and intrinsically disordered proteins. Annu. Rev. Biophysics 45 (1), 207–231. doi:10.1146/annurev-biophys-062215-010915

PubMed Abstract | CrossRef Full Text | Google Scholar

Shaw, V., Sungsuwan, S., McFallBoegeman, H., Huang, X., and Jin, X. (2022). Alternative assembly of Qβ virus like particles. doi:10.1101/2022.01.23.477406

CrossRef Full Text | Google Scholar

Singh, V., Itoh, Y., Del’Olio, S., Hassan, A., Naschberger, A., Flygaard, R. K., et al. (2024). Mitoribosome structure with cofactors and modifications reveals mechanism of ligand binding and interactions with L1 stalk. Nat. Commun. 15 (1), 4272. doi:10.1038/s41467-024-48163-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Sinz, A. (2018). Cross-linking/mass spectrometry for studying protein structures and protein–protein interactions: where are we now and where should we go from here? Angew. Chem. Int. Ed. 57 (22), 6390–6396. doi:10.1002/anie.201709559

PubMed Abstract | CrossRef Full Text | Google Scholar

Smith, K. M. L., Panepucci, E., Kaminski, J. W., Aumonier, S., Huang, C.-Y., Eris, D., et al. (2023). SDU – software for high-throughput automated data collection at the Swiss Light Source. J. Synchrotron Radiat. 30 (3), 538–545. doi:10.1107/S1600577523002631

PubMed Abstract | CrossRef Full Text | Google Scholar

Sobhy, M. A., Zhao, L., Anjum, D., Behzad, A., Takahashi, M., Tehseen, M., et al. (2022). Cryo-electron structures of the extreme thermostable enzymes sulfur oxygenase reductase and lumazine synthase. PLOS ONE 17 (10), e0275487. doi:10.1371/journal.pone.0275487

PubMed Abstract | CrossRef Full Text | Google Scholar

Świderek, K., Velasco-Lozano, S., Galmés, M. À., Olazabal, I., Sardon, H., López-Gallego, F., et al. (2023). Mechanistic studies of a lipase unveil effect of pH on hydrolysis products of small PET modules. Nat. Commun. 14 (1), 3556. doi:10.1038/s41467-023-39201-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Taylor, L. E., Knott, B. C., Baker, J. O., Alahuhta, P. M., Hobdey, S. E., Linger, J. G., et al. (2018). Engineering enhanced cellobiohydrolase activity. Nat. Commun. 9 (1), 1186. doi:10.1038/s41467-018-03501-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Teixeira, J. M. C., Liu, Z. H., Namini, A., Li, J., Vernon, R. M., Krzeminski, M., et al. (2022). IDPConformerGenerator: a flexible software suite for sampling the conformational space of disordered protein states. J. Phys. Chem. A 126 (35), 5985–6003. doi:10.1021/acs.jpca.2c03726

PubMed Abstract | CrossRef Full Text | Google Scholar

Tenboer, J., Basu, S., Zatsepin, N., Pande, K., Milathianaki, D., Frank, M., et al. (2014). Time-resolved serial crystallography captures high-resolution intermediates of photoactive yellow protein. Science 346 (6214), 1242–1246. doi:10.1126/science.1259357

PubMed Abstract | CrossRef Full Text | Google Scholar

Terashi, G., Wang, X., Prasad, D., Nakamura, T., and Kihara, D. (2024). DeepMainmast: integrated protocol of protein structure modeling for cryo-EM with deep learning and structure prediction. Nat. Methods 21 (1), 122–131. doi:10.1038/s41592-023-02099-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Thangaratnarajah, C., Rheinberger, J., and Paulino, C. (2022). Cryo-EM studies of membrane proteins at 200 keV. Curr. Opin. Struct. Biol. 76, 102440. doi:10.1016/j.sbi.2022.102440

PubMed Abstract | CrossRef Full Text | Google Scholar

Tjørnelund, H. D., Vind, J., Brask, J., Woodley, J. M., and Peters, G. H. J. (2023). Candida Antarctica lipase B performance in organic solvent at varying water activities studied by molecular dynamics simulations. Comput. Struct. Biotechnol. J. 21, 5451–5462. doi:10.1016/j.csbj.2023.10.049

PubMed Abstract | CrossRef Full Text | Google Scholar

Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., et al. (2021). Highly accurate protein structure prediction for the human proteome. Nature 596 (7873), 590–596. doi:10.1038/s41586-021-03828-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Tuttle, M. D., Comellas, G., Nieuwkoop, A. J., Covell, D. J., Berthold, D. A., Kloepper, K. D., et al. (2016). Solid-state NMR structure of a pathogenic fibril of full-length human α-synuclein. Nat. Struct. and Mol. Biol. 23 (5), 409–415. doi:10.1038/nsmb.3194

PubMed Abstract | CrossRef Full Text | Google Scholar

Tycko, R. (2011). Solid-state NMR studies of amyloid fibril structure. Annu. Rev. Phys. Chem. 62 (1), 279–299. doi:10.1146/annurev-physchem-032210-103539

PubMed Abstract | CrossRef Full Text | Google Scholar

Urban, P., and Pompon, D. (2022). Confrontation of AlphaFold models with experimental structures enlightens conformational dynamics supporting CYP102A1 functions. Sci. Rep. 12 (1), 15982. doi:10.1038/s41598-022-20390-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Uversky, V. N. (2019). Intrinsically disordered proteins and their “mysterious” (Meta)Physics. Front. Phys. 7, 10. doi:10.3389/fphy.2019.00010

CrossRef Full Text | Google Scholar

Varadi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., et al. (2022). AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 50 (D1), D439–D444. doi:10.1093/nar/gkab1061

PubMed Abstract | CrossRef Full Text | Google Scholar

Walters, W. P., and Murcko, M. (2020). Assessing the impact of generative AI on medicinal chemistry. Nat. Biotechnol. 38 (2), 143–145. doi:10.1038/s41587-020-0418-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, F., Gong, H., Liu, G., Li, M., Yan, C., Xia, T., et al. (2016). DeepPicker: a deep learning approach for fully automated particle picking in cryo-EM. J. Struct. Biol. 195 (3), 325–336. doi:10.1016/j.jsb.2016.07.006

PubMed Abstract | CrossRef Full Text | Google Scholar

Wlodawer, A., and Dauter, Z. (2017). `Atomic resolution’: a badly abused term in structural biology. Acta Crystallogr. Sect. D. Struct. Biol. 73 (4), 379–380. doi:10.1107/S205979831700225X

PubMed Abstract | CrossRef Full Text | Google Scholar

Wrapp, D., Wang, N., Corbett, K. S., Goldsmith, J. A., Hsieh, C.-L., Abiona, O., et al. (2020). Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation. Science 367 (6483), 1260–1263. doi:10.1126/science.abb2507

PubMed Abstract | CrossRef Full Text | Google Scholar

Wüthrich, K. (2001). The way to NMR structures of proteins. Nat. Struct. Biol. 8 (11), 923–925. doi:10.1038/nsb1101-923

PubMed Abstract | CrossRef Full Text | Google Scholar

Xu, Y., Wu, Y., Zhang, Y., Fan, R., Yang, Y., Li, D., et al. (2023). Cryo-EM structures of human monkeypox viral replication complexes with and without DNA duplex. Cell Res. 33 (6), 479–482. doi:10.1038/s41422-023-00796-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Yip, K. M., Fischer, N., Paknia, E., Chari, A., and Stark, H. (2020). Atomic-resolution protein structure determination by cryo-EM. Nature 587 (7832), 157–161. doi:10.1038/s41586-020-2833-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Yusupov, M. M., Yusupova, G.Zh., Baucom, A., Lieberman, K., Earnest, T. N., Cate, J. H. D., et al. (2001). Crystal structure of the ribosome at 5.5 A resolution. Science 292 (5518), 883–896. doi:10.1126/science.1060089

PubMed Abstract | CrossRef Full Text | Google Scholar

Zeltins, A. (2013). Construction and characterization of virus-like particles: a review. Mol. Biotechnol. 53 (1), 92–107. doi:10.1007/s12033-012-9598-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, L., Lin, D., Sun, X., Curth, U., Drosten, C., Sauerhering, L., et al. (2020). Crystal structure of SARS-CoV-2 main protease provides a basis for design of improved α-ketoamide inhibitors. Science 368 (6489), 409–412. doi:10.1126/science.abb3405

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhong, E. D., Bepler, T., Berger, B., and Davis, J. H. (2021). CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks. Nat. Methods 18 (2), 176–185. doi:10.1038/s41592-020-01049-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhou, Z. H. (2011). Atomic resolution cryo electron microscopy of macromolecular complexes. Adv. Protein Chem. Struct. Biol. 82, 1–35. doi:10.1016/B978-0-12-386507-6.00001-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhou, Q., and Lok, S.-M. (2024). Visualizing the virus world inside the cell by cryo-electron tomography. J. Virology 98 (12), e0108523. doi:10.1128/jvi.01085-23

PubMed Abstract | CrossRef Full Text | Google Scholar