Abstract
Proteins carry out their biological activity as dynamic structures and populate in solution or in biological membranes structural distributions with different degrees of heterogeneity. The central challenge in structural biology is to capture protein structural dynamics under equilibrium or kinetic conditions shifting from single, static pictures to movies of conformational ensembles. Ideally, this task should be pursued both in vitro and in vivo, under the influence of the native environment. The last decade has seen a tremendous development of biophysical methods for the investigation of protein structure and dynamics. However, each method has specific limitations and no single approach offers such a complex level of description. Nonetheless, the combination of experimental and computational, complementary methods is opening promising new avenues. Also the ambition of implementing structural studies on an “omic” scale is becoming more and more realistic. In spite of still major limitations, integrative structural biology is bringing dynamics into structural proteomics, with exciting perspectives for basic and applied sciences.
Introduction
Proteins are the main actors determining phenotype in the biological world. Their structural characterization is essential to understand, design and modify protein function, impacting on basic and applied research. Protein structures in solution or membranes are not static and interconvert among different conformations with different degrees of flexibility on a continuum from highly constrained to completely disordered states. A central challenge of structural biology is the dynamic characterization of conformational ensembles, rather than the determination of single structures or static ensembles that may represent only snapshots of biologically relevant transitions. Protein dynamics is relevant to virtually any kind of protein activity and manifests itself at multiple levels: conformational dynamics, conformational changes, protein folding and intermolecular interactions.
Molecular recognition is a general phenomenon that highlights the importance of conformational dynamics in shaping protein affinity and specificity (; ). Consistently, the performance of drug-design methods is strongly affected by the extent of protein flexibility that is taken into account (). Enzymatic catalysis is intimately linked to protein structure dynamics (; Lycus et al., 2023) and catalytic properties can be illuminated by modeling transition-state ensembles (). Conformation-based models for allostery are being displaced by a view invoking vibrations and fluctuations from the entire protein structure and associated entropy changes mediating allosteric communication (Madan et al., 2023). Chain flexibility has been adjusted by evolution to adapt to different environments, as shown by proteins from psychrophilic, mesophilic and thermophilic organisms (; Rabbani et al., 2023).
The mechanism of protein folding has been a “Holy Grail” of modern biology. Again, the key seems to be depicting conformational ensembles according to a rough energy landscape of protein folding with populated metastable states (). A related open question remains how to model the unfolded state, the goal being characterization of conformational ensembles along the folding coordinate from the unfolded to the native state. Furthermore, the intracellular environment affects conformational ensembles, stability and dynamic quaternary structure by crowding effects, phase transitions, post-translational modifications (PTMs) and transient intermolecular interactions (quinary structure) (; Selenko, 2019; Marciano et al., 2022). The physiological folding process is in competition with aberrant pathways leading to misfolded conformations and amyloid aggregates. Innovative diagnostic and therapeutic strategies for amyloid diseases needs to take into account the dynamics of amyloidogenic proteins and supramolecular complexes (Sun et al., 2023).
Molecular evolution has posed varying degrees of selective pressure on protein dynamics (Patil, 2022). Some biological functions, mostly regulatory switches, involve proteins or protein regions that do not fold by themselves into an ordered three-dimensional structure. These “intrinsically disordered proteins” (IDPs) or “intrinsically disordered regions” (IDRs) may fold partially or completely upon binding a partner. This adaptability may enable combinatorial interactions with multiple partners (). More than half of the human proteome contains predicted IDRs of 30 residues or longer (Toth-Petroczy et al., 2016). The functional advantages of structural disorder coexist with the threat of misfolding and aggregation (Tsoi et al., 2023). IDPs and IDRs are key actors in physiology, as well as in many devastating diseases, and represent targets of central pharmacological interest ().
Molecular chaperones (Margulies et al., 2022), molecular motors (), pores (), transporters (), and host proteins of membraneless organelles () provide additional examples of the biological relevance of protein structural dynamics.
Methodological approaches
The shift from static to dynamic protein characterization poses major technical challenges. The ambition is to approach equilibrium and kinetics, therefore dealing with heterogeneity of conformational ensembles and motions describing conformational transitions. Advancements in biophysical methods are bringing these difficult tasks within reach, but still many bottlenecks must be overcome. While no single method can give us the answers, the combination of multiple techniques in an “integrative structural biology” approach is highly promising (). Computational methods have an increasingly important role in guiding, interpreting and complementing experiments, and multiple experimental approaches exploiting orthogonal principles can be combined.
The experimental techniques yielding atomic-resolution models of protein structures are X-ray crystallography, cryogenic electron microscopy (cryo-EM) and nuclear magnetic resonance (NMR). By observing molecules in a crystal lattice, X-ray crystallography has limitations in studying protein dynamics. However, the development of time-resolved crystallography by X-ray free electron lasers (XFELs) has opened new avenues, yielding serial diffraction data-sets from protein microcrystals with femtosecond time resolution (serial femtosecond crystallography, SFX) and overcoming radiation damages by a “diffraction-before-destruction” approach (Liu and Lee, 2019). Combination with light pulses has revealed structural dynamics and transient states in light-sensitive proteins (Malla and Schmidt, 2022; Weik and Domratcheva, 2022). Other protein types can be approached, by using substrate or ligand diffusion, rather than irradiation, as an external stimulus (Malla and Schmidt, 2022).
Limitations of this approach are the dependence on crystallization, which can be difficult or impossible for some proteins and can affect conformational states, and data averaging, rather than yielding single-particle information. The latter view is of utmost importance in the investigation of heterogeneous systems. Thus, further development of XFEL technology towards single particle imaging (SPI) is highly attractive. An innovative perspective is to exploit the ion-sorting capabilities of mass spectrometry (MS) for sample injection in XFEL-SPI (Kadek et al., 2021), replacing the flow of microcrystals by the flow of single particles produced by electrospray ionization (ESI). These could then be presented to XFEL after mass selection and possibly also conformational selection by ion mobility (IM). Future improvements in XFEL power and resolution, and advances in hybrid technologies, could make this approach available for in-depth analysis of protein conformational ensembles.
Cryo-EM has entered into the realm of high-resolution techniques for protein structure determination. Rapid sample vitrification at cryogenic temperatures into a thin layer of vitreous ice, followed by EM imaging of a high number of single particles, allows reconstructing three-dimensional structures with atomic resolution, bypassing crystallization (). Rapid mixing and rapid freezing technologies are making time-resolved cryo-EM a reality on the millisecond time scale, although optimization will be needed for routine practice and improved time resolution (Klebl et al., 2021; Mäeots and Enchev, 2022). Cryo-EM can face the dual challenges of kinetic and equilibrium heterogeneity. Ensemble reconstruction into distinct conformers is already well established and progress is being made in methodologies to face continuous conformational heterogeneity (Toader et al., 2023). These tools will bring us closer to the fundamental goal of depicting conformational ensembles at atomic resolution.
NMR measures protein structural features in solution or solid state. It describes protein dynamics by an array of different protocols and observables (; Nishiyama et al., 2023) on a wide time scale, from days to picoseconds in combination with molecular-dynamics (MD) simulations (Stenström et al., 2022). NMR has treated highly dynamic systems, such as IDPs (; ), and difficult-to-handle systems, such as membrane proteins (Xue et al., 2021; ) and amyloid aggregates (). NMR is inherently a bulk method with little potential for single-molecule implementations and is exposed to the issue of signal averaging, depending on the time scale of the transitions and the procedure employed. However, the combination with MD simulations and low-resolution methods describing species distributions is a powerful approach to tackle structural heterogeneity (). Solid-state NMR at cryogenic temperatures could open new avenues to dissecting disordered ensembles (Kragelj et al., 2023). Combination with rapid-mixing and freeze-trapping technologies offers promising perspectives for kinetic studies, e.g., capturing intermediates of protein folding and assembly on the millisecond timescale ().
Ideally, we would also like to investigate proteins in vivo, under the influence of their native environment. The advancements of in-cell and in-situ NMR offer unique possibilities in this regard (Luchinat and Banci, 2022). In-cell NMR, in either solution- or solid-state, has already successfully characterized folded, soluble proteins disordered ensembles, PTMs, quinary structure, ligand binding, dynamics of membrane proteins and amyloid aggregation (Selenko, 2019; ; Theillet and Luchinat, 2022). One challenge is sample maintenance during the long acquisition times. Dedicated bioreactors seem a promising perspective. Microorganisms and mammalian cells have proven amenable to in-cell NMR. Possibly, organoids or small animals will become tractable in the future, thanks to improved sensitivity. Again, the most promising perspective lies in hybrid strategies, particularly exploiting the complementarity of NMR and cryo-electron tomography (cryo-ET) (Theillet and Luchinat, 2022).
Cryo-ET takes projection images of appropriately thinned biological samples, such as cells and tissues, under cryogenic conditions from different angles, computationally reconstructing three-dimensional images (; Moebel and Kervrann, 2022). The presently limited resolution is offset by the amplitude of the inspected field, which could in principle embrace the entire proteome, motivating expectations for the emerging new field of “visual proteomics” (). Averaging over multiple copies can lead to near-atomic resolution, although not yet for low-abundance or small structures. The so-called “resolution revolution” will likely continue, opening new avenues to in-situ structural biology (Kwon, 2021). Cryo-ET could address structural heterogeneity and fast-freezing technology could be adaptable to kinetic studies.
A very important synergism can be established between high-resolution techniques that struggle to dissect conformational ensembles and low-resolution techniques that describe, instead, structural heterogeneity. This synergism is well established for small-angle, X-ray or neutron scattering (SAXS, SANS), integrating measurements with advanced algorithms for spectra deconvolution and analysis of polydisperse systems (). Other methods, such as structural MS and atomic-force microscopy (AFM), go a step further, by physically sorting particles and assessing their individual properties, being intrinsically free from averaging shortcomings. Structural MS includes different strategies (). Native MS provides species distributions of conformers and supramolecular complexes upon gentle desolvation and ionization, yielding values of stoichiometry and solvent accessible surface area (Tamara et al., 2022). Combination with IM introduces an additional dimension in ion separation and analysis, detecting structural heterogeneity even within each charge state and measuring rotationally-averaged, collisional cross-section (CCS) (Kaltashov et al., 2022; Santambrogio et al., 2022, ; Reid et al., 2023).
Alternatively, conformation-sensitive labeling in solution is followed by denaturing MS analysis. Labeling can probe accessibility/flexibility, as isotope exchange (Masson et al., 2019), fast photochemical oxidation (FPOP) () and limited proteolysis (Malinovska et al., 2023), or generate distance restraints, as chemical cross-linking in vitro or in vivo (Piersimoni et al., 2022). This rich structural information can guide particle classification in cryo-EM/ET, ensemble deconvolution by NMR and computational modelling by experimental constraints. Structural heterogeneity deriving from PTMs, ligand binding and protein assemblies can also be described (Kaltashov et al., 2022; Reid et al., 2023). MS methods can in principle be implemented in a time-resolved mode on the millisecond scale (even microsecond for FPOP), although such instrumental setups are still not widespread (Lento and Wilson, 2022). An interesting hybrid approach (Martinez Molina et al., 2013) is thermal proteome profiling, which monitors proteome-wide changes in protein thermal stability in vitro or in vivo upon application of a stimulus (Mateus et al., 2020).
AFM measures height and force of a cantilever making interactions with proteins on a surface in aqueous media, a single molecule at a time (Müller et al., 2021; Yu and Yoshimura, 2021). The surface is scanned by a sharp tip, functionalized according to the experimental design, reaching speeds of ∼70 frames/second in high-speed AFM (HS-AFM) (). AFM can be implemented for imaging (microscopy) of biological or nanostructured surfaces, or for unfolding and interaction analysis (force spectroscopy) on recombinant proteins and polyproteins. Images at micrometer-to-sub-nanometer resolution can be obtained on isolated proteins, membrane-mimetic systems, organelles, living cells and tissues (Müller et al., 2021; Yu and Yoshimura, 2021). HS-AFM provides movies of protein conformational dynamics, polymerization and depolymerization, folding and unfolding, binding and dissociation, and measurements of the involved forces (; Lostao et al., 2023).
AFM can be combined with microscopy and spectroscopy probes, achieving higher levels of morphological and biochemical characterization (Müller et al., 2021). Coupling with Fourier-transform infrared spectroscopy enables chemical imaging and quantitative secondary-structure determination of single protein molecules (Ruggeri et al., 2020). Complementation by optical and magnetic tweezers () offers versatile approaches to study protein folding and protein machineries (; ; ). Förster Resonance Energy Transfer (FRET) brings single-molecule investigation inside living cells, with nanoscale spatiotemporal resolution (Puthenveetil et al., 2022). Although it requires protein labeling, it is a versatile method for in-vivo investigation of protein dynamics (; Majumdar and Mukhopadhyay, 2022). It has also been combined with AFM for simultaneous recording of pulling force and FRET trajectories of individual unfolding events ().
MD simulations describe vibrations and motions at atomic resolution, offering a unique view into mechanistic aspects of protein dynamics. Advancements in computational biophysics are making accessible progressively larger systems on progressively larger timescales (). Multiscale quantum mechanics/molecular mechanics (QM/MM) simulations bring quantum-mechanical accuracy into simulations of large protein structures and supramolecular complexes towards the exascale (; Vennelakanti et al., 2022; Kar, 2023). Supercomputers with exascale calculation power afford simulation times in the submillisecond (or even millisecond) timescale, covering biochemically relevant processes, from enzyme catalysis to conformational transitions and can simulate large supramolecular structures (millions of atoms) on the microsecond timescale (; Melo and Bernardi, 2023). Force fields are being optimized to face the specific complexity of folded, soluble proteins membrane proteins and IDPs (Piana et al., 2020; ; Robertson and Skiniotis, 2022). Enhanced sampling algorithms improve simulation performance on rough energy landscapes (; ). The combination with machine-learning methods further improve investigation of transitions between metastable states and simulations of large ensembles (; ).
MD simulations are suitable for equilibrium and kinetic investigation of macromolecular systems. For the relatively small IDP α-synuclein (140 aa), simulations of the entire conformational ensemble, validated against NMR chemical shifts, describe the conformational landscape in the presence or absence of ligands (Robustelli et al., 2022). Large-scale, all-atom, explicit-solvent simulations have reached impressive sample sizes, such as the chromatophore organelle (Singharoy et al., 2019) and bacterial cytoplasm (Yu et al., 2016; Oliveira Bortot et al., 2020). Kinetic studies can capture transition states, calculate dissociation rates of protein-drug complexes (; Sohraby and Nunes-Alves, 2022), simulate irreversible conformational changes () and amyloid aggregation (Rizzuti, 2022). The role of the computational approach is continually expanding towards multidisciplinary investigation guided by reciprocal input and mutual validation with experiments (Paissoni and Camilloni, 2021; Sali, 2021) and the computational challenges become larger rather than smaller, as the experimental methods become more sophisticated and integrated. Exciting perspectives take shape, translating structural into functional models, designing new proteins, making disordered targets druggable and simulating protein dynamics in vivo.
A revolution we are witnessing is de-novo structural prediction of folded proteins at atomic resolution and near-experimental accuracy by deep-learning methods (; Varadi et al., 2023). Notably, AlphaFold2 and RoseTTAFold decipher co-evolution of residue pairs from multiple sequence alignments and compute inter-residue distance probabilities, then guiding three-dimensional modeling (; ). Language models, such as ESMFold, learn to speak the protein language from known structures and predict structures from single sequences, relieving the requirement for multiple-sequence alignments and enabling exploration of regions of the protein universe that are not represented in current databases, including products of metagenomic sequences (; Lin et al., 2023). The number of parameters used to train the language model impacts on model resolution and fidelity. By increasing the ESMFold parameters up to 15 billion, model accuracy from single sequences compares with those by RoseTTAFold and AlphaFold2 from multiple sequence alignments (Lin et al., 2023). These impressive achievements make predicted structures of primary importance in structural proteomics, potentially closing the “sequence-structure gap” (Varadi et al., 2023), boosting in-silico drug screening and drug design, interactomics, functional investigation, evolutionary studies and more. Deep-learning methods will also be applicable to the reverse issue of predicting sequences from structures for protein design ().
However, a still missing ingredient in these predictors is protein dynamics, since they output single structures rather than structural distributions and fail to predict folding pathways and conformational changes (Outeiral et al., 2022; Lane, 2023). Efforts are being devoted to predict alternative conformations (Stein and Mchaourab, 2022; ; Vani et al., 2023). Expectations are legitimate, since protein dynamics and folding pathways, just like structures, are encoded by the protein sequence and subjected to selective pressure (; Tang et al., 2022; Zhao et al., 2023). Increasing experimental characterization of protein dynamics will also help training more sophisticated deep-learning algorithms. AlphaFold2 achieves a ∼60% structural coverage of the human proteome, on a per-residue basis, with good confidence (Tunyasuvunakool et al., 2021). The portion that still lacks structural models largely overlaps with the predicted disordered proteome, to the point that the AlphaFold2 confidence score performs as an excellent disorder predictor, compared with state-of-the-art tools (Tunyasuvunakool et al., 2021; ; Ma et al., 2023). The challenge is shifting from predicting disorder to modeling disordered ensembles. The combination with MD simulations and NMR is promising and has been undertaken (Laurents, 2022; Nussinov et al., 2022; ; Ma et al., 2023). Deep-learning structural proteomics is a new important actor in integrative structural biology and mutual exchange with complementary approaches will be extremely fruitful (Figure 1).
FIGURE 1
Future grand challenges
Integrative structural biology began approximately a decade ago (Ward et al., 2013) and impressive progress is continually made, interpreting the role of conformational entropy and structural dynamics in biological activity. The major challenge is modeling conformational ensembles and their changes during biological processes, as well as making such investigation accessible in vivo. Another major challenge is high-throughput application of biophysical methods to reach the omic scale and integrated omic sciences. Structural and functional proteomics will then be mature to support systems biology and precision medicine.
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The author RG declared that she was an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
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References
1
AgajanianS.AlshahraniM.BaiF.TaoP.VerkhivkerG. M. (2023). Exploring and learning the universe of protein allostery using artificial intelligence augmented biophysical and computational approaches. J. Chem. Inf. Model.63 (5), 1413–1428. 10.1021/acs.jcim.2c01634
2
AhmadK.RizziA.CapelliR.MandelliD.LyuW.CarloniP. (2022). Enhanced-sampling simulations for the estimation of ligand binding kinetics: Current status and perspective. Front. Mol. Biosci.9, 899805. 10.3389/fmolb.2022.899805
3
AhujaL. G.AotoP. C.KornevA. P.VegliaG.TaylorS. S. (2019). Dynamic allostery-based molecular workings of kinase:peptide complexes. Proc. Natl. Acad. Sci.116 (30), 15052–15061. 10.1073/pnas.1900163116
4
Alegre-CebolladaJ. (2021). Protein nanomechanics in biological context. Biophys. Rev.13 (4), 435–454. 10.1007/s12551-021-00822-9
5
AlQuraishiM. (2021). Machine learning in protein structure prediction. Curr. Opin. Chem. Biol.65, 1–8. 10.1016/j.cbpa.2021.04.005
6
AndoT. (2022). Functional implications of dynamic structures of intrinsically disordered proteins revealed by high-speed AFM imaging. Biomolecules12 (12), 1876. 10.3390/biom12121876
7
AnsariN.RizziV.CarloniP.ParrinelloM. (2021). Water-triggered, irreversible conformational change of SARS-CoV-2 main protease on passing from the solid state to aqueous solution. J. Am. Chem. Soc.143 (33), 12930–12934. 10.1021/jacs.1c05301
8
ArcusV. L.MulhollandA. J. (2020). Temperature, dynamics, and enzyme-catalyzed reaction rates. Annu. Rev. Biophysics49 (1), 163–180. 10.1146/annurev-biophys-121219-081520
9
ArigaT.TomishigeM.MizunoD. (2020). Experimental and theoretical energetics of walking molecular motors under fluctuating environments. Biophys. Rev.12 (2), 503–510. 10.1007/s12551-020-00684-7
10
BaekM.DiMaioF.AnishchenkoI.DauparasJ.OvchinnikovS.LeeG. R.et al (2021). Accurate prediction of protein structures and interactions using a three-track neural network. Science373 (6557), 871–876. 10.1126/science.abj8754
11
BanerjeeS.ChakrabortyS.SreepadaA.BanerjiD.GoyalS.KhuranaY.et al (2021). Cutting-edge single-molecule technologies unveil new mechanics in cellular biochemistry. Annu. Rev. Biophysics50 (1), 419–445. 10.1146/annurev-biophys-090420-083836
12
BaumeisterW. (2022). Cryo-electron tomography: The power of seeing the whole picture. Biochem. Biophysical Res. Commun.633, 26–28. 10.1016/j.bbrc.2022.08.078
13
BekkerG.-J.KamiyaN. (2022). Advancing the field of computational drug design using multicanonical molecular dynamics-based dynamic docking. Biophys. Rev.14 (6), 1349–1358. 10.1007/s12551-022-01010-z
14
BhatS.BlunckR. (2022). Characterising ion channel structure and dynamics using fluorescence spectroscopy techniques. Biochem. Soc. Trans.50 (5), 1427–1445. 10.1042/BST20220605
15
BhatiaH.AydinF.CarpenterT. S.LightstoneF. C.BremerP. T.IngólfssonH. I.et al (2023). The confluence of machine learning and multiscale simulations. Curr. Opin. Struct. Biol.80, 102569. 10.1016/j.sbi.2023.102569
16
BiasiniE.FaccioliP. (2023). Functional, pathogenic, and pharmacological roles of protein folding intermediates. Proteins [Preprint]. 10.1002/prot.26479
17
BolnykhV.RossettiG.RothlisbergerU.CarloniP. (2021). Expanding the boundaries of ligand–target modeling by exascale calculations. WIREs Comput. Mol. Sci.11 (4), e1535. 10.1002/wcms.1535
18
BonaccorsiM.Le MarchandT.PintacudaG. (2021). Protein structural dynamics by magic-angle spinning NMR. Curr. Opin. Struct. Biol.70, 34–43.10.1016/j.sbi.2021.02.008
19
BorkotokyS.DeyD.HazarikaZ.JoshiA.TripathiK. (2022). Unravelling viral dynamics through molecular dynamics simulations - a brief overview. Biophys. Chem.291, 106908. 10.1016/j.bpc.2022.106908
20
BunzelH. A.AndersonJ. L. R.HilvertD.ArcusV. L.van der KampM. W.MulhollandA. J. (2021). Evolution of dynamical networks enhances catalysis in a designer enzyme. Nat. Chem.13 (10), 1017–1022. 10.1038/s41557-021-00763-6
21
BuzónP.MaityS.ChristodoulisP.WiertsemaM. J.DunkelbargerS.KimC.et al (2021). Virus self-assembly proceeds through contact-rich energy minima. Sci. Adv.7 (45), eabg0811. 10.1126/sciadv.abg0811
22
CallawayE. (2022). AlphaFold’s new rival? Meta AI predicts shape of 600 million proteins. Nature611 (7935), 211–212. 10.1038/d41586-022-03539-1
23
Camacho-ZarcoA. R.SchnapkaV.GusevaS.AbyzovA.AdamskiW.MillesS.et al (2022). NMR provides unique insight into the functional dynamics and interactions of intrinsically disordered proteins. Chem. Rev.122 (10), 9331–9356. 10.1021/acs.chemrev.1c01023
24
CapelliR.BochicchioA.PicciniG.CasasnovasR.CarloniP.ParrinelloM. (2019). Chasing the full free energy landscape of neuroreceptor/ligand unbinding by metadynamics simulations. J. Chem. Theory Comput.15 (5), 3354–3361. 10.1021/acs.jctc.9b00118
25
CareyJ. (2022). Affinity, specificity, and cooperativity of DNA binding by bacterial gene regulatory proteins. Int. J. Mol. Sci.23 (1), 562. 10.3390/ijms23010562
26
CarvalhoH. F.RoqueA. C. A.IranzoO.BrancoR. J. F. (2015). Comparison of the internal dynamics of metalloproteases provides new insights on their function and evolution. PloS One10 (9), e0138118. 10.1371/journal.pone.0138118
27
ChakrabartiP.ChakravartyD. (2022). Intrinsically disordered proteins/regions and insight into their biomolecular interactions. Biophys. Chem.283, 106769. 10.1016/j.bpc.2022.106769
28
ChoudharyS.LopusM.HosurR. V. (2022). Targeting disorders in unstructured and structured proteins in various diseases. Biophys. Chem.281, 106742. 10.1016/j.bpc.2021.106742
29
ChristofiE.BarranP. (2023). Ion mobility mass spectrometry (IM-MS) for structural biology: Insights gained by measuring mass, charge, and collision cross section. Chem. Rev.123 (6), 2902–2949. 10.1021/acs.chemrev.2c00600
30
ChuW.-T.YanZ.ChuX.ZhengX.LiuZ.XuL.et al (2021). Physics of biomolecular recognition and conformational dynamics. Rep. Prog. Phys.84 (12), 126601. 10.1088/1361-6633/ac3800
31
CornwellO.AultJ. R. (2022). Fast photochemical oxidation of proteins coupled with mass spectrometry. Biochimica Biophysica Acta (BBA) - Proteins Proteomics1870 (9), 140829. 10.1016/j.bbapap.2022.140829
32
Coskuner-WeberO.CaglayanS. I. (2021). Secondary structure dependence on simulation techniques and force field parameters: From disordered to ordered proteins. Biophys. Rev.13 (6), 1173–1178. 10.1007/s12551-021-00850-5
33
Da VelaS.SvergunD. I. (2020). Methods, development and applications of small-angle X-ray scattering to characterize biological macromolecules in solution. Curr. Res. Struct. Biol.2, 164–170. 10.1016/j.crstbi.2020.08.004
34
DaiX.FuW.ChiH.MesiasV. S. D.ZhuH.LeungC. W.et al (2021). ‘Optical tweezers-controlled hotspot for sensitive and reproducible surface-enhanced Raman spectroscopy characterization of native protein structures’, Nat. Commun., 12 (1), p. 1292.10.1038/s41467-021-21543-3
35
DauparasJ.AnishchenkoI.BennettN.BaiH.RagotteR. J.MillesL. F.et al (2022). Robust deep learning-based protein sequence design using ProteinMPNN. Science378 (6615), 49–56. 10.1126/science.add2187
36
DoerrA. (2017). Cryo-electron tomography. Nat. Methods14 (1), 34. 10.1038/nmeth.4115
37
DysonH. J.WrightP. E. (2021). NMR illuminates intrinsic disorder. Curr. Opin. Struct. Biol.70, 44–52. 10.1016/j.sbi.2021.03.015
38
EvansR.RamisettyS.KulkarniP.WeningerK. (2023). Illuminating intrinsically disordered proteins with integrative structural biology. Biomolecules13 (1), 124. 10.3390/biom13010124
39
FürstM. J.FiorentiniF.FraaijeM. W. (2019). Beyond active site residues: Overall structural dynamics control catalysis in flavin-containing and heme-containing monooxygenases. Curr. Opin. Struct. Biol.59, 29–37. 10.1016/j.sbi.2019.01.019
40
GoodsellD. S. (2012). Putting proteins in context. BioEssays34 (9), 718–720. 10.1002/bies.201200072
41
GuaitaM.WattersS. C.LoerchS. (2022). Recent advances and current trends in cryo-electron microscopy. Curr. Opin. Struct. Biol.77, 102484. 10.1016/j.sbi.2022.102484
42
GuinD.GruebeleM. (2019). Weak chemical interactions that drive protein evolution: Crowding, sticking, and quinary structure in folding and function. Chem. Rev.119 (18), 10691–10717. 10.1021/acs.chemrev.8b00753
43
GünselU.HagnF. (2022). Lipid nanodiscs for high-resolution NMR studies of membrane proteins. Chem. Rev.122 (10), 9395–9421. 10.1021/acs.chemrev.1c00702
44
GuoH.-B.PerminovA.BekeleS.KedzioraG.FarajollahiS.VaraljayV.et al (2022). AlphaFold2 models indicate that protein sequence determines both structure and dynamics. Sci. Rep.12 (1), 10696. 10.1038/s41598-022-14382-9
45
HalabelianL.RicagnoS.GiorgettiS.SantambrogioC.BarbiroliA.PellegrinoS.et al (2014). Class I major histocompatibility complex, the trojan horse for secretion of amyloidogenic β2-microglobulin. J. Biol. Chem.289 (6), 3318–3327. 10.1074/jbc.M113.524157
46
HardenbergM.HorvathA.AmbrusV.FuxreiterM.VendruscoloM. (2020). Widespread occurrence of the droplet state of proteins in the human proteome. Proc. Natl. Acad. Sci.117 (52), 33254–33262. 10.1073/pnas.2007670117
47
HarishB.SwapnaG.KornhaberG. J.MontelioneG. T.CareyJ. (2017). Multiple helical conformations of the helix-turn-helix region revealed by NOE-restrained MD simulations of tryptophan aporepressor, TrpR. Proteins85 (4), 731–740. 10.1002/prot.25252
48
HatosA.TeixeiraJ. M.Barrera-VilarmauS.HorvathA.TosattoS. C. E.VendruscoloM.et al (2023). FuzPred: a web server for the sequence-based prediction of the context-dependent binding modes of proteins. Nucleic Acids Res.[Preprint]. 10.1093/nar/gkad214
49
HaubrichK.SpiteriV. A.FarnabyW.SobottF.CiulliA. (2023). Breaking free from the crystal lattice: Structural biology in solution to study protein degraders. Curr. Opin. Struct. Biol.79, 102534. 10.1016/j.sbi.2023.102534
50
HeY.LuM.CaoJ.LuH. P. (2012). Manipulating protein conformations by single-molecule AFM-FRET nanoscopy. ACS Nano6 (2), 1221–1229. 10.1021/nn2038669
51
HendriksK.ÖsterC.LangeA. (2021). Structural plasticity of the selectivity filter in cation channels. Front. Physiology12, 792958. 10.3389/fphys.2021.792958
52
HéninJ. (2022). Enhanced sampling methods for molecular dynamics simulations. Living J. Comput. Mol. Sci.4 (1), 1583. 10.33011/livecoms.4.1.1583
53
HouW.XuD.WangL.ChenY.ChenZ.ZhouC.et al (2022). Plastic structures for diverse substrates: A revisit of human abc transporters. Proteins Struct. Funct. Bioinforma.90 (10), 1749–1765. 10.1002/prot.26406
54
JeonJ.ThurberK. R.GhirlandoR.YauW. M.TyckoR. (2019). Application of millisecond time-resolved solid state NMR to the kinetics and mechanism of melittin self-assembly. Proc. Natl. Acad. Sci.116 (34), 16717–16722. 10.1073/pnas.1908006116
55
JukicN.PerrinoA. P.Redondo-MorataL.ScheuringS. (2023). Structure and dynamics of ESCRT-III membrane remodeling proteins by high-speed atomic force microscopy. J. Biol. Chem.299 (4), 104575. 10.1016/j.jbc.2023.104575
56
JumperJ.EvansR.PritzelA.GreenT.FigurnovM.RonnebergerO.et al (2021). Highly accurate protein structure prediction with AlphaFold. Nature596 (7873), 583–589. 10.1038/s41586-021-03819-2
57
KadekA.LorenzenK.UetrechtC. (2021). In a flash of light: X-Ray free electron lasers meet native mass spectrometry. Drug Discov. Today Technol.39, 89–99. 10.1016/j.ddtec.2021.07.001
58
KaltashovI. A.IvanovD. G.YangY. (2022). Mass spectrometry-based methods to characterize highly heterogeneous biopharmaceuticals, vaccines, and nonbiological complex drugs at the intact-mass level. Mass Spectrom. Rev., e21829. 10.1002/mas.21829
59
KarR. K. (2023). Benefits of hybrid QM/MM over traditional classical mechanics in pharmaceutical systems. Drug Discov. Today28 (1), 103374. 10.1016/j.drudis.2022.103374
60
KleblD. P.WhiteH. D.SobottF.MuenchS. P. (2021). On-grid and in-flow mixing for time-resolved cryo-EM. Acta Crystallogr. Sect. D. Struct. Biol.77 (10), 1233–1240. 10.1107/S2059798321008810
61
KrageljJ.DumariehR.XiaoY.FrederickK. K. (2023). Conformational ensembles explain NMR spectra of frozen intrinsically disordered proteins. Protein Sci. , 32 (5), e4628.Conformational ensembles explain NMR
62
KwonD. (2021). The secret lives of cells — As never seen before. Nature598 (7882), 558–560. 10.1038/d41586-021-02904-w
63
LaneT. J. (2023). Protein structure prediction has reached the single-structure frontier. Nat. Methods20 (2), 170–173. 10.1038/s41592-022-01760-4
64
LaurentsD. V. (2022). AlphaFold 2 and NMR spectroscopy: Partners to understand protein structure, dynamics and function. Front. Mol. Biosci.9, 906437. 10.3389/fmolb.2022.906437
65
LentoC.WilsonD. J. (2022). Subsecond time-resolved mass spectrometry in dynamic structural biology. Chem. Rev.122 (8), 7624–7646. 10.1021/acs.chemrev.1c00222
66
LinZ.AkinH.RaoR.HieB.ZhuZ.LuW.et al (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science379 (6637), 1123–1130. 10.1126/science.ade2574
67
LiuH.LeeW. (2019). The XFEL protein crystallography: Developments and perspectives. Int. J. Mol. Sci.20 (14), 3421. 10.3390/ijms20143421
68
LostaoA.LimK.PallarésM. C.PtakA.MarcuelloC. (2023). Recent advances in sensing the inter-biomolecular interactions at the nanoscale – a comprehensive review of AFM-based force spectroscopy. Int. J. Biol. Macromol.238, 124089. 10.1016/j.ijbiomac.2023.124089
69
LuchinatE.BanciL. (2022). In-cell NMR: From target structure and dynamics to drug screening. Curr. Opin. Struct. Biol.74, 102374. 10.1016/j.sbi.2022.102374
70
LycusP.EinsleO.ZhangL. (2023). Structural biology of proteins involved in nitrogen cycling. Curr. Opin. Chem. Biol.74, 102278. 10.1016/j.cbpa.2023.102278
71
MaP.LiD.BrüschweilerR. (2023). Predicting protein flexibility with AlphaFold. Proteins Struct. Funct. Bioinforma.91, 847–855. 10.1002/prot.26471
72
MadanL. K.WelshC. L.KornevA. P.TaylorS. S. (2023). The “violin model”: Looking at community networks for dynamic allostery. J. Chem. Phys.158 (8), 081001. 10.1063/5.0138175
73
MäeotsM.-E.EnchevR. I. (2022). Structural dynamics: Review of time-resolved cryo-EM. Acta Crystallogr. Sect. D. Struct. Biol.78 (8), 927–935. 10.1107/S2059798322006155
74
MajumdarA.MukhopadhyayS. (2022). Excitation energy migration to study protein oligomerization and amyloid formation. Biophys. Chem.281, 106719. 10.1016/j.bpc.2021.106719
75
MalinovskaL.CappellettiV.KohlerD.PiazzaI.TsaiT. H.PepelnjakM.et al (2023). Proteome-wide structural changes measured with limited proteolysis-mass spectrometry: An advanced protocol for high-throughput applications. Nat. Protoc.18 (3), 659–682. 10.1038/s41596-022-00771-x
76
MallaT. N.SchmidtM. (2022). Transient state measurements on proteins by time-resolved crystallography. Curr. Opin. Struct. Biol.74, 102376. 10.1016/j.sbi.2022.102376
77
MarcianoS.DeyD.ListovD.FleishmanS. J.Sonn-SegevA.MertensH.et al (2022). Protein quaternary structures in solution are a mixture of multiple forms. Chem. Sci.13 (39), 11680–11695. 10.1039/D2SC02794A
78
MarguliesD. H.TaylorD. K.JiangJ.BoydL. F.AhmadJ.MageM. G.et al (2022). Chaperones and catalysts: How antigen presentation pathways cope with biological necessity. Front. Immunol.13, 859782. 10.3389/fimmu.2022.859782
79
MassonG. R.BurkeJ. E.AhnN. G.AnandG. S.BorchersC.BrierS.et al (2019). Recommendations for performing, interpreting and reporting hydrogen deuterium exchange mass spectrometry (HDX-MS) experiments. Nat. Methods16 (7), 595–602. 10.1038/s41592-019-0459-y
80
MateusA.KurzawaN.BecherI.SridharanS.HelmD.SteinF.et al (2020). Thermal proteome profiling for interrogating protein interactions. Mol. Syst. Biol.16 (3), e9232. 10.15252/msb.20199232
81
MeloM. C. R.BernardiR. C. (2023). Fostering discoveries in the era of exascale computing: How the next generation of supercomputers empowers computational and experimental biophysics alike. Biophysical J. 10.1016/j.bpj.2023.01.042
82
MoebelE.KervrannC. (2022). Towards unsupervised classification of macromolecular complexes in cryo electron tomography: Challenges and opportunities. Comput. Methods Programs Biomed.225, 107017. 10.1016/j.cmpb.2022.107017
83
MolinaD. M.JafariR.IgnatushchenkoM.SekiT.LarssonE. A.DanC.et al (2013). Monitoring drug target engagement in cells and tissues using the cellular thermal shift assay. Science341 (6141), 84–87. 10.1126/science.1233606
84
MüllerD. J.DumitruA. C.Lo GiudiceC.GaubH. E.HinterdorferP.HummerG.et al (2021). Atomic force microscopy-based force spectroscopy and multiparametric imaging of biomolecular and cellular systems. Chem. Rev.121 (19), 11701–11725. 10.1021/acs.chemrev.0c00617
85
NishiyamaY.HouG.AgarwalV.SuY.RamamoorthyA. (2023). Ultrafast magic angle spinning solid-state NMR spectroscopy: Advances in methodology and applications. Chem. Rev.123 (3), 918–988. 10.1021/acs.chemrev.2c00197
86
NussinovR.ZhangM.LiuY.JangH. (2022). AlphaFold, artificial intelligence (AI), and allostery. J. Phys. Chem. B126 (34), 6372–6383. 10.1021/acs.jpcb.2c04346
87
Oliveira BortotL.BashardaneshZ.van der SpoelD. (2020). Making soup: Preparing and validating models of the bacterial cytoplasm for molecular simulation. J. Chem. Inf. Model.60 (1), 322–331. 10.1021/acs.jcim.9b00971
88
OuteiralC.NissleyD. A.DeaneC. M. (2022). Current structure predictors are not learning the physics of protein folding. Bioinformatics38 (7), 1881–1887. 10.1093/bioinformatics/btab881
89
PaissoniC.CamilloniC. (2021). How to determine accurate conformational ensembles by metadynamics metainference: A chignolin study case. Front. Mol. Biosci.8, 694130. 10.3389/fmolb.2021.694130
90
PatilA. (2022). Enrichment patterns of intrinsic disorder in proteins. Biophys. Rev.14 (6), 1487–1493. 10.1007/s12551-022-01016-7
91
PianaS.RobustelliP.TanD.ChenS.ShawD. E. (2020). Development of a force field for the simulation of single-chain proteins and protein–protein complexes. J. Chem. Theory Comput.16 (4), 2494–2507. 10.1021/acs.jctc.9b00251
92
PiersimoniL.KastritisP. L.ArltC.SinzA. (2022). Cross-linking mass spectrometry for investigating protein conformations and protein–protein Interactions─A method for all seasons. Chem. Rev.122 (8), 7500–7531. 10.1021/acs.chemrev.1c00786
93
PuthenveetilR.ChristensonE. T.VinogradovaO. (2022). New horizons in structural biology of membrane proteins: Experimental evaluation of the role of conformational dynamics and intrinsic flexibility. Membranes12 (2), 227. 10.3390/membranes12020227
94
RabbaniG.AhmadE.AhmadA.KhanR. H. (2023). Structural features, temperature adaptation and industrial applications of microbial lipases from psychrophilic, mesophilic and thermophilic origins. Int. J. Biol. Macromol.225, 822–839. 10.1016/j.ijbiomac.2022.11.146
95
ReidD. J.ThibertS.ZhouM. (2023). Dissecting the structural heterogeneity of proteins by native mass spectrometry. Protein Sci.32 (4), e4612. 10.1002/pro.4612
96
RizzutiB. (2022). Molecular simulations of proteins: From simplified physical interactions to complex biological phenomena. Biochim Biophys Acta Proteins Proteom1870 (3), 140757. 10.1016/j.bbapap.2022.140757
97
RobertsonM. J.SkiniotisG. (2022). Development of OPLS-AA/M parameters for simulations of G protein-coupled receptors and other membrane proteins. J. Chem. Theory Comput.18 (7), 4482–4489. 10.1021/acs.jctc.2c00015
98
RobustelliP.Ibanez-de-OpakuaA.Campbell-BezatC.GiordanettoF.BeckerS.ZweckstetterM.et al (2022). Molecular basis of small-molecule binding to α-synuclein. J. Am. Chem. Soc.144 (6), 2501–2510. 10.1021/jacs.1c07591
99
RuggeriF. S.ManniniB.SchmidR.VendruscoloM.KnowlesT. P. J. (2020). Single molecule secondary structure determination of proteins through infrared absorption nanospectroscopy. Nat. Commun.11 (1), 2945. 10.1038/s41467-020-16728-1
100
SaliA. (2021). From integrative structural biology to cell biology. J. Biol. Chem.296, 100743. 10.1016/j.jbc.2021.100743
101
SantambrogioC.PonziniE.GrandoriR. (2022). Native mass spectrometry for the investigation of protein structural (dis)order. Biochim Biophys Acta Proteins Proteom1870 (10), 140828. 10.1016/j.bbapap.2022.140828
102
SelenkoP. (2019). Quo vadis biomolecular NMR spectroscopy?Int. J. Mol. Sci.20 (6), 1278. 10.3390/ijms20061278
103
SingharoyA.MaffeoC.Delgado-MagneroK. H.SwainsburyD. J.SenerM.KleinekathöferU.et al (2019). Atoms to phenotypes: Molecular design principles of cellular energy metabolism. Cell179 (5), 1098–1111.e23. 10.1016/j.cell.2019.10.021
104
SohrabyF.Nunes-AlvesA. (2023). Advances in computational methods for ligand binding kinetics. Trends Biochem. Sci.48, 437–449. 10.1016/j.tibs.2022.11.003
105
SteinR. A.MchaourabH. S. (2022). SPEACH_AF: Sampling protein ensembles and conformational heterogeneity with Alphafold2. PLoS Comput. Biol.18 (8), e1010483. 10.1371/journal.pcbi.1010483
106
StenströmO.ChampionC.LehnerM.BouvigniesG.RinikerS.FerrageF. (2022). How does it really move? Recent progress in the investigation of protein nanosecond dynamics by NMR and simulation. Curr. Opin. Struct. Biol.77, 102459. 10.1016/j.sbi.2022.102459
107
SunX.DysonH. J.WrightP. E. (2023). Role of conformational dynamics in pathogenic protein aggregation. Curr. Opin. Chem. Biol.73, 102280. 10.1016/j.cbpa.2023.102280
108
TamaraS.den BoerM. A.HeckA. J. R. (2022). High-Resolution Native Mass Spectrometry. Chem. Rev.122 (8), 7269–7326. 10.1021/acs.chemrev.1c00212
109
TangQ.-Y.RenW.WangJ.KanekoK. (2022). The statistical trends of protein evolution: A lesson from AlphaFold database. Mol. Biol. Evol.39 (10), msac197. 10.1093/molbev/msac197
110
TheilletF.-X.LuchinatE. (2022). In-cell NMR: Why and how?Prog. Nucl. Magnetic Reson. Spectrosc.133, 1–112. 10.1016/j.pnmrs.2022.04.002
111
ToaderB.SigworthF. J.LedermanR. R. (2023). Methods for cryo-EM single particle reconstruction of macromolecules having continuous heterogeneity. J. Mol. Biol.435, 168020. 10.1016/j.jmb.2023.168020
112
Toth-PetroczyA.PalmedoP.IngrahamJ.HopfT. A.BergerB.SanderC.et al (2016). Structured states of disordered proteins from genomic sequences. Cell167 (1), 158–170.e12. 10.1016/j.cell.2016.09.010
113
TsoiP. S.QuanM. D.FerreonJ. C.FerreonA. C. M. (2023). Aggregation of disordered proteins associated with neurodegeneration. Int. J. Mol. Sci.24 (4), 3380. 10.3390/ijms24043380
114
TunyasuvunakoolK.AdlerJ.WuZ.GreenT.ZielinskiM.ŽídekA.et al (2021). Highly accurate protein structure prediction for the human proteome. Nature596 (7873), 590–596. 10.1038/s41586-021-03828-1
115
VaniB. P.et al (2023). AlphaFold2-RAVE: From sequence to Boltzmann ranking. J. Chem. Theory Comput.
116
VaradiM.BordinN.OrengoC.VelankarS. (2023). The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors. Curr. Opin. Struct. Biol.79, 102543. [Preprint]. 10.1016/j.sbi.2023.102543
117
VennelakantiV.NazemiA.MehmoodR.SteevesA. H.KulikH. J. (2022). Harder, better, faster, stronger: Large-scale QM and QM/MM for predictive modeling in enzymes and proteins. Curr. Opin. Struct. Biol.72, 9–17. 10.1016/j.sbi.2021.07.004
118
WardA. B.SaliA.WilsonI. A. (2013). Integrative structural biology. Science339 (6122), 913–915. 10.1126/science.1228565
119
WeikM.DomratchevaT. (2022). Insight into the structural dynamics of light sensitive proteins from time-resolved crystallography and quantum chemical calculations. Curr. Opin. Struct. Biol.77, 102496. 10.1016/j.sbi.2022.102496
120
XueK.MovellanK. T.ZhangX. C.NajbauerE. E.ForsterM. C.BeckerS.et al (2021). Towards a native environment: Structure and function of membrane proteins in lipid bilayers by NMR. Chem. Sci.12 (43), 14332–14342. 10.1039/D1SC02813H
121
YuI.MoriT.AndoT.HaradaR.JungJ.SugitaY.et al (2016). Biomolecular interactions modulate macromolecular structure and dynamics in atomistic model of a bacterial cytoplasm. eLife5, e19274. 10.7554/eLife.19274
122
YuY.YoshimuraS. H. (2021). Investigating the morphological dynamics of the plasma membrane by high-speed atomic force microscopy. J. Cell Sci.134 (17), jcs243584. 10.1242/jcs.243584
123
ZhaoK.XiaY.ZhangF.ZhouX.LiS. Z.ZhangG. (2023). Protein structure and folding pathway prediction based on remote homologs recognition using PAthreader. Commun. Biol.6 (1), 243. 10.1038/s42003-023-04605-8
Summary
Keywords
crystallography, nuclear magnetic resonance, cryo-electron microscopy, cryo-electron tomography, mass spectrometry, atomic force microscopy, molecular dynamics simulations, deep-learning technology
Citation
Grandori R (2023) Protein structure and dynamics in the era of integrative structural biology. Front. Biophys. 1:1219843. doi: 10.3389/frbis.2023.1219843
Received
09 May 2023
Accepted
19 June 2023
Published
17 July 2023
Volume
1 - 2023
Edited by
David Alsteens, Université Catholique de Louvain, Belgium
Reviewed by
Yuichi Togashi, Ritsumeikan University, Japan
Jannette Carey, Princeton University, United States
Albert Guskov, University of Groningen, Netherlands
Updates
Copyright
© 2023 Grandori.
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*Correspondence: Rita Grandori, rita.grandori@unimib.it
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