Volume II: Methods, Tools and Algorithms in Protein Bioinformatics

The field of protein bioinformatics continues to evolve at a remarkable pace, driven by innovations in computational biology, artificial intelligence, and high-throughput molecular technologies. As proteome-scale datasets expand across sequence, structural, and functional dimensions, bioinformatics provides the analytical foundation to decode protein behavior, interactions, and design principles. Recent breakthroughs—from deep-learning-driven 3D structure prediction to integrative modeling of protein complexes and dynamics—are reshaping our understanding of how sequence encodes function. However, this rapid expansion also reveals pressing challenges in model interpretability, reproducibility, data integration, and benchmarking rigor, underscoring the enduring need for robust computational frameworks that can generalize across complex biological systems.



Over the past few years, machine learning has markedly enhanced protein characterization, enabling new paradigms for structure refinement, mutational effect prediction, and binding affinity estimation. Advances in simulation and hybrid modeling are bridging physics-based and data-driven views of proteins, aiding in accurate modeling of conformational flexibility, disordered regions, and macromolecular assemblies. Similarly, omics integration and network-based analytics are uncovering the systemic roles of proteins in health, disease, and evolution. While accessible web servers and open-source pipelines have democratized protein analysis, ensuring methodological transparency, reproducible outputs, and interoperability across platforms remains a central challenge. These developments call for critical and comprehensive syntheses of emerging tools and conceptual frameworks that drive innovation in protein bioinformatics and its translational applications.



This Research Topic aims to consolidate methodological progress and comparative insights on the methods, tools, and algorithms defining the next generation of protein bioinformatics. We seek contributions that critically evaluate existing techniques, introduce new computational frameworks, or synthesize advances that strengthen reproducibility, interpretability, and utility across both experimental and computational domains. By highlighting innovations that refine protein prediction, engineering, and function annotation, this collection will serve as a reference point for ongoing progress in the digital exploration of proteins.



To gather further insights into cutting-edge developments in computational protein science, we welcome submissions addressing, but not limited to, the following themes:

o Protein sequence analysis, annotation, and evolutionary inference

o Structure prediction, refinement, and validation beyond deep learning benchmarks

o Modeling of protein dynamics, flexibility, and disordered regions

o Prediction and simulation of protein–protein, protein–ligand, and protein–nucleic acid complexes

o Integrative and multi-omics approaches for functional annotation and system-level mapping

o Computational protein design, stability engineering, and mutational scanning

o Data curation, benchmarking standards, and reproducibility in protein bioinformatics

o AI, foundation models, and multimodal representation learning in protein science

o Visualization, workflow interoperability, and community-driven tool development

o Protein/peptide function prediction and mining targeting major chronic diseases

We invite papers that critically assess recent methodological trends, emerging algorithms, and open challenges in protein bioinformatics.

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Dr. Zheng Liangzhen declares that he is employed at Shanghai Zelixir Biotech Co Ltd, a company engaged in protein design and related bioinformatics activities. This affiliation is declared in accordance with Frontiers' conflict of interest policy

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Protein bioinformatics, structure prediction, deep learning, protein-protein interactions, computational protein design, multi-omics integration, protein dynamics, machine learning, functional annotation, reproducibility

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.