Methods, Tools and Algorithms in Protein Bioinformatics

Protein bioinformatics sits at the intersection of molecular biology, computer science, and statistics, providing essential methods to understand protein structure, function, dynamics and evolution. The rapid growth of protein sequence and structure databases, together with advances in high throughput technologies and deep learning, has transformed how proteins are analyzed, predicted and engineered. From sequence alignment and homology modeling to large scale interaction mapping and functional annotation, computational tools now support nearly every stage of protein research and drug discovery. Machine learning and AI driven models are improving structure prediction, stability estimation, and protein–ligand interaction analysis, while integrative approaches combine sequence, structural, evolutionary and biophysical data to generate richer, more reliable insights. At the same time, user friendly software, web servers, and pipelines have made sophisticated analyses accessible to a wider community, raising new questions about benchmarking, interpretability, reproducibility and best practices.



This topic aims to bring together methodological advances, critical evaluations, and state of the art applications that push the boundaries of what can be inferred or predicted about proteins in silico. The aim of this research topic is also to highlight and critically assess current and emerging computational methods, tools and algorithms in protein bioinformatics, with a focus on their innovation, validation, practical utility, and impact on experimental and translational protein science.



We invite Mini Reviews, Systematic Reviews, and Full Reviews on (but not limited to) the following subthemes:



- Protein sequence analysis and evolution

- Sequence analysis, feature extraction, and annotation

- Structure prediction, modeling, refinement, and validation

- Analysis of protein dynamics, flexibility, and conformational ensembles

Protein interactions and complexes

- Protein–protein, protein–ligand, and protein–nucleic acid interaction prediction

- Function prediction and protein design

- Integrative and multi omics approaches

- Tools, benchmarking and reproducibility



Mini Reviews should concisely summarize focused or emerging methods or tools. Systematic Reviews should apply transparent, reproducible methodologies to compare, benchmark or synthesize evidence across tools and algorithms. Full Reviews should provide broad, critical overviews of major methodological areas, including current challenges, limitations, and future directions in protein bioinformatics.

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
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• ... 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
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: protein bioinformatics, protein structure prediction, sequence analysis, protein–protein interactions, machine learning, deep learning, functional annotation, protein design, multi‑omics integration, benchmarking and 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.