Computational Protein Bioinformatics: From Sequence and Structure to Aggregation and Dynamics

Proteins are dynamic molecular systems whose functions depend on the relationship between amino acid sequence, three-dimensional structure, conformational flexibility, intermolecular interactions, and cellular environment. Protein bioinformatics provides an integrated framework for studying these relationships through sequence analysis, structural modeling, molecular simulation, machine learning, database development, and experimental data integration. Recent advances in deep learning, protein language models, and large-scale sequence and structural resources have improved the prediction of protein structure, function, disorder, interaction sites, stability, and mutation effects. Nevertheless, major challenges remain in understanding how proteins change conformation, form pathogenic and functional supramolecular pentameric arrangements, and interact with membranes, nucleic acids, ligands, and other proteins.Protein aggregation provides an important biological context for addressing these broader challenges. Amyloid formation, amorphous aggregation, biomolecular condensation, and liquid-liquid phase separation are associated with neurodegeneration, cancer, infection, ageing, and inherited disorders. These processes may involve intrinsically disordered proteins and regions, partially folded intermediates, transient oligomers, and heterogeneous conformational ensembles. Their investigation requires methods that connect sequence composition and evolutionary information with structural transitions, aggregation propensity, kinetics, morphology, toxicity, and biological function.Computational approaches can predict aggregation-prone regions, amyloidogenic segments, disorder, phase-separation tendencies, binding sites, protein stability, and mutation effects. Molecular dynamics, enhanced sampling, coarse-grained modeling, docking, network analysis, and machine learning provide complementary perspectives on protein behavior. Experimental methods remain essential for testing these predictions, including thioflavin T assays, transmission electron microscopy, circular dichroism, small-angle X-ray scattering, hydrogen-deuterium exchange mass spectrometry, fluorescence spectroscopy, atomic force microscopy, cryo-electron microscopy, and proteomics. This Research Topic will connect general protein bioinformatics with aggregation-related questions, while welcoming studies addressing diverse and emerging challenges in protein sequence, structure, function, and dynamics.This Research Topic aims to advance protein bioinformatics research on sequence, structure, function, disorder, aggregation, and conformational dynamics. It will bring together predictive methods, experimental validation, computational resources, and databases to improve understanding of protein behavior and support reproducible, biologically relevant discoveries.To gather further insights in protein bioinformatics and aggregation-related phenomena, this Research Topic will welcome studies ranging from general protein analysis to focused investigations of misfolding, phase separation, and biomolecular assemblies. Contributions should provide transferable methods, datasets, resources, or biological insights and may include, but are not limited to:• Sequence-based prediction of protein function, stability, disorder, aggregation propensity, and amyloidogenic regions• Intrinsically disordered proteins and regions, conformational ensembles, and disorder-mediated interactions• Structural bioinformatics, protein structure prediction, folding, misfolding, and loss of native structure• Computational analysis of amyloid formation, oligomerization, fibrillation, phase separation, and biomolecular condensates• Molecular dynamics, enhanced sampling, coarse-grained modeling, docking, and aggregation pathway analysis• Machine learning and protein language models for structure, function, interactions, aggregation, and mutation effects• Experimental validation using thioflavin T, transmission electron microscopy, circular dichroism, SAXS, HDX-MS, cryo-EM, spectroscopy, microscopy, proteomics, and biochemical assays• Effects of mutations, post-translational modifications, environmental conditions, and molecular partners• Predictive web servers, software, databases, benchmark datasets, and resources integrating sequence, structure, experimental evidence, literature, and citation information• Reproducibility, uncertainty estimation, interpretability, data standards, and comparative evaluation of protein bioinformatics methods Topic editors: Valentin Iglesias, Michael Gromiha, Michał Burdukiewicz Source: https://www.frontiersin.org/research-topics/85969/computational-protein-bioinformatics-from-sequence-and-structure-to-aggregation-and-dynamics