Advances in Protein Structure Biology – Use of AI and Beyond Volume II

This Research topic is a second volume of the successful Research Topic “Machine Learning in Computer-Aided Drug Design”. Please see the first Volume here.Recent advances in artificial intelligence have transformed protein structure biology into a data-rich and increasingly predictive field of biomolecular science. Structure-prediction models such as AlphaFold, RoseTTAFold, and ESMFold can generate accurate structural models for many folded proteins with unprecedented speed. These advances have also enabled specialized tools for structural search, sequence and protein design, and conformational-ensemble generation. Examples include Foldseek, ProteinMPNN, RFdiffusion, and BioEmu, respectively. These tools serve distinct purposes: RFdiffusion generates protein structures, ProteinMPNN designs sequences for specified structures, Foldseek performs rapid structural searches, and BioEmu models protein conformational ensembles.Despite rapid progress, current methods still struggle to model protein dynamics, flexible and intrinsically disordered regions, heterogeneous assemblies, and binding affinities. This Research Topic welcomes original research that addresses these challenges and advances the structural and functional interpretation of biomolecules and their complexes. Areas of interest include conformational landscapes, structure–function relationships, molecular interactions, interface characterization, and binder design, with an emphasis on deep learning, generative modelling, molecular simulation, and integrative structural biology.Both experimental and computational studies are encouraged, especially work that validates computational predictions through biochemical, biophysical, cellular, or structural experiments. We also welcome resource articles presenting curated datasets, rigorous benchmarks, open-source software, and reusable computational tools. The collection aims to highlight methods and discoveries that improve the accuracy, interpretability, reproducibility, and practical value of structural models while accelerating discovery and engineering across protein science.Potential themes include, but are not limited to:• Modelling proteins, nucleic acids, and other biomacromolecules and their complexes• Biomolecular dynamics and conformational ensembles• Intrinsically disordered proteins and flexible regions• Protein evolution and structure–function relationships• Protein engineering and de novo design• Structural bioinformatics and functional annotation• Protein–protein, protein–peptide, protein–ligand, and protein–nucleic-acid interactions• Binder design and affinity prediction• Drug discovery and structure-guided molecular design• Integrative and experimental structural biology• Structural systems biology• Biomolecular condensates and phase separation• Curated datasets, benchmarking resources, and open-source software Topic editors: Arumay Pal, Sucharita Dey Source: https://www.frontiersin.org/research-topics/86000/advances-in-protein-structure-biology---use-of-ai-and-beyond-volume-ii