Artificial Intelligence for Protein Conformational Analysis, Biomolecular Modeling, and Biomedical Discovery

OverviewThe concept for this Research Topic emerged from discussions held after the Deep Dive webinar dedicated to the Frontiers in Science lead article, Breaking the memory wall: next-generation artificial intelligence hardware. Proteins are not static molecular objects but dynamic conformational ensembles whose structures, motions, disorder, interactions, and transient states determine biological function and disease mechanisms. This is especially important for intrinsically disordered proteins, flexible multidomain proteins, phase-separating systems, biomolecular condensates, and disease-associated proteins involved in neurodegeneration, cancer, inflammation, infection, and metabolic disorders. Classical molecular simulations, biophysical experiments, and bioinformatics approaches have provided fundamental insight into these systems, yet many biologically important conformational states remain difficult to capture because they are transient, heterogeneous, rare, or highly context-dependent.Recent advances in artificial intelligence are transforming this field. Deep learning, protein language models, graph neural networks, diffusion models, generative ensemble approaches, AlphaFold-derived methods, AI-assisted molecular dynamics, and hybrid physics-informed models now offer new possibilities for predicting, sampling, analyzing, and interpreting protein conformational landscapes. At the same time, important challenges remain. AI models often perform best for ordered proteins and may struggle with intrinsically disordered regions, conformational heterogeneity, allostery, rare transitions, post-translational modifications, protein-protein interactions, protein-nucleic acid interactions, and disease-relevant ensemble shifts. Therefore, there is an urgent need for methods that integrate AI, molecular simulations, experimental restraints, structural bioinformatics, and biomedical interpretation.This Research Topic aims to bring together researchers developing and applying artificial intelligence approaches for protein conformational analysis, biomolecular modeling, ensemble generation, disorder-aware structural biology, drug discovery, and biomedical applications. The collection will provide a focused platform for methodological advances, benchmark studies, biological applications, and perspectives on how AI can move the field from static structure prediction toward dynamic, ensemble-based understanding of biomolecular systems.Rationale and SignificanceThe success of structure prediction methods has reshaped structural biology, but biological function is often encoded not only in a single predicted structure, but in the distribution of conformational states accessible to a biomolecule. This is particularly true for intrinsically disordered proteins and intrinsically disordered regions, which are central to signaling, regulation, transcription, neurodegeneration, cancer biology, viral infection, and liquid-liquid phase separation. These systems challenge traditional structure-centric models because they do not adopt one stable three-dimensional structure under physiological conditions.At the same time, the biomedical importance of conformational heterogeneity is rapidly increasing. Disease-associated mutations can alter conformational ensembles without necessarily producing a single obvious structural change. Drug discovery increasingly targets transient pockets, cryptic sites, allosteric states, and protein interaction interfaces that may only appear in specific conformational substates. Biomarker discovery also requires understanding how structural and dynamic features change across disease conditions, molecular environments, and patient-specific contexts.AI has the potential to accelerate this transition from static structural models to dynamic, biologically meaningful ensemble models. However, AI-based conformational analysis must be carefully validated, benchmarked, and integrated with experimental data and physical principles. This Research Topic will therefore emphasize not only new algorithms, but also interpretability, uncertainty, biological relevance, reproducibility, benchmarking, and translational applications.Scope of the Research TopicThe Research Topic will welcome original research articles, reviews, mini-reviews, perspectives, methods articles, and opinion pieces addressing AI-based approaches for biomolecular conformational analysis and biomedical discovery.Suitable contributions may focus on AI-driven protein structure and ensemble prediction, generative models for conformational sampling, integration of AlphaFold-derived approaches with molecular dynamics or experimental restraints, protein language models for conformational and functional inference, graph neural networks for biomolecular dynamics, AI approaches for intrinsically disordered proteins and regions, and disorder-aware structural bioinformatics.The collection will also include studies on AI-assisted molecular simulations, rare-event sampling, allostery, conformational selection, induced fit, protein-protein and protein-nucleic acid interactions, liquid-liquid phase separation, biomolecular condensates, post-translational modifications, mutation-induced ensemble changes, transient pocket detection, cryptic binding site discovery, fragment-based drug design, and biomedical applications in neurodegeneration, cancer, infectious disease, and precision medicine.A central aim is to encourage contributions that connect AI predictions to biological and medical interpretation. Therefore, manuscripts that integrate AI with molecular dynamics, NMR, SAXS, cryo-EM, FRET, HDX-MS, chemical shifts, mutational data, omics data, clinical data, or drug discovery pipelines will be especially encouraged. Topic editors: Orkid Coskuner-Weber, Lukasz Kurgan, Vladimir N. Uversky Source: https://www.frontiersin.org/research-topics/82436/artificial-intelligence-for-protein-conformational-analysis-biomolecular-modeling-and-biomedical-discovery