We have evaluated the prediction accuracy of three different tools, deep-learning-based AlphaFold2, AlphaFold3, and large language model-based ESMFold, utilizing the experimentally derived structures deposited in the Protein Data Bank between 2022 an...
Journal of chemical information and modeling
Dec 30, 2025
The ability of proteins to adopt multiple conformations is fundamental to their biological function. With the advent of AlphaFold, machine learning (ML)-based methods have extended their capabilities to more broadly sample this intrinsic conformation...
Journal of chemical information and modeling
Dec 13, 2025
Molecular dynamics (MD) simulations are powerful tools for studying biomolecular systems, but they are fundamentally limited by accessible time scales, making the study of rare events such as protein folding or ligand unbinding computationally challe...
Journal of chemical information and modeling
Nov 26, 2025
Protein structure database search has become increasingly challenging due to the growing number of experimental and computational structures. We introduce mTM-align2, a novel two-step approach for rapid and accurate protein structure database search....
Proceedings of the National Academy of Sciences of the United States of America
Nov 25, 2025
Protein misfolding plays a central role in diseases such as Alzheimer's disease, Parkinson's disease, type 2 diabetes, and transthyretin amyloidosis (ATTR), often driven by specific aggregation-prone segments such as A and A of amyloid-42 (A42), -Syn...
α-Helical domains are widespread and versatile, yet typically fail under low mechanical load because backbone hydrogen bonds unzip sequentially, limiting their use in force-bearing nanomaterials and molecular devices. We present an AI-guided strategy...
Artificial intelligence is revolutionizing structural bioinformatics, with AlphaFold arguably being the most impactful development to date. The structural atlases generated by these methods present significant opportunities for unraveling biological ...
Co-folding models represent a major innovation in deep-learning-based protein-ligand structure prediction. The recent publications of RoseTTAFold All-Atom, AlphaFold3, and others have shown high-quality results on predicting the structures of protein...
The emergence of artificial intelligence in protein structure prediction has significantly advanced our understanding of protein folding. Yet, challenges remain in accurately modeling intrinsically disordered proteins (IDPs) and capturing conformatio...
Proceedings of the National Academy of Sciences of the United States of America
Sep 15, 2025
Francis Crick's global parameterization of coiled coil geometry has been widely useful for guiding design of new protein structures and functions. However, design guided by similar global parameterization of beta barrel structures has been less succe...
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