ArticleJournal of chemical information and modeling2024
Exploring the Alternative Conformation of a Known Protein Structure Based on Contact Map Prediction.
Article in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Biasing Conformational Sampling in AlphaFold 3 and Boltz-2 via Pair Representation Scaling.Journal of chemical information and modeling · 2026Article
- ESMDynamic: Fast and accurate prediction of protein dynamic contact maps from single sequences.Nature communications · 2026Article
- Advances and challenges in multiscale biomolecular simulations: artificial intelligence-driven paradigm shift.Quantitative biology (Beijing, China) · 2026Article
- Sequence-based drug-target binding site pre-training enables cryptic pocket detection and improves binding affinity and kinetics prediction.Journal of cheminformatics · 2026Article
- Sequence-based Drug-Target Binding Site Pretraining Enables Cryptic Pocket Detection and Improves Binding Affinity and Kinetics Prediction.bioRxiv : the preprint server for biology · 2026Article
- Generating Dynamic Structures Through Physics-Based Sampling of Predicted Inter-Residue Geometries.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Mechanism-Driven Screening of Membrane-Targeting and Pore-Forming Antimicrobial Peptides.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Incorporating targeted protein structure in deep learning methods for molecule generation in computational drug design.Chemical science · 2025Review
- A comprehensive benchmarking of the AlphaFold3 for predicting biomacromolecules and their interactions.Briefings in bioinformatics · 2025Article
- Structure Prediction of Alternate Frame Folding Systems with AlphaFold3.Journal of chemical information and modeling · 2025Article
- af2rave: protein ensemble generation with physics-based sampling.Digital discovery · 2025Article
- Article
- Predicting protein conformational motions using energetic frustration analysis and AlphaFold2.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Article
- Substrate Recognition Properties from an Intermediate Structural State of the UreA Transporter.International journal of molecular sciences · 2022Article
Corrections and comments
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The rapid development of deep learning-based methods has considerably advanced the field of protein structure prediction. The accuracy of predicting the 3D structures of simple proteins is comparable to that of experimentally determined structures, providing broad possibilities for structure-based biological studies. Another critical question is whether and how multistate structures can be predicted from a given protein sequence. In this study, analysis of tens of two-state proteins demonstrated that deep learning-based contact map predictions contain structural information on both states, which suggests that it is probably appropriate to change the target of deep learning-based protein structure prediction from one specific structure to multiple likely structures. Furthermore, by combining deep learning- and physics-based computational methods, we developed a protocol for exploring alternative conformations from a known structure of a given protein, by which we successfully approached the holo-state conformations of multiple representative proteins from their apo-state structures.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.