ArticleProceedings of the National Academy of Sciences of the United States of America2024
Predicting protein conformational motions using energetic frustration analysis and AlphaFold2.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- From static structures to dynamic energy landscapes: the latest revolution in molecular biology.Current opinion in structural biology · 2026Review
- Enhanced Sampling of Protein Conformations in AlphaFold3 with Repulsive Bias in the Diffusion Generative Model.JACS Au · 2026Article
- Explainable AI reveals the allosteric blind spot in protein-ligand binding predictions.Cell reports. Physical science · 2026Article
- Integrated Framework for Probing Multimodal Protein Foundation Models with Structure-Functional Interpretability Analysis in Detection of Allosteric Binding Sites.bioRxiv : the preprint server for biology · 2026Article
- Advances and challenges in multiscale biomolecular simulations: artificial intelligence-driven paradigm shift.Quantitative biology (Beijing, China) · 2026Article
- Predicting and Decoding Allosteric Binding Sites Using Protein Language Models and Structure-Based Machine Learning: An Energy Landscape-Guided Explainable AI Framework.Journal of chemical theory and computation · 2026Article
- Concurrent chemical reaction and cluster restructuring in a frustrated fluxional catalytic system identified via simple kMC model.Scientific reports · 2026Article
- Exploration of the structural and functional diversity in the metamorphic RfaH subfamily.bioRxiv : the preprint server for biology · 2026Article
- Allosteric regulation of enzymatic catalysis by molecular crowding.Communications chemistry · 2026Article
- Generating Dynamic Structures Through Physics-Based Sampling of Predicted Inter-Residue Geometries.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Novel Rapid In Situ Curable PEGDA/PEG Composite Hydrogel Drives Biological Closure of Aneurysm Neck.ACS omega · 2026Article
- Evolutionary remodeling of the FXR binding pocket drove functional divergence and ligand discrimination.Molecular biology and evolution · 2026Article
- Probing the dark energy in the functional protein universe.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- DNA actively regulates the "safety-belt" dynamics of condensin during loop extrusion.Nature communications · 2026Article
- Binding Specificity and Local Frustration in Structure-based Drug Discovery.Current medicinal chemistry · 2026Review
- The transformative impact of AI-enabled AlphaFold 3: evolution, current status, and future prospects in structural biology.Frontiers in artificial intelligence · 2026Review
- Advances in Methods for Accurate Prediction of RNA-Small Molecule Binding Sites: From Isolated to AI-Integrated Strategies.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Prediction of enzyme function using an interpretable optimized ensemble learning framework.Chemical science · 2025Article
- Role of frustrations in cell reprogramming.PNAS nexus · 2025Article
- Hierarchical AF2RAVE for Multiconformation Virtual Screening Targeting S100 CaJournal of chemical theory and computation · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Proteins perform their biological functions through motion. Although high throughput prediction of the three-dimensional static structures of proteins has proved feasible using deep-learning-based methods, predicting the conformational motions remains a challenge. Purely data-driven machine learning methods encounter difficulty for addressing such motions because available laboratory data on conformational motions are still limited. In this work, we develop a method for generating protein allosteric motions by integrating physical energy landscape information into deep-learning-based methods. We show that local energetic frustration, which represents a quantification of the local features of the energy landscape governing protein allosteric dynamics, can be utilized to empower AlphaFold2 (AF2) to predict protein conformational motions. Starting from ground state static structures, this integrative method generates alternative structures as well as pathways of protein conformational motions, using a progressive enhancement of the energetic frustration features in the input multiple sequence alignment sequences. For a model protein adenylate kinase, we show that the generated conformational motions are consistent with available experimental and molecular dynamics simulation data. Applying the method to another two proteins KaiB and ribose-binding protein, which involve large-amplitude conformational changes, can also successfully generate the alternative conformations. We also show how to extract overall features of the AF2 energy landscape topography, which has been considered by many to be black box. Incorporating physical knowledge into deep-learning-based structure prediction algorithms provides a useful strategy to address the challenges of dynamic structure prediction of allosteric proteins.
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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 Socratic graph.