ArticleJournal of chemical theory and computation2023
Effective Molecular Dynamics from Neural Network-Based Structure Prediction Models.
Article in Journal of chemical theory and computation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- AF-CALVADOS: AlphaFold-guided simulations of multi-domain proteins at the proteome level.Protein science : a publication of the Protein Society · 2026Article
- Context-dependent peptide recognition shapes tyrosine kinase substrate specificity beyond consensus motifs.bioRxiv : the preprint server for biology · 2026Article
- Advances and challenges in multiscale biomolecular simulations: artificial intelligence-driven paradigm shift.Quantitative biology (Beijing, China) · 2026Article
- Shoebill: an interpretable AlphaFold2-informed predictor of protein crystallization propensity using XGBoost.Briefings in bioinformatics · 2026Article
- AI-Driven Design of Miniproteins as Potential Allosteric Modulators.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Improving Conformational Ensembles of Folded Proteins in Go̅Martini.Journal of chemical theory and computation · 2026Article
- Genome assembly and protein structure modeling reveal key molecular features of divergentMicrobiology spectrum · 2026Article
- Evolutionary remodeling of the FXR binding pocket drove functional divergence and ligand discrimination.Molecular biology and evolution · 2026Article
- DNA actively regulates the "safety-belt" dynamics of condensin during loop extrusion.Nature communications · 2026Article
- Temporally resolved and interpretable machine learning model of GPCR conformational transition.Nature communications · 2025Article
- Unveiling the influence of fastest nobel prize winner discovery: alphafold's algorithmic intelligence in medical sciences.Journal of molecular modeling · 2025Review
- GōMartini 3: From large conformational changes in proteins to environmental bias corrections.Nature communications · 2025Article
- Modeling Boltzmann-weighted structural ensembles of proteins using artificial intelligence-based methods.Current opinion in structural biology · 2025Review
- A coarse-grained model for disordered and multi-domain proteins.Protein science : a publication of the Protein Society · 2024Article
- Evolution of the conformational dynamics of the molecular chaperone Hsp90.Nature communications · 2024Article
- Predicting protein conformational motions using energetic frustration analysis and AlphaFold2.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Activity of botulinum neurotoxin X and its structure when shielded by a non-toxic non-hemagglutinin protein.Communications chemistry · 2024Article
- Rescaling protein-protein interactions improves Martini 3 for flexible proteins in solution.Nature communications · 2024Article
- The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins.Nature chemical biology · 2024Review
- Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors.Nature communications · 2024Article
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Recent breakthroughs in neural network-based structure prediction methods, such as AlphaFold2 and RoseTTAFold, have dramatically improved the quality of computational protein structure prediction. These models also provide statistical confidence scores that can estimate uncertainties in the predicted structures, but it remains unclear to what extent these scores are related to the intrinsic conformational dynamics of proteins. Here, we compare AlphaFold2 prediction scores with explicit large-scale molecular dynamics simulations of 28 one- and two-domain proteins with varying degrees of flexibility. We demonstrate a strong correlation between the statistical prediction scores and the explicit motion derived from extensive atomistic molecular dynamics simulations and further derive an elastic network model based on the statistical scores of AlphFold2 (AF-ENM), which we benchmark in combination with coarse-grained molecular dynamics simulations. We show that our AF-ENM method reproduces the global protein dynamics with improved accuracy, providing a powerful way to derive effective molecular dynamics using neural network-based structure prediction models.
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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.