ArticleNature communications2023
Direct generation of protein conformational ensembles via machine learning.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers.
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Who cites it
91 citing papers in PubMed.
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- The Advantages of AI for Computational Protein Studies and Looking Ahead at the Next Challenges: Single Structures Are Not Enough.Journal of molecular biology · 2026Review
- Embrace Disorder as a Function: Bio-Informed Biomaterials Design Advanced by Intrinsically Disordered Proteins.Biomimetics (Basel, Switzerland) · 2026Article
- Hierarchical discrete representations for coarse-to-fine protein conformation generation.Bioinformatics (Oxford, England) · 2026Article
- AF-CALVADOS: AlphaFold-guided simulations of multi-domain proteins at the proteome level.Protein science : a publication of the Protein Society · 2026Article
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- DeepPath: overcoming data scarcity for protein transition pathway prediction using physics-based deep learning.Chemical science · 2026Article
- The Monomeric Conformational Ensembles of Aβ40 and Aβ42 Encode Their Differential Amyloid Aggregation Propensity.The journal of physical chemistry. B · 2026Article
- Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.BioData mining · 2026Review
- Advancements in single-molecule fluorescence spectroscopy for probing conformations, dynamics, and interactions in disordered protein regions.Current opinion in structural biology · 2026Review
- AI-based methods for simulating, sampling, and predicting protein ensembles.Current opinion in structural biology · 2026Review
- Mechanistic Dissection of Conformational Transition of Bicyclic Peptide via Molecular Modeling and Deep Learning.bioRxiv : the preprint server for biology · 2026Article
- Transient tertiary structure in intrinsically disordered proteins revealed by multithermal enhanced sampling.Nature communications · 2026Article
- Molecular Modeling of the Pathogenetic Mechanisms of Neuropsychiatric Disorders.International journal of molecular sciences · 2026Review
- Toward a unified framework for determining conformational ensembles of disordered proteins.Nature methods · 2026Review
- Fast sampling of protein conformational dynamics.Science advances · 2026Article
- IDPForge: Deep Learning of Proteins with Global and Local Regions of Disorder.bioRxiv : the preprint server for biology · 2026Article
- Assessing the Performance of BioEmu in Understanding Protein Dynamics.International journal of molecular sciences · 2026Article
- Bias in the AlphaFold3 prediction of ligand-induced domain motion in enzymes.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery.Biology · 2026Review
31 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
Dynamics and conformational sampling are essential for linking protein structure to biological function. While challenging to probe experimentally, computer simulations are widely used to describe protein dynamics, but at significant computational costs that continue to limit the systems that can be studied. Here, we demonstrate that machine learning can be trained with simulation data to directly generate physically realistic conformational ensembles of proteins without the need for any sampling and at negligible computational cost. As a proof-of-principle we train a generative adversarial network based on a transformer architecture with self-attention on coarse-grained simulations of intrinsically disordered peptides. The resulting model, idpGAN, can predict sequence-dependent coarse-grained ensembles for sequences that are not present in the training set demonstrating that transferability can be achieved beyond the limited training data. We also retrain idpGAN on atomistic simulation data to show that the approach can be extended in principle to higher-resolution conformational ensemble generation.
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Registered trials
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.