ArticlePLoS computational biology2024
Transferable deep generative modeling of intrinsically disordered protein conformations.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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27 citing papers in PubMed.
- Artificial Intelligence and Protein Design: A retrospective study on 20-year emerging trends and core research areas from bibliometric perspectives.Probiotics and antimicrobial proteins · 2026Article
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- HyRes: Accurate Physics-Based Simulation of Dynamic Protein Structures and Interactions in Complex Environments at Scale.bioRxiv : the preprint server for biology · 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
- AI-based methods for simulating, sampling, and predicting protein ensembles.Current opinion in structural biology · 2026Review
- From possibility to precision in macromolecular ensemble prediction.Nature methods · 2026Article
- In Search of Shape in the Unshaped: Constructing Ensembles of Intrinsically Disordered Proteins.Biophysics reviews · 2026Article
- Article
- Toward a unified framework for determining conformational ensembles of disordered proteins.Nature methods · 2026Review
- IDPForge: Deep Learning of Proteins with Global and Local Regions of Disorder.bioRxiv : the preprint server for biology · 2026Article
- Accurate conformational ensembles of intrinsically disordered proteins using reweighting based on NMR chemical shifts.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Atomic resolution ensembles of intrinsically disordered proteins with Alphafold.Nature communications · 2026Article
- Resolving Conformational Heterogeneity in Intrinsically Disordered Proteins via Experimentally Guided Multi-Replica Simulations.bioRxiv : the preprint server for biology · 2026Article
- Decoding intrinsically disordered regions in biomolecular condensates.Fundamental research · 2026Review
- Characterizing Structural and Kinetic Ensembles of Intrinsically Disordered Proteins Using Writhe.Journal of chemical theory and computation · 2025Article
- Deep generative modeling of temperature-dependent structural ensembles of proteins.Communications chemistry · 2025Article
- Sequence-Dependent Conformational Landscapes of Intrinsically Disordered Proteins Reveal Asymmetric Chain Compaction.Journal of chemical theory and computation · 2025Article
- Determining accurate conformational ensembles of intrinsically disordered proteins at atomic resolution.Nature communications · 2025Article
- A deep learning method for predicting interactions for intrinsically disordered regions of proteins.bioRxiv : the preprint server for biology · 2025Article
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Abstract
Intrinsically disordered proteins have dynamic structures through which they play key biological roles. The elucidation of their conformational ensembles is a challenging problem requiring an integrated use of computational and experimental methods. Molecular simulations are a valuable computational strategy for constructing structural ensembles of disordered proteins but are highly resource-intensive. Recently, machine learning approaches based on deep generative models that learn from simulation data have emerged as an efficient alternative for generating structural ensembles. However, such methods currently suffer from limited transferability when modeling sequences and conformations absent in the training data. Here, we develop a novel generative model that achieves high levels of transferability for intrinsically disordered protein ensembles. The approach, named idpSAM, is a latent diffusion model based on transformer neural networks. It combines an autoencoder to learn a representation of protein geometry and a diffusion model to sample novel conformations in the encoded space. IdpSAM was trained on a large dataset of simulations of disordered protein regions performed with the ABSINTH implicit solvent model. Thanks to the expressiveness of its neural networks and its training stability, idpSAM faithfully captures 3D structural ensembles of test sequences with no similarity in the training set. Our study also demonstrates the potential for generating full conformational ensembles from datasets with limited sampling and underscores the importance of training set size for generalization. We believe that idpSAM represents a significant progress in transferable protein ensemble modeling through machine learning.
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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.