ArticleThe Journal of chemical physics2023
Learning to evolve structural ensembles of unfolded and disordered proteins using experimental solution data.
Article in The Journal of chemical physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- 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
- Characterization of flexible RNA binding by tandem RNA recognition motifs through integrative ensemble modelling.Nucleic acids research · 2026Article
- Deep generative modeling of temperature-dependent structural ensembles of proteins.Communications chemistry · 2025Article
- Targeting protein disorder: the next hurdle in drug discovery.Nature reviews. Drug discovery · 2025Review
- Biomolecular Condensates as Emerging Biomaterials: Functional Mechanisms and Advances in Computational and Experimental Approaches.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Quality Evaluation Based Simulation Selection (QEBSS) for analysis of conformational ensembles and dynamics of multidomain proteins.Communications chemistry · 2025Article
- Biological insights from integrative modeling of intrinsically disordered protein systems.Current opinion in structural biology · 2025Review
- Generation of protein dynamics by machine learning.Current opinion in structural biology · 2025Review
- Modeling Boltzmann-weighted structural ensembles of proteins using artificial intelligence-based methods.Current opinion in structural biology · 2025Review
- Frontiers in integrative structural modeling of macromolecular assemblies.QRB discovery · 2025Review
- Use of AI-methods over MD simulations in the sampling of conformational ensembles in IDPs.Frontiers in molecular biosciences · 2025Review
- UCBShift 2.0: Bridging the Gap from Backbone to Side Chain Protein Chemical Shift Prediction for Protein Structures.Journal of the American Chemical Society · 2024Article
- Computational Methods to Investigate Intrinsically Disordered Proteins and their Complexes.ArXiv · 2024Article
- Unlocking the unfolded structure of ubiquitin: Combining time-resolved x-ray solution scattering and molecular dynamics to generate unfolded ensembles.The Journal of chemical physics · 2024Article
- A curated rotamer library for common post-translational modifications of proteins.Bioinformatics (Oxford, England) · 2024Article
- Article
- Transferable deep generative modeling of intrinsically disordered protein conformations.PLoS computational biology · 2024Article
- The molecular basis for cellular function of intrinsically disordered protein regions.Nature reviews. Molecular cell biology · 2024Review
- Transferable deep generative modeling of intrinsically disordered protein conformations.bioRxiv : the preprint server for biology · 2024Article
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8 authors.
Funding
Abstract
The structural characterization of proteins with a disorder requires a computational approach backed by experiments to model their diverse and dynamic structural ensembles. The selection of conformational ensembles consistent with solution experiments of disordered proteins highly depends on the initial pool of conformers, with currently available tools limited by conformational sampling. We have developed a Generative Recurrent Neural Network (GRNN) that uses supervised learning to bias the probability distributions of torsions to take advantage of experimental data types such as nuclear magnetic resonance J-couplings, nuclear Overhauser effects, and paramagnetic resonance enhancements. We show that updating the generative model parameters according to the reward feedback on the basis of the agreement between experimental data and probabilistic selection of torsions from learned distributions provides an alternative to existing approaches that simply reweight conformers of a static structural pool for disordered proteins. Instead, the biased GRNN, DynamICE, learns to physically change the conformations of the underlying pool of the disordered protein to those that better agree with experiments.
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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.