ArticleCommunications biology2022
Artificial intelligence guided conformational mining of intrinsically disordered proteins.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- DreamFold: A World Model to efficiently generate protein folding pathways in the latent space.Protein science : a publication of the Protein Society · 2026Article
- Neurofilament Light Disordered Tail Mutations Reshape Its Self-Assembled Network Structure.Journal of the American Chemical Society · 2026Article
- In Search of Shape in the Unshaped: Constructing Ensembles of Intrinsically Disordered Proteins.Biophysics reviews · 2026Article
- Hierarchical multi-timescale structural dynamics of the disordered N-terminal of p53.Nature communications · 2026Article
- IDPForge: Deep Learning of Proteins with Global and Local Regions of Disorder.bioRxiv : the preprint server for biology · 2026Article
- Enhanced Sampling and Free Energy Calculations in Protein Simulations.Advances in experimental medicine and biology · 2026Review
- 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
- Dissecting the Molecular Determinants of α-synuclein Phase Separation and Condensate Aging: The Pivotal Role of β-Sheet-Rich Motifs.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 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
- Generation of protein dynamics by machine learning.Current opinion in structural biology · 2025Review
- Atomistic molecular dynamics simulations of intrinsically disordered proteins.Current opinion in structural biology · 2025Review
- Integrating chemical artificial intelligence and cognitive computing for predictive analysis of biological pathways: a case for intrinsically disordered proteins.Biophysical reviews · 2025Review
- Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning.Journal of chemical information and modeling · 2025Article
- Use of AI-methods over MD simulations in the sampling of conformational ensembles in IDPs.Frontiers in molecular biosciences · 2025Review
- Protein disorder - function paradigm: Putative role in inflammation.Bioinformation · 2025Article
- Physics-Based Machine Learning Trains Hamiltonians and Decodes the Sequence-Conformation Relation in the Disordered Proteome.Journal of chemical theory and computation · 2024Article
- Impact of Phosphorylation on the Physiological Form of Human alpha-Synuclein in Aqueous Solution.Journal of chemical information and modeling · 2024Article
- Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning.Research square · 2024Article
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Authors and funding
4 authors.
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Abstract
Artificial intelligence recently achieved the breakthrough of predicting the three-dimensional structures of proteins. The next frontier is presented by intrinsically disordered proteins (IDPs), which, representing 30% to 50% of proteomes, readily access vast conformational space. Molecular dynamics (MD) simulations are promising in sampling IDP conformations, but only at extremely high computational cost. Here, we developed generative autoencoders that learn from short MD simulations and generate full conformational ensembles. An encoder represents IDP conformations as vectors in a reduced-dimensional latent space. The mean vector and covariance matrix of the training dataset are calculated to define a multivariate Gaussian distribution, from which vectors are sampled and fed to a decoder to generate new conformations. The ensembles of generated conformations cover those sampled by long MD simulations and are validated by small-angle X-ray scattering profile and NMR chemical shifts. This work illustrates the vast potential of artificial intelligence in conformational mining of IDPs.
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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.