ArticleCommunications chemistry2025
Deep generative modeling of temperature-dependent structural ensembles of proteins.
Article in Communications chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 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
- Discovery of a cryptic aminoacidic triad involved in the temperature adaptation of GH1 enzymes.The FEBS journal · 2026Article
- Learning the reaction coordinate: collective variables from physical intuition to generative models.Digital discovery · 2026Review
- From possibility to precision in macromolecular ensemble prediction.Nature methods · 2026Article
- AI-based methods for simulating, sampling, and predicting protein ensembles.Current opinion in structural biology · 2026Review
- Alphafuser: a parsimonious approach to predicting higher-order protein complexes.Acta crystallographica. Section D, Structural biology · 2026Article
- Beyond Classical Force Fields: Physics-Driven Assessment of the Grappa Machine-Learned Force Field on the FoldBind Dataset.Chemphyschem : a European journal of chemical physics and physical chemistry · 2026Article
- Generation of protein dynamics by machine learning.Current opinion in structural biology · 2025Review
- Uncovering Anti-Melanoma Mechanisms ofInternational journal of molecular sciences · 2025Article
- Predicting the conformational flexibility of antibody and T cell receptor complementarity-determining regions.Nature machine intelligence · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
Deep learning has revolutionized protein structure prediction, but capturing conformational ensembles remains a challenge. Molecular dynamics (MD) simulations can describe biomolecular dynamics, but they are computationally expensive. Alternatively, deep learning models trained on MD can generate structural ensembles at reduced cost. We present aSAM (atomistic structural autoencoder model), a latent diffusion model trained on MD to generate heavy atom protein ensembles. aSAM models atoms in a latent space to accurate sample side chain and backbone torsion angle distributions. Additionally, the aSAMt version generates ensembles conditioned on temperature. Trained on mdCATH, aSAMt captures temperature-dependent ensemble properties and demonstrates generalization beyond training temperatures. By comparing aSAMt ensembles to long MD simulations of fast-folding proteins, we find that high-temperature training enhances the ability of generators to explore energy landscapes. aSAMt can also capture experimentally observed thermal behavior of proteins. This work generalizes deep learning ensemble generation towards the inclusion of environmental conditions.
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Registered trials
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