Article

ESMDynamic: Fast and Accurate Prediction of Protein Dynamic Contact Maps from Single Sequences

2025-08-24

Abstract excerpt

Understanding conformational dynamics is essential for elucidating protein function, yet most deep learning models in structural biology predict only static structures. Here, we present ESMDynamic, a deep learning model that predicts residue–residue contact dynamics directly from protein sequence. Built on the ESMFold architecture and trained on conformational variability from experimental structure ensembles and...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: cf14b311-0d9e-5de2-b8e8-408e6dd2c911
DOI: 10.1101/2025.08.20.671365

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

ESMDynamic: Fast and Accurate Prediction of Protein Dynamic Contact Maps from Single SequencesDOI 10.1101/2025.08.20.671365Read

HyRes: Accurate Physics-Based Simulation of Dynamic Protein Structures and Interactions in Complex Environments at ScaleSemantic proximity Read

Atomic resolution ensembles of intrinsically disordered proteins with AlphafoldSemantic proximity Read

Integrating structural homology with deep learning to achieve highly accurate protein-protein interface prediction for the human interactomeSemantic proximity Read

Protein language models trained on biophysical dynamics inform mutation effects.Semantic proximity Read

AF-CALVADOS: AlphaFold-guided simulations of multi-domain proteins at the proteome levelSemantic proximity Read

Decoding conformational heterogeneity across disordered proteomesSemantic proximity Read

ProteinFlux: accurate, rapid and scalable generative prediction of protein dynamics driven by post-translational modificationsSemantic proximity Read

ETLD: an encoder-transformation layer-decoder architecture for protein contact and mutation effects prediction.Semantic proximity Read

PLANET-MD: Ultra-fast Proteome-scale Prediction of Allosteric Networks in ProteinsSemantic proximity Read

Folding membrane proteins by deep transfer learningSemantic proximity Read

Approximating conformational Boltzmann distributions with AlphaFold2 predictionsSemantic proximity Read

Automated identification of functional dynamic contact networks from X-ray crystallography.Semantic proximity Read