ArticleLife (Basel, Switzerland)2021
Multiscale Enhanced Sampling Using Machine Learning.
Article in Life (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- IDPForge: Deep Learning of Proteins with Global and Local Regions of Disorder.bioRxiv : the preprint server for biology · 2026Article
- Simulations of Functional Motions of Super Large Biomolecules with a Mixed-Resolution Model.Journal of chemical theory and computation · 2024Article
- Machine Learning Generation of Dynamic Protein Conformational Ensembles.Molecules (Basel, Switzerland) · 2023Review
- Machine Learning-Driven Multiscale Modeling: Bridging the Scales with a Next-Generation Simulation Infrastructure.Journal of chemical theory and computation · 2023Article
- Learning to evolve structural ensembles of unfolded and disordered proteins using experimental solution data.The Journal of chemical physics · 2023Article
- Enhancing Conformational Sampling for Intrinsically Disordered and Ordered Proteins by Variational Autoencoder.International journal of molecular sciences · 2023Article
- Artificial intelligence guided conformational mining of intrinsically disordered proteins.Communications biology · 2022Article
- From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output.Frontiers in pharmacology · 2022Review
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1 author.
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Abstract
Multiscale enhanced sampling (MSES) allows for an enhanced sampling of all-atom protein structures by coupling with the accelerated dynamics of the associated coarse-grained (CG) model. In this paper, we propose an MSES extension to replace the CG model with the dynamics on the reduced subspace generated by a machine learning approach, the variational autoencoder (VAE). The molecular dynamic (MD) trajectories of the ribose-binding protein (RBP) in both the closed and open forms were used as the input by extracting the inter-residue distances as the structural features in order to train the VAE model, allowing the encoded latent layer to characterize the difference in the structural dynamics of the closed and open forms. The interpolated data characterizing the RBP structural change in between the closed and open forms were thus efficiently generated in the low-dimensional latent space of the VAE, which was then decoded into the time-series data of the inter-residue distances and was useful for driving the structural sampling at an atomistic resolution via the MSES scheme. The free energy surfaces on the latent space demonstrated the refinement of the generated data that had a single basin into the simulated data containing two closed and open basins, thus illustrating the usefulness of the MD simulation together with the molecular mechanics force field in recovering the correct structural ensemble.
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