ArticleFrontiers in molecular biosciences2021
Explore Protein Conformational Space With Variational Autoencoder.
Article in Frontiers in molecular biosciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 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
- Inferring dynamic information from protein structures by Gaussian integrals and deep learning.Bioinformatics (Oxford, England) · 2026Article
- Article
- Multiscale conformational sampling of multidomain fusion proteins by a physics informed diffusion model.bioRxiv : the preprint server for biology · 2026Article
- Reinforced molecular dynamics: Physics-infused generative machine learning model simulates protein motion.PNAS nexus · 2026Article
- Asymmetric Dynamics Between the Protomers of the σ2 Receptor Homodimer.Journal of chemical information and modeling · 2025Article
- A review of machine learning methods for imbalanced data challenges in chemistry.Chemical science · 2025Review
- Thermal Adaptation of Cytosolic Malate Dehydrogenase Revealed by Deep Learning and Coevolutionary Analysis.Journal of chemical theory and computation · 2025Article
- A structural biology compatible file format for atomic force microscopy.Nature communications · 2025Article
- IDRdecoder: a machine learning approach for rational drug discovery toward intrinsically disordered regions.Frontiers in bioinformatics · 2025Article
- Advances and Challenges in Milestoning Simulations for Drug-Target Kinetics.Journal of chemical theory and computation · 2024Article
- Protein Ensemble Generation Through Variational Autoencoder Latent Space Sampling.Journal of chemical theory and computation · 2024Article
- Exploring DNA Damage and Repair Mechanisms: A Review with Computational Insights.Biotech (Basel (Switzerland)) · 2024Review
- Molecular dynamics of the human RhD and RhAG blood group proteins.Frontiers in chemistry · 2024Article
- Advances in Computational Approaches for Estimating Passive Permeability in Drug Discovery.Membranes · 2023Review
- Structural characterization of an intrinsically disordered protein complex using integrated small-angle neutron scattering and computing.Protein science : a publication of the Protein Society · 2023Article
- Assessments of Variational Autoencoder in Protein Conformation Exploration.Journal of computational biophysics and chemistry · 2023Article
- Machine Learning Generation of Dynamic Protein Conformational Ensembles.Molecules (Basel, Switzerland) · 2023Review
- From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on "Allosteric Intersection" of Biochemical and Big Data Approaches.International journal of molecular sciences · 2023Review
- Latent generative landscapes as maps of functional diversity in protein sequence space.Nature communications · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
Molecular dynamics (MD) simulations have been actively used in the study of protein structure and function. However, extensive sampling in the protein conformational space requires large computational resources and takes a prohibitive amount of time. In this study, we demonstrated that variational autoencoders (VAEs), a type of deep learning model, can be employed to explore the conformational space of a protein through MD simulations. VAEs are shown to be superior to autoencoders (AEs) through a benchmark study, with low deviation between the training and decoded conformations. Moreover, we show that the learned latent space in the VAE can be used to generate unsampled protein conformations. Additional simulations starting from these generated conformations accelerated the sampling process and explored hidden spaces in the conformational landscape.
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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.