Explore Protein Conformational Space With Variational Autoencoder
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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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Deep learning involving variational autoencoders has been shown useful; the learned latent space has been employed to generate unsampled protein conformations, and simulations starting from these conformations accelerated sampling.
“The learned latent space in the variational autoencoders has been employed to generate unsampled protein conformations, and simulations starting from these conformations accelerated the sampling.”
From AlphaFold, Artificial Intelligence (AI), and Allostery (2022) · cites this paper as [193]
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- Authors
- Tian, H., Jiang, X., Trozzi, F., Xiao, S., Larson, E. C., & Tao, P.
- Affiliations
- Southern Methodist University (US)
- Corresponding author
- Tao, Peng
- Venue
- Frontiers in Molecular Biosciences
- Publisher
- Frontiers Media SA
- Published
- 2021-11-12
- Volume
- 8
- Pages
- 781635-781635
- ISSN
- 2296-889X (ISSN-L)
- PMC
- PMC8633506
- OpenAlex
- W3213738679
- Cited by
- 51
- References
- 47
- Citations by year
- 2026: 8 · 2025: 14 · 2024: 9 · 2023: 11 · 2022: 9
- FWCI
- 2.28
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Molecular Biosciences (publishedVersion, cc-by)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute of General Medical Sciences (R15GM122013)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Model Reduction and Neural Networks
- Keywords
- protein conformational space, variational autoencoder, molecular dynamics simulation, conformational sampling, latent space, autoencoder comparison, protein structure
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