ArticleBMC bioinformatics2018
Deep clustering of protein folding simulations.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed, 90 citations in OpenAlex.
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- Mechanistic insights into the association and activation of the SARS-CoV-2 2'-O-Methyltransferase (NSP16).bioRxiv : the preprint server for biology · 2026Article
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- Evolutionary Dynamics and Functional Differences in Clinically Relevant Pen β-Lactamases fromJournal of chemical information and modeling · 2025Article
- Unsupervised Learning of Progress Coordinates during Weighted Ensemble Simulations: Application to NTL9 Protein Folding.Journal of chemical theory and computation · 2025Article
- A beginner's approach to deep learning applied to VS and MD techniques.Journal of cheminformatics · 2025Review
- Generating Protein Structures for Pathway Discovery Using Deep Learning.Journal of chemical theory and computation · 2024Article
- A Stochastic Landscape Approach for Protein Folding State Classification.Journal of chemical theory and computation · 2024Article
- Deciphering the Coevolutionary Dynamics of L2 β-Lactamases via Deep Learning.Journal of chemical information and modeling · 2024Article
- Deep learning workflow for the inverse design of molecules with specific optoelectronic properties.Scientific reports · 2023Article
- 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
- A Study of a Protein-Folding Machine: Transient Rotation of the Polypeptide Backbone Facilitates Rapid Folding of Protein Domains in All-Atom Molecular Dynamics Simulations.International journal of molecular sciences · 2023Article
- Adaptive language model training for molecular design.Journal of cheminformatics · 2023Article
- Gating interactions steer loop conformational changes in the active site of the L1 metallo-β-lactamase.eLife · 2023Article
- #COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol.The international journal of high performance computing applications · 2023Article
- Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action.The international journal of high performance computing applications · 2022Article
- AlphaFold, Artificial Intelligence (AI), and Allostery.The journal of physical chemistry. B · 2022Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
backgroundWe examine the problem of clustering biomolecular simulations using deep learning techniques. Since biomolecular simulation datasets are inherently high dimensional, it is often necessary to build low dimensional representations that can be used to extract quantitative insights into the atomistic mechanisms that underlie complex biological processes.
resultsWe use a convolutional variational autoencoder (CVAE) to learn low dimensional, biophysically relevant latent features from long time-scale protein folding simulations in an unsupervised manner. We demonstrate our approach on three model protein folding systems, namely Fs-peptide (14 μs aggregate sampling), villin head piece (single trajectory of 125 μs) and β- β- α (BBA) protein (223 + 102 μs sampling across two independent trajectories). In these systems, we show that the CVAE latent features learned correspond to distinct conformational substates along the protein folding pathways. The CVAE model predicts, on average, nearly 89% of all contacts within the folding trajectories correctly, while being able to extract folded, unfolded and potentially misfolded states in an unsupervised manner. Further, the CVAE model can be used to learn latent features of protein folding that can be applied to other independent trajectories, making it particularly attractive for identifying intrinsic features that correspond to conformational substates that share similar structural features.
conclusionsTogether, we show that the CVAE model can quantitatively describe complex biophysical processes such as protein folding.
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Registered trials
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.