Predicting improved protein conformations with a temporal deep recurrent neural network
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Abstract
Accurate protein structure prediction from amino acid sequence is still an unsolved problem. The most reliable methods centre on template based modelling. However, the accuracy of these models entirely depends on the availability of experimentally resolved homologous template structures. In order to generate more accurate models, extensive physics based molecular dynamics (MD) refinement simulations are performed to sample many different conformations to find improved conformational states. In this study, we propose a deep recurrent network model, called DeepTrajectory, that is able to identify these improved conformational states, with high precision, from a variety of different MD based sampling protocols. The proposed model learns the temporal patterns of features computed from MD trajectory data in order to classify whether each recorded simulation snapshot is an improved quality conformational state, decreased quality conformational state or whether there is no perceivable change in state with respect to the starting conformation. The model was trained and tested on 904 trajectories from 42 different protein systems with a cumulative number of more than 1.7 million snapshots. We show that our model outperforms other state of the art machine-learning algorithms that do not consider temporal dependencies. To our knowledge, DeepTrajectory is the first implementation of a time-dependent deep-learning protocol that is re-trainable and able to adapt to any new MD based sampling procedure, thereby demonstrating how a neural network can be used to learn the latter part of the protein folding funnel.
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- Authors
- Pfeiffenberger, E., & Bates, P. A.
- Affiliations
- The Francis Crick Institute (GB)
- Corresponding author
- Pfeiffenberger, Erik
- Bates, Paul A.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-09-04
- Volume
- 13
- Issue
- 9
- Pages
- e0202652
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC6122789
- OpenAlex
- W2949122381
- Cited by
- 21
- References
- 68
- Citations by year
- 2025: 3 · 2024: 2 · 2023: 1 · 2022: 4 · 2021: 3 · 2020: 4 · 2019: 2 · 2018: 2
- FWCI
- 0.98
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- Cancer Research UK (FC001003)
- Medical Research Council (FC001003)
- Wellcome Trust (FC001003)
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein structure prediction, protein conformational states, molecular dynamics trajectories, deep recurrent neural networks, conformational sampling
- MeSH terms
- Deep Learning, Algorithms, Amino Acid Sequence, Protein Conformation, Software, Neural Networks, Computer, Protein Folding, Sequence Analysis, Protein, Molecular Dynamics Simulation
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