Single-sequence-based prediction of protein secondary structures and solvent accessibility by deep whole-sequence learning
© 2018 Wiley Periodicals, Inc.
| Veröffentlicht in: | Journal of computational chemistry. - 1984. - 39(2018), 26 vom: 05. Okt., Seite 2210-2216 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2018
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| Zugriff auf das übergeordnete Werk: | Journal of computational chemistry |
| Schlagworte: | Journal Article Research Support, Non-U.S. Gov't backbone angles contact prediction protein structure prediction secondary structure prediction solvent accessibility prediction Proteins Solvents |
| Zusammenfassung: | © 2018 Wiley Periodicals, Inc. Predicting protein structure from sequence alone is challenging. Thus, the majority of methods for protein structure prediction rely on evolutionary information from multiple sequence alignments. In previous work we showed that Long Short-Term Bidirectional Recurrent Neural Networks (LSTM-BRNNs) improved over regular neural networks by better capturing intra-sequence dependencies. Here we show a single-sequence-based prediction method employing LSTM-BRNNs (SPIDER3-Single), that consistently achieves Q3 accuracy of 72.5%, and correlation coefficient of 0.67 between predicted and actual solvent accessible surface area. Moreover, it yields reasonably accurate prediction of eight-state secondary structure, main-chain angles (backbone ϕ and ψ torsion angles and C α-atom-based θ and τ angles), half-sphere exposure, and contact number. The method is more accurate than the corresponding evolutionary-based method for proteins with few sequence homologs, and computationally efficient for large-scale screening of protein-structural properties. It is available as an option in the SPIDER3 server, and a standalone version for download, at http://sparks-lab.org. © 2018 Wiley Periodicals, Inc |
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| Beschreibung: | Date Completed 18.09.2019 Date Revised 18.09.2019 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1096-987X |
| DOI: | 10.1002/jcc.25534 |