Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network
Copyright © 2014 Wiley Periodicals, Inc.
| Veröffentlicht in: | Journal of computational chemistry. - 1984. - 35(2014), 28 vom: 30. Okt., Seite 2040-6 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2014
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| Zugriff auf das übergeordnete Werk: | Journal of computational chemistry |
| Schlagworte: | Journal Article Research Support, Non-U.S. Gov't deep learning fold recognition fragment structure prediction local structure prediction neural network protein structure prediction secondary structure prediction Proteins |
| Zusammenfassung: | Copyright © 2014 Wiley Periodicals, Inc. Because a nearly constant distance between two neighbouring Cα atoms, local backbone structure of proteins can be represented accurately by the angle between C(αi-1)-C(αi)-C(αi+1) (θ) and a dihedral angle rotated about the C(αi)-C(αi+1) bond (τ). θ and τ angles, as the representative of structural properties of three to four amino-acid residues, offer a description of backbone conformations that is complementary to φ and ψ angles (single residue) and secondary structures (>3 residues). Here, we report the first machine-learning technique for sequence-based prediction of θ and τ angles. Predicted angles based on an independent test have a mean absolute error of 9° for θ and 34° for τ with a distribution on the θ-τ plane close to that of native values. The average root-mean-square distance of 10-residue fragment structures constructed from predicted θ and τ angles is only 1.9Å from their corresponding native structures. Predicted θ and τ angles are expected to be complementary to predicted ϕ and ψ angles and secondary structures for using in model validation and template-based as well as template-free structure prediction. The deep neural network learning technique is available as an on-line server called Structural Property prediction with Integrated DEep neuRal network (SPIDER) at http://sparks-lab.org |
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| Beschreibung: | Date Completed 20.05.2015 Date Revised 10.12.2019 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1096-987X |
| DOI: | 10.1002/jcc.23718 |