Sequence-based prediction of protein-peptide binding sites using support vector machine
© 2016 Wiley Periodicals, Inc.
| Veröffentlicht in: | Journal of computational chemistry. - 1984. - 37(2016), 13 vom: 15. Mai, Seite 1223-9 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2016
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| Zugriff auf das übergeordnete Werk: | Journal of computational chemistry |
| Schlagworte: | Journal Article Research Support, Non-U.S. Gov't binding site features machine learning prediction protein-peptide sequence-based support vector machine Peptides |
| Zusammenfassung: | © 2016 Wiley Periodicals, Inc. Protein-peptide interactions are essential for all cellular processes including DNA repair, replication, gene-expression, and metabolism. As most protein-peptide interactions are uncharacterized, it is cost effective to investigate them computationally as the first step. All existing approaches for predicting protein-peptide binding sites, however, are based on protein structures despite the fact that the structures for most proteins are not yet solved. This article proposes the first machine-learning method called SPRINT to make Sequence-based prediction of Protein-peptide Residue-level Interactions. SPRINT yields a robust and consistent performance for 10-fold cross validations and independent test. The most important feature is evolution-generated sequence profiles. For the test set (1056 binding and non-binding residues), it yields a Matthews' Correlation Coefficient of 0.326 with a sensitivity of 64% and a specificity of 68%. This sequence-based technique shows comparable or more accurate than structure-based methods for peptide-binding site prediction. SPRINT is available as an online server at: http://sparks-lab.org/. © 2016 Wiley Periodicals, Inc |
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| Beschreibung: | Date Completed 20.08.2018 Date Revised 20.08.2018 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1096-987X |
| DOI: | 10.1002/jcc.24314 |