Predicting lysine-malonylation sites of proteins using sequence and predicted structural features
© 2018 Wiley Periodicals, Inc.
| Veröffentlicht in: | Journal of computational chemistry. - 1984. - 39(2018), 22 vom: 15. Aug., Seite 1757-1763 |
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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 lysine-malonylation sites prediction post translational modification support vector machines Bacterial Proteins Malonates Lysine K3Z4F929H6 |
| Zusammenfassung: | © 2018 Wiley Periodicals, Inc. Malonylation is a recently discovered post-translational modification (PTM) in which a malonyl group attaches to a lysine (K) amino acid residue of a protein. In this work, a novel machine learning model, SPRINT-Mal, is developed to predict malonylation sites by employing sequence and predicted structural features. Evolutionary information and physicochemical properties are found to be the two most discriminative features whereas a structural feature called half-sphere exposure provides additional improvement to the prediction performance. SPRINT-Mal trained on mouse data yields robust performance for 10-fold cross validation and independent test set with Area Under the Curve (AUC) values of 0.74 and 0.76 and Matthews' Correlation Coefficient (MCC) of 0.213 and 0.20, respectively. Moreover, SPRINT-Mal achieved comparable performance when testing on H. sapiens proteins without species-specific training but not in bacterium S. erythraea. This suggests similar underlying physicochemical mechanisms between mouse and human but not between mouse and bacterium. SPRINT-Mal is freely available as an online server at: http://sparks-lab.org/server/SPRINT-Mal/. © 2018 Wiley Periodicals, Inc |
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| Beschreibung: | Date Completed 17.09.2019 Date Revised 17.09.2019 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1096-987X |
| DOI: | 10.1002/jcc.25353 |