Journal
JOURNAL OF COMPUTATIONAL BIOLOGY
Volume 24, Issue 10, Pages 1050-1059Publisher
MARY ANN LIEBERT, INC
DOI: 10.1089/cmb.2016.0206
Keywords
binary encoding; gradient boosting machine; lysine succinylation
Categories
Funding
- National Natural Science Foundation of China [31570160]
- Nature Science Foundation [2013225086]
- Science of Public Research Foundation from the Scientific and Technological Department of Liaoning Province [2014001015]
- Innovation Team Project from the Education Department of Liaoning Province [LT2015011]
- General Research Project Foundation from the Education Department of Liaoning Province [L2014001]
- Large-scale Equipment Shared Services Project from the Science and Technology Bureau of Shenyang [F15165400]
- Applied Basic Research Project from the Science and Technology Bureau of Shenyang [F16205151]
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Lysine succinylation is an extremely important protein post-translational modification that plays a fundamental role in regulating various biological reactions, and dysfunction of this process is associated with a number of diseases. Thus, determining which Lys residues in an uncharacterized protein sequence are succinylated underpins both basic research and drug development endeavors. To solve this problem, we have developed a predictor called pSuc-PseRat. The features of the pSuc-PseRat predictor are derived from two aspects: (1) the binary encoding from succinylated sites and non-succinylated sites; (2) the sequence-coupling effects between succinylated sites and non-succinylated sites. Eleven gradient boosting machine classifiers were trained with these features to build the predictor. The pSuc-PseRat predictor achieved an average ACU (area under the receiver operating characteristic curve) score of 0.805 in the fivefold cross-validation set and performed better than existing predictors on two comprehensive independent test sets. A freely available web server has been developed for pSuc-PseRat.
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