Computational prediction of protein epsilon lysine acetylation sites based on a feature selection method.

In this study, we developed a position-specific method for epsilon lysine acetylation site prediction. Various kinds of features such as position specific scoring matrix (PSSM), amino acid factors (AAF), and disorders were incorporated. A feature selection method based on mRMR (Maximum Relevance Minimum Redundancy) and IFS (Incremental Feature Selection) was employed. Finally, 319 optimal features were selected from total 541 features. Using the 319 optimal features to encode peptides, a predictor was constructed based on dagging. As a result, an accuracy of 69.56% with MCC of 0.2792 was achieved. We analyzed the optimal features, and it suggested some important factors determining the lysine acetylation sites, which provided insights into the mechanism of lysine acetylation sites, providing guidance of experimental validation. PMID: 28292250 [PubMed - as supplied by publisher]
Source: Combinatorial Chemistry and High Throughput Screening - Category: Chemistry Authors: Tags: Comb Chem High Throughput Screen Source Type: research
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