Computational identification of microbial phosphorylation sites by the enhanced characteristics of sequence information.

ABSTRACT: Protein phosphorylation on serine (S) and threonine (T) has emerged as a key device in the control of many biological processes. Recently phosphorylation in microbial organisms has attracted much attention for its critical roles in various cellular processes such as cell growth and cell division. Here a novel machine learning predictor, MPSite (Microbial Phosphorylation Site predictor), was developed to identify microbial phosphorylation sites using the enhanced characteristics of sequence features. The final feature vectors optimized via a Wilcoxon rank sum test. A random forest classifier was then trained using the optimum features to build the predictor. Benchmarking investigation using the 5-fold cross-validation and independent datasets test showed that the MPSite is able to achieve r

SUBMITTER: Hasan MM 

PROVIDER: S-EPMC6547684 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature