Prediction of serine phosphorylation sites mapping on Schizosaccharomyces Pombe by fusing three encoding schemes with the random forest classifier.

ABSTRACT: Serine phosphorylation is one type of protein post-translational modifications (PTMs), which plays an essential role in various cellular processes and disease pathogenesis. Numerous methods are used for the prediction of phosphorylation sites. However, the traditional wet-lab based experimental approaches are time-consuming, laborious, and expensive. In this work, a computational predictor was proposed to predict serine phosphorylation sites mapping on Schizosaccharomyces pombe (SP) by the fusion of three encoding schemes namely k-spaced amino acid pair composition (CKSAAP), binary and amino acid composition (AAC) with the random forest (RF) classifier. So far, the proposed method is firstly developed to predict serine phosphorylation sites for SP. Both the training and independent test pe

SUBMITTER: Tasmia SA 

PROVIDER: S-EPMC8850546 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature