Recent Development of Machine Learning Methods in Microbial Phosphorylation Sites

Abstract

A variety of protein post-translational modifications has been identified that control many cellular functions. Phosphorylation studies in mycobacterial organisms have shown critical importance in diverse biological processes, such as intercellular communication and cell division. Recent technical advances in high-precision mass spectrometry have determined a large number of microbial phosphorylated proteins and phosphorylation sites throughout the proteome analysis. Identification of phosphorylated proteins with specific modified residues through experimentation is often laborintensive, costly and time-consuming. All these limitations could be overcome through the application of machine learning (ML) approaches. However, only a limited number of computational phosphorylation site prediction tools have been developed so far. This work aims to present a complete survey of the existing ML-predictors for microbial phosphorylation. We cover a variety of important aspects for developing a successful predictor, including operating ML algorithms, feature selection methods, window size, and software utility. Initially, we review the currently available phosphorylation site databases of the microbiome, the state-of-the-art ML approaches, working principles, and their performances. Lastly, we discuss the limitations and future directions of the computational ML methods for the prediction of phosphorylation.

An Improved Computational Prediction Model for Lysine Succinylation Sites Mapping on Homo sapiens by Fusing Three Sequence Encoding Schemes with the Random Forest Classifier

“…The prediction performance not only depends on the estimation of the model parameters but also depends on the selection of tuning parameters like CD-HIT (Cluster Database at High Identity with Tolerance) threshold [ 20 - 23 ], window size of protein sequence, ratio of positive and negative windows, encoding scheme, features and classifier. The appropriate value of CD-HIT threshold (CHT), window size (WS) and ratio of positive and negative windows depends on the dataset [ 15 - 18 , 24 - 26 ].…”