DeepPhoPred: Accurate Deep Learning Model to Predict Microbial Phosphorylation.

Abstract

Phosphorylation is a substantial posttranslational modification of proteins that refers to adding a phosphate group to the amino acid side chain after translation process in the ribosome. It is vital to coordinate cellular functions, such as regulating metabolism, proliferation, apoptosis, subcellular trafficking, and other crucial physiological processes. Phosphorylation prediction in a microbial organism can assist in understanding pathogenesis and host-pathogen interaction, drug and antibody design, and antimicrobial agent development. Experimental methods for predicting phosphorylation sites are costly, slow, and tedious. Hence low-cost and high-speed computational approaches are highly desirable. This paper presents a new deep learning tool called DeepPhoPred for predicting microbial phospho-serine (pS), phospho-threonine (pT), and phospho-tyrosine (pY) sites. DeepPhoPred incorporates a two-headed convolutional neural network architecture with the squeeze and excitation blocks followed by fully connected layers that jointly learn significant features from the peptide's structural and evolutionary information to predict phosphorylation sites. Our empirical results demonstrate that DeepPhoPred significantly outperforms the existing microbial phosphorylation site predictors with its highly efficient deep-learning architecture. DeepPhoPred as a standalone predictor, all its source codes, and our employed datasets are publicly available at https://github.com/faisalahm3d/DeepPhoPred.