DeepPhos: Prediction of Protein Phosphorylation Sites with Deep Learning

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2019 Jan 3

PMID 30601936

Citations 64

Authors

Fenglin Luo

Minghui Wang

Yu Liu

Xing-Ming Zhao

Ao Li

Affiliations

Soon will be listed here.

Abstract

Motivation: Phosphorylation is the most studied post-translational modification, which is crucial for multiple biological processes. Recently, many efforts have been taken to develop computational predictors for phosphorylation site prediction, but most of them are based on feature selection and discriminative classification. Thus, it is useful to develop a novel and highly accurate predictor that can unveil intricate patterns automatically for protein phosphorylation sites.

Results: In this study we present DeepPhos, a novel deep learning architecture for prediction of protein phosphorylation. Unlike multi-layer convolutional neural networks, DeepPhos consists of densely connected convolutional neuron network blocks which can capture multiple representations of sequences to make final phosphorylation prediction by intra block concatenation layers and inter block concatenation layers. DeepPhos can also be used for kinase-specific prediction varying from group, family, subfamily and individual kinase level. The experimental results demonstrated that DeepPhos outperforms competitive predictors in general and kinase-specific phosphorylation site prediction.

Availability And Implementation: The source code of DeepPhos is publicly deposited at https://github.com/USTCHIlab/DeepPhos.

Supplementary Information: Supplementary data are available at Bioinformatics online.

Citing Articles

GraphPhos: Predict Protein-Phosphorylation Sites Based on Graph Neural Networks.

Wang Z, Yang X, Gao S, Liang Y, Shi X Int J Mol Sci. 2025; 26(3).

PMID: 39940709 PMC: 11818044. DOI: 10.3390/ijms26030941.

Artificial Intelligence Transforming Post-Translational Modification Research.

Kim D, Yin T, Zhang T, Im A, Cort J, Rozum J Bioengineering (Basel). 2025; 12(1).

PMID: 39851300 PMC: 11762806. DOI: 10.3390/bioengineering12010026.

MMFuncPhos: A Multi-Modal Learning Framework for Identifying Functional Phosphorylation Sites and Their Regulatory Types.

Xie J, Dong R, Zhu J, Lin H, Wang S, Lai L Adv Sci (Weinh). 2025; 12(9):e2410981.

PMID: 39804866 PMC: 11884596. DOI: 10.1002/advs.202410981.

Benchmarking recent computational tools for DNA-binding protein identification.

Luo X, Chi A, Lin A, Ong T, Wong L, Rahman C Brief Bioinform. 2024; 26(1).

PMID: 39657630 PMC: 11630855. DOI: 10.1093/bib/bbae634.

MFPSP: Identification of fungal species-specific phosphorylation site using offspring competition-based genetic algorithm.

Wang C, Zou Q PLoS Comput Biol. 2024; 20(11):e1012607.

PMID: 39556608 PMC: 11611262. DOI: 10.1371/journal.pcbi.1012607.

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