pCysMod: Prediction of Multiple Cysteine Modifications Based on Deep Learning Framework

Shihua Li; Kai Yu; Guandi Wu; Qingfeng Zhang; Panqin Wang; Jian Zheng; Ze-Xian Liu; Jichao Wang; Xinjiao Gao; Han Cheng

doi:10.3389/fcell.2021.617366

pCysMod: Prediction of Multiple Cysteine Modifications Based on Deep Learning Framework

Front Cell Dev Biol. 2021 Feb 23:9:617366. doi: 10.3389/fcell.2021.617366. eCollection 2021.

Authors

Shihua Li^{1

2}, Kai Yu¹, Guandi Wu¹, Qingfeng Zhang¹, Panqin Wang², Jian Zheng¹, Ze-Xian Liu¹, Jichao Wang³, Xinjiao Gao⁴, Han Cheng²

Affiliations

¹ State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
² School of Life Sciences, Zhengzhou University, Zhengzhou, China.
³ CAS Key Lab of Biobased Materials, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China.
⁴ MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, China.

Abstract

Thiol groups on cysteines can undergo multiple post-translational modifications (PTMs), acting as a molecular switch to maintain redox homeostasis and regulating a series of cell signaling transductions. Identification of sophistical protein cysteine modifications is crucial for dissecting its underlying regulatory mechanism. Instead of a time-consuming and labor-intensive experimental method, various computational methods have attracted intense research interest due to their convenience and low cost. Here, we developed the first comprehensive deep learning based tool pCysMod for multiple protein cysteine modification prediction, including S-nitrosylation, S-palmitoylation, S-sulfenylation, S-sulfhydration, and S-sulfinylation. Experimentally verified cysteine sites curated from literature and sites collected by other databases and predicting tools were integrated as benchmark dataset. Several protein sequence features were extracted and united into a deep learning model, and the hyperparameters were optimized by particle swarm optimization algorithms. Cross-validations indicated our model showed excellent robustness and outperformed existing tools, which was able to achieve an average AUC of 0.793, 0.807, 0.796, 0.793, and 0.876 for S-nitrosylation, S-palmitoylation, S-sulfenylation, S-sulfhydration, and S-sulfinylation, demonstrating pCysMod was stable and suitable for protein cysteine modification prediction. Besides, we constructed a comprehensive protein cysteine modification prediction web server based on this model to benefit the researches finding the potential modification sites of their interested proteins, which could be accessed at http://pcysmod.omicsbio.info. This work will undoubtedly greatly promote the study of protein cysteine modification and contribute to clarifying the biological regulation mechanisms of cysteine modification within and among the cells.

Keywords: deep learning; feature extraction; post-translational modifications; prediction; protein cysteine modifications.