pCysMod: Prediction of Multiple Cysteine Modifications Based on Deep Learning Framework - PubMed (original) (raw)

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

Shihua Li et al. Front Cell Dev Biol. 2021.

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.

Copyright © 2021 Li, Yu, Wu, Zhang, Wang, Zheng, Liu, Wang, Gao and Cheng.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1

FIGURE 1

An overview of the model.

FIGURE 2

FIGURE 2

The characteristic of cysteine modification sites and proteins. (A) The secondary structure. (B) The disorder information of cysteine modification sites. (C) Preference for amino acids around the cysteine modification sites and non-cysteine modification sites.

FIGURE 3

FIGURE 3

The ROC curves and AUCs of 4-, 6-, 8-, and tenfold cross-validations are shown.

FIGURE 4

FIGURE 4

The web server of pCysMod. (A) The prediction page. (B) Potential cysteine modification sites. (C) Secondary structure and surface accessibility.

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