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.
Highlights
Post-translational modifications (PTMs) occur at specific amino acids extending the chemical repertoire of the 20 standard amino acids, which reversibly coordinate the signaling networks (Mann and Jensen, 2003; Mertins et al, 2013; Strzyz, 2016)
These modifications lead to a cascade of biochemical reactions and regulate various physiological and pathological processes, such as autophagy (Carroll et al, 2018), protein stabilization (Kröncke and Klotz, 2009), redox homeostasis (Fra et al, 2017), and cell signaling (Hourihan et al, 2016), demonstrating a close relationship with many human diseases including cancers, diabetes, and so on
The sequence features were extracted by four methods, including binary encoding profiles (BE), amino acid composition (AAC), position-specific scoring matrix (PSSM), and composition of k-spaced amino acid pairs (CKSAAP) (Figure 1)
Summary
Post-translational modifications (PTMs) occur at specific amino acids extending the chemical repertoire of the 20 standard amino acids, which reversibly coordinate the signaling networks (Mann and Jensen, 2003; Mertins et al, 2013; Strzyz, 2016). The thioesterification reaction happened on lipid including S-prenylation and S-palmitoylation (Roth et al, 2006) These modifications lead to a cascade of biochemical reactions and regulate various physiological and pathological processes, such as autophagy (Carroll et al, 2018), protein stabilization (Kröncke and Klotz, 2009), redox homeostasis (Fra et al, 2017), and cell signaling (Hourihan et al, 2016), demonstrating a close relationship with many human diseases including cancers, diabetes, and so on. To dissect the molecular mechanisms and regulatory roles of cysteine modification, it is urgently needed to precisely parse the potential cysteine modification sites and types
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