Deep learning based prediction of species-specific protein S-glutathionylation sites |
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| Institution: | 1. Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;2. Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;3. DNA Tumor Virus Section, Laboratory of Viral Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA;1. The First Affiliated Hospital of Southern University of Science and Technology, the Second Clinical Medical College of Jinan University, Shenzhen People''s Hospital, Shenzhen, CN 518020, PR China;2. Department of Nephrology and Blood Purification, the First Affiliated Hospital of Jinan University, Jinan University, Guangzhou 510632, PR China;3. Guangxi Key Laboratory of Metabolic Diseases Research, Affiliated No. 924 Hospital, Southern Medical University, Guilin 541002, Guangxi, PR China;1. Department of Computer Science, School of Systems and Technology, University of Management and Technology, C-II Johar Town, Lahore, Pakistan;2. Dr Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi 75270, Pakistan;3. Faculty of Computing and Information Technology in Rabigh, King Abdul Aziz University, Jeddah 21577, Saudi Arabia;4. Gordon Life Science Institute, Boston, MA 02478, USA;5. Department of Computer Sciences, Abdul Wali Khan University, Mardan, Pakistan |
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| Abstract: | As a widespread and reversible post-translational modification of proteins, S-glutathionylation specifically generates the mixed disulfides between cysteine residues and glutathione, which regulates various biological processes including oxidative stress, nitrosative stress and signal transduction. The identification of proteins and specific sites that undergo S-glutathionylation is crucial for understanding the underlying mechanisms and regulatory effects of S-glutathionylation. Experimental identification of S-glutathionylation sites is laborious and time-consuming, whereas computational predictions are more attractive due to their high speed and convenience. Here, we developed a novel computational framework DeepGSH (http://deepgsh.cancerbio.info/) for species-specific S-glutathionylation sites prediction, based on deep learning and particle swarm optimization algorithms. 5-fold cross validation indicated that DeepGSH was able to achieve an AUC of 0.8393 and 0.8458 for Homo sapiens and Mus musculus. According to critical evaluation and comparison, DeepGSH showed excellent robustness and better performance than existing tools in both species, demonstrating DeepGSH was suitable for S-glutathionylation prediction. The prediction results of DeepGSH might provide guidance for experimental validation of S-glutathionylation sites and helpful information to understand the intrinsic mechanisms. |
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