Identification of S-nitrosylation sites based on multiple features combination

Scientific Reports
Taoying LiYan Chen

Abstract

Protein S-nitrosylation (SNO) is a typical reversible, redox-dependent and post-translational modification that involves covalent modification of cysteine residues with nitric oxide (NO) for the thiol group. Numerous experiments have shown that SNO plays a major role in cell function and pathophysiology. In order to rapidly analysis the big sets of data, the computing methods for identifying the SNO sites are being considered as necessary auxiliary tools. In this study, multiple features including Parallel correlation pseudo amino acid composition (PC-PseAAC), Basic kmer1 (kmer1), Basic kmer2 (kmer2), General parallel correlation pseudo amino acid composition (PC-PseAAC_G), Adapted Normal distribution Bi-Profile Bayes (ANBPB), Double Bi-Profile Bayes (DBPB), Bi-Profile Bayes (BPB), Incorporating Amino Acid Pairwise (IAAPair) and Position-specific Tri-Amino Acid Propensity(PSTAAP) were employed to extract the sequence information. To remove information redundancy, information gain (IG) was applied to evaluate the importance of amino acids, which is the information entropy of class after subtracting the conditional entropy for the given amino acid. The prediction performance of the SNO sites was found to be best by using the cros...Continue Reading

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Citations

Sep 20, 2020·Journal of Biomolecular Structure & Dynamics·Zhongwang Zhang, Lidong Wang
Feb 26, 2021·BioMed Research International·Qian ZhaoKe Han

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Methods Mentioned

BETA
feature extraction

Software Mentioned

kmer1
kmer2
iSNO
Basic kmer
kmer
IG
GPS
One
AAPair
in

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