Prediction of S-Sulfenylation Sites Using Statistical Moments Based Features via CHOU’S 5-Step Rule

Post-translation modification (PTM) of cysteine S-sulfenylation sites in protein is important in cellular biology. S-sulfenylation plays a significant role in protein functioning, cell signaling and transcriptional regulation. Cysteine, S-sulfenylation site prediction is crucial in order to interpre...

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Bibliographic Details
Published inInternational journal of peptide research and therapeutics Vol. 26; no. 3; pp. 1291 - 1301
Main Authors Butt, Ahmad Hassan, Khan, Yaser Daanial
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Netherlands 01.09.2020
Springer Nature B.V
Subjects
Animal Anatomy
Biochemistry
Biomedical and Life Sciences
Cysteine
Gene regulation
Histology
Life Sciences
Molecular Medicine
Molecular modelling
Morphology
Pharmaceutical Sciences/Technology
Pharmacology/Toxicology
Polymer Sciences
Post-translation
Statistical prediction
Statistics
Transcription
Hahn moments
Cysteine S-sulfenylation
Chou’s 5-step-rule
Tenfold cross validation
Post-translation modifications
Position relative matrix
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Summary:Post-translation modification (PTM) of cysteine S-sulfenylation sites in protein is important in cellular biology. S-sulfenylation plays a significant role in protein functioning, cell signaling and transcriptional regulation. Cysteine, S-sulfenylation site prediction is crucial in order to interpret the S-sulfenylation molecular mechanisms. In this study, statistical moments based methodology is proposed for cysteine S-sulfenylation site predictions. The system proposed has achieved accuracy far better than current state-of-the-art methods using tenfold cross validations and independent tests. The outcomes from the proposed method revealed that using statistical moments based features could produce more efficient and effective results. For the accessibility of the scientific community, we have developed a GitHub repository for cysteine S-sulfenylation sites prediction system which is freely accessible at https://www.github.com/ahmad-umt/S-Sulfenylation .
ISSN:1573-3149
1573-3904
DOI:10.1007/s10989-019-09931-2