ArticleInternational journal of molecular sciences2014
Prediction of protein S-nitrosylation sites based on adapted normal distribution bi-profile Bayes and Chou's pseudo amino acid composition.
Article in International journal of molecular sciences, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Empirical Comparison and Analysis of Artificial Intelligence-Based Methods for Identifying Phosphorylation Sites of SARS-CoV-2 Infection.International journal of molecular sciences · 2024Review
- pCysMod: Prediction of Multiple Cysteine Modifications Based on Deep Learning Framework.Frontiers in cell and developmental biology · 2021Article
- Recent Advances in Predicting Protein S-Nitrosylation Sites.BioMed research international · 2021Review
- Some illuminating remarks on molecular genetics and genomics as well as drug development.Molecular genetics and genomics : MGG · 2020Review
- iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical MomentsCurrent genomics · 2019Article
- iMethylK_pseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAACCurrent genomics · 2019Article
- Identification of S-nitrosylation sites based on multiple features combination.Scientific reports · 2019Article
- iHyd-PseAAC (EPSV): Identifying Hydroxylation Sites in Proteins by Extracting Enhanced Position and Sequence Variant FeatureCurrent genomics · 2019Article
- RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou's five-step rule.Database : the journal of biological databases and curation · 2019Article
- Detecting Succinylation sites from protein sequences using ensemble support vector machine.BMC bioinformatics · 2018Article
- 70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features.BMC systems biology · 2018Article
- Computational Structural Biology ofFrontiers in oncology · 2018Review
- Features of reactive cysteines discovered through computation: from kinase inhibition to enrichment around protein degrons.Scientific reports · 2017Article
- Prediction of reversible disulfide based on features from local structural signatures.BMC genomics · 2017Article
- Pse-Analysis: a python package for DNA/RNA and protein/ peptide sequence analysis based on pseudo components and kernel methods.Oncotarget · 2017Article
- Genome-Wide Prediction of DNA Methylation Using DNA Composition and Sequence Complexity in Human.International journal of molecular sciences · 2017Article
- Exploring Mouse Protein Function via Multiple Approaches.PloS one · 2016Article
- Protein remote homology detection by combining Chou's distance-pair pseudo amino acid composition and principal component analysis.Molecular genetics and genomics : MGG · 2015Article
- Sequence-based identification of recombination spots using pseudo nucleic acid representation and recursive feature extraction by linear kernel SVM.BMC bioinformatics · 2014Article
- iNitro-Tyr: prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition.PloS one · 2014Article
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3 authors.
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Abstract
Protein S-nitrosylation is a reversible post-translational modification by covalent modification on the thiol group of cysteine residues by nitric oxide. Growing evidence shows that protein S-nitrosylation plays an important role in normal cellular function as well as in various pathophysiologic conditions. Because of the inherent chemical instability of the S-NO bond and the low abundance of endogenous S-nitrosylated proteins, the unambiguous identification of S-nitrosylation sites by commonly used proteomic approaches remains challenging. Therefore, computational prediction of S-nitrosylation sites has been considered as a powerful auxiliary tool. In this work, we mainly adopted an adapted normal distribution bi-profile Bayes (ANBPB) feature extraction model to characterize the distinction of position-specific amino acids in 784 S-nitrosylated and 1568 non-S-nitrosylated peptide sequences. We developed a support vector machine prediction model, iSNO-ANBPB, by incorporating ANBPB with the Chou's pseudo amino acid composition. In jackknife cross-validation experiments, iSNO-ANBPB yielded an accuracy of 65.39% and a Matthew's correlation coefficient (MCC) of 0.3014. When tested on an independent dataset, iSNO-ANBPB achieved an accuracy of 63.41% and a MCC of 0.2984, which are much higher than the values achieved by the existing predictors SNOSite, iSNO-PseAAC, the Li et al. algorithm, and iSNO-AAPair. On another training dataset, iSNO-ANBPB also outperformed GPS-SNO and iSNO-PseAAC in the 10-fold crossvalidation test.
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