ArticleInternational journal of molecular sciences2014
PSNO: predicting cysteine S-nitrosylation sites by incorporating various sequence-derived features into the general form of Chou's PseAAC.
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 28 papers.
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Who cites it
28 citing papers in PubMed.
- Protein S-Nitrosylation: A Chemical Modification with Ubiquitous Biological Activities.The protein journal · 2024Review
- PPSNO: A Feature-Rich SNO Sites Predictor by Stacking Ensemble Strategy from Protein Sequence-Derived Information.Interdisciplinary sciences, computational life sciences · 2024Article
- Biological Mechanisms ofAntioxidants (Basel, Switzerland) · 2021Review
- Recent Advances in Predicting Protein S-Nitrosylation Sites.BioMed research international · 2021Review
- Recent Development of Machine Learning Methods in Microbial Phosphorylation Sites.Current genomics · 2020Review
- Some illuminating remarks on molecular genetics and genomics as well as drug development.Molecular genetics and genomics : MGG · 2020Review
- iN6-methylat (5-step): identifying DNA NMolecular genetics and genomics : MGG · 2019Article
- iMethylK_pseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAACCurrent genomics · 2019Article
- iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical MomentsCurrent genomics · 2019Article
- Identification of S-nitrosylation sites based on multiple features combination.Scientific reports · 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
- Computational Structural Biology ofFrontiers in oncology · 2018Review
- A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction.BMC bioinformatics · 2017Article
- Glypre: In Silico Prediction of Protein Glycation Sites by Fusing Multiple Features and Support Vector Machine.Molecules (Basel, Switzerland) · 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
- Predicting Protein-Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids.International journal of molecular sciences · 2016Article
- Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributes.BMC bioinformatics · 2016Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
S-nitrosylation (SNO) is one of the most universal reversible post-translational modifications involved in many biological processes. Malfunction or dysregulation of SNO leads to a series of severe diseases, such as developmental abnormalities and various diseases. Therefore, the identification of SNO sites (SNOs) provides insights into disease progression and drug development. In this paper, a new bioinformatics tool, named PSNO, is proposed to identify SNOs from protein sequences. Firstly, we explore various promising sequence-derived discriminative features, including the evolutionary profile, the predicted secondary structure and the physicochemical properties. Secondly, rather than simply combining the features, which may bring about information redundancy and unwanted noise, we use the relative entropy selection and incremental feature selection approach to select the optimal feature subsets. Thirdly, we train our model by the technique of the k-nearest neighbor algorithm. Using both informative features and an elaborate feature selection scheme, our method, PSNO, achieves good prediction performance with a mean Mathews correlation coefficient (MCC) value of about 0.5119 on the training dataset using 10-fold cross-validation. These results indicate that PSNO can be used as a competitive predictor among the state-of-the-art SNOs prediction tools. A web-server, named PSNO, which implements the proposed method, is freely available at http://59.73.198.144:8088/PSNO/.
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