ArticlePloS one2013
iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 119 papers.
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119 citing papers in PubMed.
- Protein S-nitrosylation in the heart: Mechanisms, physiological roles, and pathological remodeling.Redox biology · 2026Review
- Predicting Polymerase Chain Reaction Success: Integrating theCombinatorial chemistry & high throughput screening · 2026Article
- Block sparse Bayes-based fuzzy system for RNA N6-methyladenosine sites prediction.PLoS computational biology · 2025Article
- LMPTMSite: A Platform for PTM Site Prediction in Proteins Leveraging Transformer-Based Protein Language Models.Methods in molecular biology (Clifton, N.J.) · 2025Article
- The evolution of S-nitrosylation detection methodology and the role of protein S-nitrosylation in various cancers.Cancer cell international · 2024Review
- 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
- SNO-DCA: A model for predictingHeliyon · 2024Article
- Investigating the Precise Identification of Citrullination Sites with High- Performance Score Metrics Using a Powerful Computation Predicting Tool.Combinatorial chemistry & high throughput screening · 2024Article
- pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model.BMC bioinformatics · 2023Article
- Thirty years of molecular dynamics simulations on posttranslational modifications of proteins.Physical chemistry chemical physics : PCCP · 2022Review
- iPromoter-Seqvec: identifying promoters using bidirectional long short-term memory and sequence-embedded features.BMC genomics · 2022Article
- Hsp70 in Redox Homeostasis.Cells · 2022Review
- GSNOR facilitates antiviral innate immunity by restricting TBK1 cysteine S-nitrosation.Redox biology · 2021Article
- Biological Mechanisms ofAntioxidants (Basel, Switzerland) · 2021Review
- Evaluating machine learning methodologies for identification of cancer driver genes.Scientific reports · 2021Article
- Nitric oxide is associated with strigolactone and karrikin signal transduction inPlant signaling & behavior · 2021Article
- Protein S-nitrosation differentially modulates tomato responses to infection by hemi-biotrophic oomycetes of Phytophthora spp.Horticulture research · 2021Article
- S-Nitrosation of E3 Ubiquitin Ligase Complex Components Regulates Hormonal Signalings in Arabidopsis.Frontiers in plant science · 2021Article
- Yin and Yang in Post-Translational Modifications of Human D-Amino Acid Oxidase.Frontiers in molecular biosciences · 2021Article
59 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
Posttranslational modifications (PTMs) of proteins are responsible for sensing and transducing signals to regulate various cellular functions and signaling events. S-nitrosylation (SNO) is one of the most important and universal PTMs. With the avalanche of protein sequences generated in the post-genomic age, it is highly desired to develop computational methods for timely identifying the exact SNO sites in proteins because this kind of information is very useful for both basic research and drug development. Here, a new predictor, called iSNO-PseAAC, was developed for identifying the SNO sites in proteins by incorporating the position-specific amino acid propensity (PSAAP) into the general form of pseudo amino acid composition (PseAAC). The predictor was implemented using the conditional random field (CRF) algorithm. As a demonstration, a benchmark dataset was constructed that contains 731 SNO sites and 810 non-SNO sites. To reduce the homology bias, none of these sites were derived from the proteins that had [Formula: see text] pairwise sequence identity to any other. It was observed that the overall cross-validation success rate achieved by iSNO-PseAAC in identifying nitrosylated proteins on an independent dataset was over 90%, indicating that the new predictor is quite promising. Furthermore, a user-friendly web-server for iSNO-PseAAC was established at http://app.aporc.org/iSNO-PseAAC/, by which users can easily obtain the desired results without the need to follow the mathematical equations involved during the process of developing the prediction method. It is anticipated that iSNO-PseAAC may become a useful high throughput tool for identifying the SNO sites, or at the very least play a complementary role to the existing methods in this area.
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