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iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition

Author

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  • Yan Xu
  • Jun Ding
  • Ling-Yun Wu
  • Kuo-Chen Chou

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 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.

Suggested Citation

  • Yan Xu & Jun Ding & Ling-Yun Wu & Kuo-Chen Chou, 2013. "iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-7, February.
    • Handle: RePEc:plo:pone00:0055844
    DOI: 10.1371/journal.pone.0055844
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    References listed on IDEAS

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    7. Wei-Zhong Lin & Jian-An Fang & Xuan Xiao & Kuo-Chen Chou, 2012. "Predicting Secretory Proteins of Malaria Parasite by Incorporating Sequence Evolution Information into Pseudo Amino Acid Composition via Grey System Model," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-7, November.
    8. Wei-Zhong Lin & Jian-An Fang & Xuan Xiao & Kuo-Chen Chou, 2011. "iDNA-Prot: Identification of DNA Binding Proteins Using Random Forest with Grey Model," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-7, September.
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    Cited by:

    1. Kuo Chen Chou, 2020. "How the Artificial Intelligence Tool iRNA-PseU is Working in Predicting the RNA Pseudouridine Sites?," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 24(2), pages 18055-18064, January.
    2. Kuo-Chen Chou, 2020. "Showcase to Illustrate How the Web-Server iKcr-PseEns is Working," International Journal of Sciences, Office ijSciences, vol. 9(01), pages 85-95, January.
    3. Bin Liu & Longyun Fang & Fule Liu & Xiaolong Wang & Junjie Chen & Kuo-Chen Chou, 2015. "Identification of Real MicroRNA Precursors with a Pseudo Structure Status Composition Approach," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-20, March.
    4. Kuo-Chen Chou, 2020. "The pLoc_bal-mGneg Predictor is a Powerful Web-Server for Identifying the Subcellular Localization of Gram-Negative Bacterial Proteins based on their Sequences Information Alone," International Journal of Sciences, Office ijSciences, vol. 9(01), pages 27-34, January.
    5. Wenzheng Bao & Bin Yang & Rong Bao & Yuehui Chen, 2019. "LipoFNT: Lipoylation Sites Identification with Flexible Neural Tree," Complexity, Hindawi, vol. 2019, pages 1-9, July.
    6. Abdollah Dehzangi & Yosvany López & Sunil Pranit Lal & Ghazaleh Taherzadeh & Abdul Sattar & Tatsuhiko Tsunoda & Alok Sharma, 2018. "Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile bigrams," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-16, February.
    7. Bandana Kumari & Ravindra Kumar & Manish Kumar, 2014. "PalmPred: An SVM Based Palmitoylation Prediction Method Using Sequence Profile Information," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-10, February.

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