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iNitro-Tyr: Prediction of Nitrotyrosine Sites in Proteins with General Pseudo Amino Acid Composition

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Listed:
  • Yan Xu
  • Xin Wen
  • Li-Shu Wen
  • Ling-Yun Wu
  • Nai-Yang Deng
  • Kuo-Chen Chou

Abstract

Nitrotyrosine is one of the post-translational modifications (PTMs) in proteins that occurs when their tyrosine residue is nitrated. Compared with healthy people, a remarkably increased level of nitrotyrosine is detected in those suffering from rheumatoid arthritis, septic shock, and coeliac disease. Given an uncharacterized protein sequence that contains many tyrosine residues, which one of them can be nitrated and which one cannot? This is a challenging problem, not only directly related to in-depth understanding the PTM’s mechanism but also to the nitrotyrosine-based drug development. Particularly, with the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop a high throughput tool in this regard. Here, a new predictor called “iNitro-Tyr” was developed by incorporating the position-specific dipeptide propensity into the general pseudo amino acid composition for discriminating the nitrotyrosine sites from non-nitrotyrosine sites in proteins. It was demonstrated via the rigorous jackknife tests that the new predictor not only can yield higher success rate but also is much more stable and less noisy. A web-server for iNitro-Tyr is accessible to the public at http://app.aporc.org/iNitro-Tyr/. For the convenience of most experimental scientists, we have further provided a protocol of step-by-step guide, by which users can easily get their desired results without the need to follow the complicated mathematics that were presented in this paper just for the integrity of its development process. It has not escaped our notice that the approach presented here can be also used to deal with the other PTM sites in proteins.

Suggested Citation

  • Yan Xu & Xin Wen & Li-Shu Wen & Ling-Yun Wu & Nai-Yang Deng & Kuo-Chen Chou, 2014. "iNitro-Tyr: Prediction of Nitrotyrosine Sites in Proteins with General Pseudo Amino Acid Composition," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-10, August.
  • Handle: RePEc:plo:pone00:0105018
    DOI: 10.1371/journal.pone.0105018
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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. 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.
    4. 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.
    5. Sabit Ahmed & Afrida Rahman & Md Al Mehedi Hasan & Md Khaled Ben Islam & Julia Rahman & Shamim Ahmad, 2021. "predPhogly-Site: Predicting phosphoglycerylation sites by incorporating probabilistic sequence-coupling information into PseAAC and addressing data imbalance," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-17, April.

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