PredNTS: Improved and robust prediction of nitrotyrosine sites by integrating multiple sequence features

Andi Nur Nilamyani, Firda Nurul Auliah, Mohammad Ali Moni, Watshara Shoombuatong, Md Mehedi Hasan, Hiroyuki Kurata

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


Nitrotyrosine, which is generated by numerous reactive nitrogen species, is a type of protein post-translational modification. Identification of site-specific nitration modification on tyro-sine is a prerequisite to understanding the molecular function of nitrated proteins. Thanks to the progress of machine learning, computational prediction can play a vital role before the biological experimentation. Herein, we developed a computational predictor PredNTS by integrating multiple sequence features including K-mer, composition of k-spaced amino acid pairs (CKSAAP), AAindex, and binary encoding schemes. The important features were selected by the recursive feature elimination approach using a random forest classifier. Finally, we linearly combined the successive random forest (RF) probability scores generated by the different, single encoding-employing RF models. The resultant PredNTS predictor achieved an area under a curve (AUC) of 0.910 using five-fold cross validation. It outperformed the existing predictors on a comprehensive and independent dataset. Furthermore, we investigated several machine learning algorithms to demonstrate the superiority of the employed RF algorithm. The PredNTS is a useful computational resource for the prediction of nitrotyrosine sites. The web-application with the curated datasets of the PredNTS is publicly available.

Original languageEnglish
Article number2704
Pages (from-to)1-11
Number of pages11
JournalInternational Journal of Molecular Sciences
Issue number5
Publication statusPublished - 01 Mar 2021


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