NTpred: A robust and precise machine learning framework for in-silico identification of Tyrosine nitration sites in protein sequences

Abstract

AbstractPost-translational modifications (PTMs) either enhance a protein’s activity in various sub-cellular processes, or degrade their activity which leads towards failure of intracellular processes. Tyrosine nitration (NT) modification degrades protein’s activity that initiate and propagate various diseases including Neurodegenerative, Cardiovascular, Autoimmune diseases, and Carcinogenesis. Identification of NT modification support development of novel therapies and drug discoveries for associated diseases. Identification of NT modification in biochemical labs is expensive, time consuming, and error-prone. To supplement this process, several computational approaches have been proposed. However these approaches remain fail to precisely identify NT modification, due to the extraction of irrelevant, redundant and less discriminative features from protein sequences. The paper in hand presents NTpred framework competent in extracting comprehensive features from raw protein sequences using four different sequence encoders. To reap the benefits of different encoders, it generates four additional feature spaces by fusing different combinations of individual encodings. Furthermore, it eradicates irrelevant and redundant features from eight different feature spaces through a Recursive Feature Elimination process. Selected features of four individual encodings and four feature fusion vectors are used to train eight different Gradient Boosted Tree classifiers. The probability scores from the trained classifiers are utilized to generate a new probabilistic feature space, that is utilized to train a Logistic Regression classifier. On BD1 benchmark dataset, the proposed framework outperform existing best performing predictor in 5-fold cross validation and independent test evaluation with combined improvement of 13.7% in MCC and 20.1% in AUC. Similarly, on BD2 benchmark dataset, the proposed framework outperform existing best performing predictor with combined improvement of 5.3% in MCC and 1.0% in AUC.