Post Translational Modification (PTM) is defined as the alteration of protein sequence upon interaction with different macromolecules after the translation process. Glutarylation is considered one of the most important PTMs, which is associated with a wide range of cellular functioning, including metabolism, translation, and specified separate subcellular localizations. During the past few years, a wide range of computational approaches has been proposed to predict Glutarylation sites. However, despite all the efforts that have been made so far, the prediction performance of the Glutarylation sites has remained limited. One of the main challenges to tackle this problem is to extract features with significant discriminatory information. To address this issue, we propose a new machine learning method called BiPepGlut using the concept of a bi-peptide-based evolutionary method for feature extraction. To build this model, we also use the Extra-Trees (ET) classifier for the classification purpose, which, to the best of our knowledge, has never been used for this task. Our results demonstrate BiPepGlut is able to significantly outperform previously proposed models to tackle this problem. BiPepGlut achieves 92.0%, 84.8%, 95.6%, 0.82, and 0.88 in accuracy, sensitivity, specificity, Matthew’s Correlation Coefficient, and F1-score, respectively. BiPepGlut is implemented as a publicly available online predictor.

Identification of post-translational modifications (PTM) is significant in the study of computational proteomics, cell biology, pathogenesis, and drug development due to its role in many bio-molecular mechanisms. Though there are several computational tools to identify individual PTMs, only three predictors have been established to predict multiple PTMs at the same lysine residue. Furthermore, detailed analysis and assessment on dataset balancing and the significance of different feature encoding techniques for a suitable multi-PTM prediction model are still lacking. This study introduces a computational method named ’iMul-kSite’ for predicting acetylation, crotonylation, methylation, succinylation, and glutarylation, from an unrecognized peptide sample with one, multiple, or no modifications. After successfully eliminating the redundant data samples from the majority class by analyzing the hardness of the sequence-coupling information, feature representation has been optimized by adopting the combination of ANOVA F-Test and incremental feature selection approach. The proposed predictor predicts multi-label PTM sites with 92.83% accuracy using the top 100 features. It has also achieved a 93.36% aiming rate and 96.23% coverage rate, which are much better than the existing state-of-the-art predictors on the validation test. This performance indicates that ’iMul-kSite’ can be used as a supportive tool for further K-PTM study. For the convenience of the experimental scientists, ’iMul-kSite’ has been deployed as a user-friendly web-server at http://103.99.176.239/iMul-kSite.

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RNA-Seq has made significant contributions to various fields, particularly in cancer research. Recent studies on differential gene expression analysis and the discovery of novel cancer biomarkers have extensively used RNA-Seq...

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