Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features
| Autor: | Wakil Ahmad, Swakkhar Shatabda, S. M. Shovan, Ghazaleh Taherzadeh, Alok Sharma, Al Mehedi Hasan, Easin Arafat, Shubhashis Roy Dipta, Abdollah Dehzangi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Support Vector Machine Correlation coefficient lcsh:QH426-470 Computer science Feature extraction Machine learning computer.software_genre extra-trees classifier Article Evolution Molecular Glutarates 03 medical and health sciences Mice Protein sequencing Genetics Animals Amino Acid Sequence lysine Glutarylation bi-peptide evolutionary features Genetics (clinical) 030102 biochemistry & molecular biology business.industry Lysine Computational Biology Proteins Mycobacterium tuberculosis Peptide Fragments lcsh:Genetics 030104 developmental biology machine learning post-translational modification Posttranslational modification Artificial intelligence business computer Classifier (UML) Protein Processing Post-Translational Algorithms |
| Zdroj: | Genes Volume 11 Issue 9 Genes, Vol 11, Iss 1023, p 1023 (2020) |
| ISSN: | 2073-4425 |
| DOI: | 10.3390/genes11091023 |
| Popis: | 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&rsquo s Correlation Coefficient, and F1-score, respectively. BiPepGlut is implemented as a publicly available online predictor. |
| Databáze: | OpenAIRE |
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