Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features
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 me...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
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MDPI AG
2020-08-01
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| Series: | Genes |
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| Online Access: | https://www.mdpi.com/2073-4425/11/9/1023 |
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| author | Md. Easin Arafat Md. Wakil Ahmad S.M. Shovan Abdollah Dehzangi Shubhashis Roy Dipta Md. Al Mehedi Hasan Ghazaleh Taherzadeh Swakkhar Shatabda Alok Sharma |
| author_facet | Md. Easin Arafat Md. Wakil Ahmad S.M. Shovan Abdollah Dehzangi Shubhashis Roy Dipta Md. Al Mehedi Hasan Ghazaleh Taherzadeh Swakkhar Shatabda Alok Sharma |
| author_sort | Md. Easin Arafat |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T16:39:28Z |
| format | Article |
| id | doaj.art-1f7e3302773c47968b9c65b25be025e3 |
| institution | Directory Open Access Journal |
| issn | 2073-4425 |
| language | English |
| last_indexed | 2024-03-10T16:39:28Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Genes |
| spelling | doaj.art-1f7e3302773c47968b9c65b25be025e32023-11-20T12:04:48ZengMDPI AGGenes2073-44252020-08-01119102310.3390/genes11091023Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary FeaturesMd. Easin Arafat0Md. Wakil Ahmad1S.M. Shovan2Abdollah Dehzangi3Shubhashis Roy Dipta4Md. Al Mehedi Hasan5Ghazaleh Taherzadeh6Swakkhar Shatabda7Alok Sharma8Department of Computer Science and Engineering, United International University, Dhaka 1212, BangladeshDepartment of Computer Science and Engineering, United International University, Dhaka 1212, BangladeshDepartment of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, BangladeshDepartment of Computer Science, Rutgers University, Camden, NJ 08102, USADepartment of Computer Science and Engineering, United International University, Dhaka 1212, BangladeshDepartment of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, BangladeshInstitute for Bioscience and Biotechnology Research, University of Maryland, College Park, MD 20742, USADepartment of Computer Science and Engineering, United International University, Dhaka 1212, BangladeshInstitute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD 4111, AustraliaPost 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.https://www.mdpi.com/2073-4425/11/9/1023post-translational modificationlysine Glutarylationmachine learningextra-trees classifierbi-peptide evolutionary features |
| spellingShingle | Md. Easin Arafat Md. Wakil Ahmad S.M. Shovan Abdollah Dehzangi Shubhashis Roy Dipta Md. Al Mehedi Hasan Ghazaleh Taherzadeh Swakkhar Shatabda Alok Sharma Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features Genes post-translational modification lysine Glutarylation machine learning extra-trees classifier bi-peptide evolutionary features |
| title | Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features |
| title_full | Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features |
| title_fullStr | Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features |
| title_full_unstemmed | Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features |
| title_short | Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features |
| title_sort | accurately predicting glutarylation sites using sequential bi peptide based evolutionary features |
| topic | post-translational modification lysine Glutarylation machine learning extra-trees classifier bi-peptide evolutionary features |
| url | https://www.mdpi.com/2073-4425/11/9/1023 |
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