Accurate Prediction of Lysine Methylation Sites Using Evolutionary and Structural-Based Information
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2024
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| author | Md. Easin Arafat Md. Wakil Ahmad S. M. Shovan Towhid Ul Haq Nazrul Islam Mufti Mahmud M. Shamim Kaiser |
| author_facet | Md. Easin Arafat Md. Wakil Ahmad S. M. Shovan Towhid Ul Haq Nazrul Islam Mufti Mahmud M. Shamim Kaiser |
| contents | <p>Methylation is considered one of the proteins’ most important post-translational modifications (PTM). Plasticity and cellular dynamics are among the many traits that are regulated by methylation. Currently, methylation sites are identified using experimental approaches. However, these methods are time-consuming and expensive. With the use of computer modelling, methylation sites can be identified quickly and accurately, providing valuable information for further trial and investigation. In this study, we propose a new machine-learning model called MeSEP to predict methylation sites that incorporates both evolutionary and structural-based information. To build this model, we first extract evolutionary and structural features from the PSSM and SPD2 profiles, respectively. We then employ Extreme Gradient Boosting (XGBoost) as the classification model to predict methylation sites. To address the issue of imbalanced data and bias towards negative samples, we use the SMOTETomek-based hybrid sampling method. The MeSEP was validated on an independent test set (ITS) and 10-fold cross-validation (TCV) using lysine methylation sites. The method achieved: an accuracy of 82.9% in ITS and 84.6% in TCV; precision of 0.92 in ITS and 0.94 in TCV; area under the curve values of 0.90 in ITS and 0.92 in TCV; F1 score of 0.81 in ITS and 0.83 in TCV; and MCC of 0.67 in ITS and 0.70 in TCV. MeSEP significantly outperformed previous studies found in the literature. MeSEP as a standalone toolkit and all its source codes are publicly available at https://github.com/arafatro/MeSEP.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_1007_s12559-024-10268-2 |
| institution | Zenodo |
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| publishDate | 2024 |
| publisher | Zenodo |
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| spellingShingle | Accurate Prediction of Lysine Methylation Sites Using Evolutionary and Structural-Based Information Md. Easin Arafat Md. Wakil Ahmad S. M. Shovan Towhid Ul Haq Nazrul Islam Mufti Mahmud M. Shamim Kaiser Post-translational modification Methylation Sequential information Evolutionary features Structural features Extreme Gradient Boosting · Computational approach <p>Methylation is considered one of the proteins’ most important post-translational modifications (PTM). Plasticity and cellular dynamics are among the many traits that are regulated by methylation. Currently, methylation sites are identified using experimental approaches. However, these methods are time-consuming and expensive. With the use of computer modelling, methylation sites can be identified quickly and accurately, providing valuable information for further trial and investigation. In this study, we propose a new machine-learning model called MeSEP to predict methylation sites that incorporates both evolutionary and structural-based information. To build this model, we first extract evolutionary and structural features from the PSSM and SPD2 profiles, respectively. We then employ Extreme Gradient Boosting (XGBoost) as the classification model to predict methylation sites. To address the issue of imbalanced data and bias towards negative samples, we use the SMOTETomek-based hybrid sampling method. The MeSEP was validated on an independent test set (ITS) and 10-fold cross-validation (TCV) using lysine methylation sites. The method achieved: an accuracy of 82.9% in ITS and 84.6% in TCV; precision of 0.92 in ITS and 0.94 in TCV; area under the curve values of 0.90 in ITS and 0.92 in TCV; F1 score of 0.81 in ITS and 0.83 in TCV; and MCC of 0.67 in ITS and 0.70 in TCV. MeSEP significantly outperformed previous studies found in the literature. MeSEP as a standalone toolkit and all its source codes are publicly available at https://github.com/arafatro/MeSEP.</p> |
| title | Accurate Prediction of Lysine Methylation Sites Using Evolutionary and Structural-Based Information |
| topic | Post-translational modification Methylation Sequential information Evolutionary features Structural features Extreme Gradient Boosting · Computational approach |
| url | https://doi.org/10.1007/s12559-024-10268-2 |