Prediction Of O-Glycosylation Site Using Pre-Trained Language Model And Machine Learning

Alkuhlani, Alhasan; Gad, Walaa; Roushdy, Mohamed Ismail; Salem, Abdel-Badeeh M.

doi:10.21608/ijicis.2023.160986.1218

Prediction Of O-Glycosylation Site Using Pre-Trained Language Model And Machine Learning

Document Type : Original Article

Authors

¹ Computer Science Departement, Faculty of Computer Information Sciences, Ain shams University, Cairo, Egypt

² Information Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

³ Faculty of Computer and Information Technology, Future University in Egypt, Cairo, Egypt

⁴ Computer Sciece Department, Faculty of Computer and Information Sciences, Ain Shams University

10.21608/ijicis.2023.160986.1218

Abstract

O-glycosylation is a typical type of protein post-translational modifications (PTMs), which is linked to several diseases and has significant roles in many biological processes. Identification of O-glycosylation sites is important to know the mechanism of the O-glycosylation process. However, the identification of PTM sites by laboratory experimental tools is time and money-consuming. Thus, the utilization of computational and artificial intelligence is becoming essential to predict O-glycosylation sites. In this paper, we proposed a new model to improve O-glycosylation site prediction using a transformer-based protein language model and machine learning. The dataset was collected and prepared from a recent data source called OGP (O-glycoprotein repository). The TAPE (Tasks Assessing Protein Embeddings) protein language model was used to feature extraction from the peptide sequences using the embedding strategy. Then, feature selection was implemented using the linear support vector machine (SVM) to select informative features. The XGBoost ensemble-based machine learning method was utilized for classification and prediction. The proposed model achieved high-performance results with 0.7761 accuracy, 0.7391 sensitivity, 0.8130 specificity, 0.8295 AUC, and 0.5537 MCC when compared with the traditional machine learning methods. On an independent dataset, the proposed method performed better than the latest available methods for predicting O-glycosylation sites.

Keywords