Incorporating a transfer learning technique with amino acid embeddings to efficiently predict N-linked glycosylation sites in ion channels. (March 2021)

• Record Type: Journal Article
• Authors: Nguyen, Trinh-Trung-Duong; Le, Nguyen-Quoc-Khanh; Tran, The-Anh; Pham, Dinh-Minh; Ou, Yu-Yen
• Is Part Of: Computers in biology and medicine. Volume 130(2021)
• Publication Date: 2021-03
• Subjects: Ion channel -- N-linked glycosylation -- Post-translational modification site prediction -- Transfer learning -- Amino acid embeddings; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104212
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Glycosylation is a dynamic enzymatic process that attaches glycan to proteins or other organic molecules such as lipoproteins. Research has shown that such a process in ion channel proteins plays a fundamental role in modulating ion channel functions. This study used a computational method to predict N-linked glycosylation sites, the most common type, in ion channel proteins. From segments of ion channel proteins centered around N-linked glycosylation sites, the amino acid embedding vectors of each residue were concatenated to create features for prediction. We experimented with two different models for converting amino acids to their corresponding embeddings: one was fed with ion channel sequences and the other with a large dataset composed of more than one million protein sequences. The latter model stemmed from the idea of transfer learning technique and emerged as a more efficient feature extractor. Our best model was obtained from this transfer learning approach and a hyperparameter tuning process with a random search on 5-fold cross-validation data. It achieved an accuracy, specificity, sensitivity, and Matthews correlation coefficient of 93.4%, 92.8%, 98.6%, and 0.726, respectively. Corresponding scores on an independent test were 92.9%, 92.2%, 99%, and 0.717. These results outperform the position-specific scoring matrix features that are predominantly employed in post-translational modification site predictions. Furthermore, compared to N-GlyDE, GlycoEP, SPRINT-Gly, the most recent N-linked glycosylation site predictors, our model yields higher scores on the above 4 metrics, thus further demonstrating the efficiency of our approach. Graphical abstract: In this study, we approached a computational method to predict N_linked glycosylation sites, the most abundant glycosylation type, in ion channel proteins. From segments of ion channel proteins centering the N-linked glycosylation sites, the amino acid embedding vectors of each residue was concatenated to create the features for prediction. We experimented with 2 different models for converting amino acids to their corresponding embeddings: one was fed with ion channel sequences and the other was fueled with a large dataset comprised of more than 1 million protein sequences. The latter model, stemmed from the idea of transfer learning technique, emerge as a more efficient feature extractor. Image 1 Highlights: Glycosylation is a dynamic enzymatic process that plays a fundamental role in modulating ion channel functions. Advanced natural language processing technique was applied on ion channel segments centering N_linked glycosylational sites. Different hyperparameters were surveyed to examine their influence to prediction performance. Efficacy of transfer learning from a large dataset with more than 1 million protein sequences was proved and highlighted.