Nafi, M. M. I. | Search bioRxiv

bioRxiv2025

StackGlyEmbed: Prediction of N-linked Glycosylation sites using protein language models

N-linked glycosylation is one of the most basic post-translational modifications (PTMs) where oligosaccharides covalently bond with Asparagine (N). These are found in the conserved regions like N-X-S or N-X-T where X can be any residue except Proline (P). Prediction of N-linked glycosylation sites has great importance as these PTMs play a vital role in many biological processes and functionalities. Experimental methods, such as mass spectrometry, for detecting N-linked glycosylation sites are very expensive. Therefore, prediction of N-linked glycosylation sites has become an important research field. In this work, we propose StackGlyEmbed, a stacking ensemble machine learning model, to computationally predict N-linked glycosylation sites. We have explored embeddings from several protein language models and built the stacking ensemble using SVM, XGB and KNN learners in the base layer, with a second SVM model in the meta layer. StackGlyEmbed achieves 98.2% sensitivity, 92.5% balanced accuracy, 89.1% F1-score and 82.6% MCC in independent testing, outperforming the existing SOTA methods. StackGlyEmbed is freely available at https://github.com/nafcoder/StackGlyEmbed.

bioinformatics↗

bioRxiv2024

Prediction of protein-carbohydrate binding sites from protein primary sequence

BackgroundA protein is a large complex macromolecule that has a crucial role in performing most of the work in cells and tissues. It is made up of one or more long chains of amino acid residues. Another important biomolecule, after DNA and protein, is carbohydrate. Carbohydrates interact with proteins to run various biological processes. Several biochemical experiments exist to learn the protein-carbohydrate interactions, but they are expensive, time-consuming, and challenging. Therefore, developing computational techniques for effectively predicting protein-carbohydrate binding interactions from protein primary sequence has given rise to a prominent new field of research. ResultIn this study, we propose StackCBEmbed, an ensemble machine learning model to effectively classify protein-carbohydrate binding interactions at residue level. StackCBEmbed combines traditional sequence-based features along with features derived from a pre-trained transformer-based protein language model. To the best of our knowledge, ours is the first attempt to apply protein language model in predicting protein-carbohydrate binding interactions. StackCBEmbed achieved sensitivity and balanced accuracy scores of 0.730, 0.776 and 0.666, 0.742 in two separate independent test sets. This performance is superior compared to the earlier prediction models benchmarked in the same datasets. ConclusionWe thus hope that StackCBEmbed will discover novel protein-carbohydrate interactions and help advance the related fields of research. StackCBEmbed is freely available as Python scripts at https://github.com/nafiislam/StackCBEmbed.

bioinformatics↗