N-linked glycosylation site prediction stacking SVM, XGBoost, and KNN classifiers over ProtT5, ESM-2, and ProteinBERT sequence embeddings.
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N-linked glycosylation attaches an oligosaccharide to an asparagine residue and is one of the most consequential post-translational modifications in eukaryotic biology, shaping protein folding, stability, trafficking, and immune recognition. The modification almost always occurs inside an N-X-[S/T] sequon, where X is any residue but proline — but the sequon is necessary, not sufficient, and most sequons in a proteome are never glycosylated. Mass spectrometry can settle the question experimentally at considerable cost, which is why sequence-based predictors have been pursued for two decades.
StackGlyEmbed, from Bangladesh University of Engineering and Technology, attacks the problem by replacing hand-engineered descriptors with protein language model embeddings and then stacking conventional classifiers on top of them. Earlier tools in this line — NetNGlyc, GlycoMine, N-GlyDE, SPRINT-Gly — relied largely on composition and physicochemical features; LMNglyPred was the first to use a single language model, ProtT5-XL-U50. StackGlyEmbed combines three: ProtT5-XL-U50, ESM-2, and ProteinBERT.
The result is a fixed two-step pipeline — extract embeddings, then run the trained ensemble — that can be applied to any new human protein sequence without retraining.
Two human datasets were used. N-GlyDE contributes 3,079 training sequons (2,049 positive) and a 446-sequon independent test set; N-GlycositeAtlas, as curated for LMNglyPred, contributes 24,258 training sequons (8,401 positive) and a 2,478-sequon test set. Each training set is split 60/40, with the larger part training the base learners and the remainder training the meta learner. Features are Yeo–Johnson transformed, class imbalance is handled by ensemble random undersampling, and learners are tuned by grid search with 10-fold cross-validation.
On the N-GlyDE independent test set StackGlyEmbed reaches 98.2% sensitivity, 86.8% specificity, 92.5% balanced accuracy, 89.1% F1, 0.826 MCC, and 0.967 AUROC, against 0.810 F1 and 0.718 MCC for LMNglyPred and 0.710 F1 and 0.531 MCC for DeepNGlyPred. Performance on N-GlycositeAtlas is substantially lower — 83.1% sensitivity, 69.3% F1, 0.515 MCC — though still ahead of LMNglyPred on the same split; the headline numbers quoted in the abstract are the N-GlyDE figures. The N-GlyDE-trained ensemble is the one shipped in the repository.
The tool suits glycoproteomics groups triaging candidate sites before targeted mass spectrometry, and biologics teams screening therapeutic protein sequences for glycosylation liabilities that would affect expression, half-life, or immunogenicity. Because inference needs only a FASTA sequence and the site positions of interest, it slots into annotation pipelines where structural data is unavailable. Both datasets are human, so predictions on other organisms fall outside the evaluated domain.
StackGlyEmbed's contribution is methodological rather than architectural: it shows that combining representations from several protein language models, plus a windowed aggregation that most sequon-restricted predictors omit, measurably outperforms single-PLM deep learning baselines on the same benchmarks — and that a modest stacking ensemble is sufficient to exploit them. The gap between its two test sets is a useful caution about how much benchmark composition drives reported glycosylation-prediction numbers. The GitHub repository ships the trained base-layer pickles, transformers, and both prediction scripts, but carries no LICENSE file, leaving reuse terms unstated.
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