LMNglyPred: prediction of human N-linked glycosylation sites using embeddings from a pre-trained protein language model
Pakhrin, Subash C. Pokharel, Suresh Aoki-Kinoshita, Kiyoko F. Beck, Moriah R. Dam, Tarun K. Caragea, Doina KC, Dukka B.
Pakhrin, Subash C.
Pokharel, Suresh
Aoki-Kinoshita, Kiyoko F.
Beck, Moriah R.
Dam, Tarun K.
Caragea, Doina
KC, Dukka B.
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2023-05-01
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Article
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Keywords
Deep learning,N-linked glycosylation,Post-translation modification,Prediction,Protein language model
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Citation
Subash C Pakhrin, PhD and others, LMNglyPred: prediction of human N-linked glycosylation sites using embeddings from a pre-trained protein language model, Glycobiology, Volume 33, Issue 5, May 2023, Pages 411-422, https://doi.org/10.1093/glycob/cwad033
Abstract
Protein N-linked glycosylation is an important post-translational mechanism in Homo sapiens, playing essential roles in many vital biological processes. It occurs at the N-X-[S/T] sequon in amino acid sequences, where X can be any amino acid except proline. However, not all N-X-[S/T] sequons are glycosylated; thus, the N-X-[S/T] sequon is a necessary but not sufficient determinant for protein glycosylation. In this regard, computational prediction of N-linked glycosylation sites confined to N-X-[S/T] sequons is an important problem that has not been extensively addressed by the existing methods, especially in regard to the creation of negative sets and leveraging the distilled information from protein language models (pLMs). Here, we developed LMNglyPred, a deep learning-based approach, to predict N-linked glycosylated sites in human proteins using embeddings from a pre-trained pLM. LMNglyPred produces sensitivity, specificity, Matthews Correlation Coefficient, precision, and accuracy of 76.50, 75.36, 0.49, 60.99, and 75.74 percent, respectively, on a benchmark-independent test set. These results demonstrate that LMNglyPred is a robust computational tool to predict N-linked glycosylation sites confined to the N-X-[S/T] sequon.
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Oxford University Press
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Glycobiology
Volume 33, No. 5
Volume 33, No. 5
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1460-2423