ArticleFrontiers in bioinformatics2022
BERT-Kgly: A Bidirectional Encoder Representations From Transformers (BERT)-Based Model for Predicting Lysine Glycation Site for
Article in Frontiers in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- Protein Language Models: Applications and Perspectives.Journal of proteome research · 2026Review
- Accurate identification of broadly neutralizing antibodies against dengue virus based on deep stacking strategy with multi-perspective features.Scientific reports · 2025Article
- BBATProt: a framework predicting biological function with enhanced feature extraction via interpretable deep learning.Briefings in bioinformatics · 2025Article
- Substrate prediction for RiPP biosynthetic enzymesDigital discovery · 2025Article
- Article
- Addressing the Problem of Lysine Glycation Prediction in Proteins via Recurrent Neural Networks.BioMed research international · 2025Article
- Protein feature engineering framework for AMPylation site prediction.Scientific reports · 2024Article
- SumoPred-PLM: human SUMOylation and SUMO2/3 sites Prediction using Pre-trained Protein Language Model.NAR genomics and bioinformatics · 2024Article
- Substrate Prediction for RiPP Biosynthetic Enzymes via Masked Language Modeling and Transfer Learning.ArXiv · 2024Article
- Prediction of linear B-cell epitopes based on protein sequence features and BERT embeddings.Scientific reports · 2024Article
- MSTL-Kace: Prediction of Prokaryotic Lysine Acetylation Sites Based on Multistage Transfer Learning Strategy.ACS omega · 2023Article
- ProtTrans-Glutar: Incorporating Features From Pre-trained Transformer-Based Models for Predicting Glutarylation Sites.Frontiers in genetics · 2022Article
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Authors and funding
6 authors.
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Abstract
As one of the most important posttranslational modifications (PTMs), protein lysine glycation changes the characteristics of the proteins and leads to the dysfunction of the proteins, which may cause diseases. Accurately detecting the glycation sites is of great benefit for understanding the biological function and potential mechanism of glycation in the treatment of diseases. However, experimental methods are expensive and time-consuming for lysine glycation site identification. Instead, computational methods, with their higher efficiency and lower cost, could be an important supplement to the experimental methods. In this study, we proposed a novel predictor, BERT-Kgly, for protein lysine glycation site prediction, which was developed by extracting embedding features of protein segments from pretrained Bidirectional Encoder Representations from Transformers (BERT) models. Three pretrained BERT models were explored to get the embeddings with optimal representability, and three downstream deep networks were employed to build our models. Our results showed that the model based on embeddings extracted from the BERT model pretrained on 556,603 protein sequences of UniProt outperforms other models. In addition, an independent test set was used to evaluate and compare our model with other existing methods, which indicated that our model was superior to other existing models.
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