ArticleScientific reports2022
Improving protein succinylation sites prediction using embeddings from protein language model.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 2 of them syntheses that pooled it.
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Who cites it
50 citing papers in PubMed, 2 syntheses or guidelines 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
- Current computational tools for protein lysine acylation site prediction.Briefings in bioinformatics · 2024Pooled it
- Crosstalk between cysteine and lysine modifications: Integrating redox and metabolic regulation.Redox biology · 2026Review
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- Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications.Acta pharmaceutica Sinica. B · 2026Review
- The genetic foundations of convergent traits.Nature reviews. Genetics · 2026Review
- UniPTMs: a unified multi-type PTM site prediction model via master-slave architecture-based multi-stage fusion strategy and hierarchical contrastive loss.BMC bioinformatics · 2026Article
- eSIG-Net: an interaction language model that decodes the protein code of single mutations.Nature methods · 2026Article
- Protein language models accurately predict polymorphic peptide-modulated NK cell receptor-HLA class I interaction strengths.Science advances · 2026Article
- eSIG-Net: Accurate prediction of single-mutation induced perturbations on protein interactions using a language model.bioRxiv : the preprint server for biology · 2026Article
- UniGraphPTMs: a contrastive learning-enhanced universal framework for PTM site prediction via GNN and multimodal fusion.BMC genomics · 2026Article
- BiGKbhb: a bi-directional gated recurrent unit model for predicting lysine β-hydroxybutyrylation sites.BMC genomics · 2026Article
- Predicting protein-carbohydrate binding sites: a deep learning approach integrating protein language model embeddings and structural features.Briefings in bioinformatics · 2026Article
- Article
- DeepVIC: modular prediction and classification of bacterial virulence factors using protein language model embeddings.Bioinformatics advances · 2026Article
- Identifying protein succinylation sites using generative transformer and a two-dimensional representation with a deep capsule network.iScience · 2025Article
- An ensemble-based model comprising deep learning for predicting peptide-binding residues in proteins.NAR genomics and bioinformatics · 2025Article
- SPACE: STRING proteins as complementary embeddings.Bioinformatics (Oxford, England) · 2025Article
- Hybrid protein-ligand binding residue prediction with protein language models: does the structure matter?Bioinformatics (Oxford, England) · 2025Article
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5 authors.
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Abstract
Protein succinylation is an important post-translational modification (PTM) responsible for many vital metabolic activities in cells, including cellular respiration, regulation, and repair. Here, we present a novel approach that combines features from supervised word embedding with embedding from a protein language model called ProtT5-XL-UniRef50 (hereafter termed, ProtT5) in a deep learning framework to predict protein succinylation sites. To our knowledge, this is one of the first attempts to employ embedding from a pre-trained protein language model to predict protein succinylation sites. The proposed model, dubbed LMSuccSite, achieves state-of-the-art results compared to existing methods, with performance scores of 0.36, 0.79, 0.79 for MCC, sensitivity, and specificity, respectively. LMSuccSite is likely to serve as a valuable resource for exploration of succinylation and its role in cellular physiology and disease.
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