ArticleScientific reports2021
Computational identification of multiple lysine PTM sites by analyzing the instance hardness and feature importance.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Current computational tools for protein lysine acylation site prediction.Briefings in bioinformatics · 2024Pooled it
- PLysPTM-HGNN: predicting lysine PTM sites of proteins using hybrid graph neural networks.BMC bioinformatics · 2026Article
- MlyPredCSED: based on extreme point deviation compensated clustering combined with cross-scale convolutional neural networks to predict multiple lysine sites in human.Briefings in bioinformatics · 2025Article
- PreMLS: The undersampling technique based on ClusterCentroids to predict multiple lysine sites.PLoS computational biology · 2024Article
- Impact of Lysine Succinylation on the Biology of Fungi.Current issues in molecular biology · 2024Review
- RMTLysPTM: recognizing multiple types of lysine PTM sites by deep analysis on sequences.Briefings in bioinformatics · 2023Article
- Protein post-translational modification by lysine succinylation: Biochemistry, biological implications, and therapeutic opportunities.Genes & diseases · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identification of post-translational modifications (PTM) is significant in the study of computational proteomics, cell biology, pathogenesis, and drug development due to its role in many bio-molecular mechanisms. Though there are several computational tools to identify individual PTMs, only three predictors have been established to predict multiple PTMs at the same lysine residue. Furthermore, detailed analysis and assessment on dataset balancing and the significance of different feature encoding techniques for a suitable multi-PTM prediction model are still lacking. This study introduces a computational method named 'iMul-kSite' for predicting acetylation, crotonylation, methylation, succinylation, and glutarylation, from an unrecognized peptide sample with one, multiple, or no modifications. After successfully eliminating the redundant data samples from the majority class by analyzing the hardness of the sequence-coupling information, feature representation has been optimized by adopting the combination of ANOVA F-Test and incremental feature selection approach. The proposed predictor predicts multi-label PTM sites with 92.83% accuracy using the top 100 features. It has also achieved a 93.36% aiming rate and 96.23% coverage rate, which are much better than the existing state-of-the-art predictors on the validation test. This performance indicates that 'iMul-kSite' can be used as a supportive tool for further K-PTM study. For the convenience of the experimental scientists, 'iMul-kSite' has been deployed as a user-friendly web-server at http://103.99.176.239/iMul-kSite .
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