ArticleScientific reports2014
Accurate in silico identification of species-specific acetylation sites by integrating protein sequence-derived and functional features.
Article in Scientific reports, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.
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Who cites it
34 citing papers in PubMed, 2 syntheses or guidelines pooled it, 80 citations in OpenAlex.
- Current computational tools for protein lysine acylation site prediction.Briefings in bioinformatics · 2024Pooled it
- Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches.Briefings in bioinformatics · 2019Pooled it
- CLASPP: A unified model for predicting post-translational modifications.PLoS computational biology · 2026Article
- CLASPP: A unified model for predicting post-translational modifications.bioRxiv : the preprint server for biology · 2026Article
- PCBert-Kla: an efficient prediction method for lysine lactylation sites based on ProtBert and fusion of physicochemical features.Briefings in bioinformatics · 2025Article
- Recent Advances, Challenges, and Functional Applications of Protein Chemical Modification in the Food Industry.Foods (Basel, Switzerland) · 2025Review
- MSTL-Kace: Prediction of Prokaryotic Lysine Acetylation Sites Based on Multistage Transfer Learning Strategy.ACS omega · 2023Article
- Prediction of serine phosphorylation sites mapping on Schizosaccharomyces Pombe by fusing three encoding schemes with the random forest classifier.Scientific reports · 2022Article
- STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction.Briefings in bioinformatics · 2022Article
- Systematic Characterization of Lysine Post-translational Modification Sites Using MUscADEL.Methods in molecular biology (Clifton, N.J.) · 2022Article
- MDCAN-Lys: A Model for Predicting Succinylation Sites Based on Multilane Dense Convolutional Attention Network.Biomolecules · 2021Article
- IRC-Fuse: improved and robust prediction of redox-sensitive cysteine by fusing of multiple feature representations.Journal of computer-aided molecular design · 2021Article
- KSHV LANA acetylation-selective acidic domain reader sequence mediates virus persistence.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- RF-MaloSite and DL-Malosite: Methods based on random forest and deep learning to identify malonylation sites.Computational and structural biotechnology journal · 2020Article
- Large-scale comparative assessment of computational predictors for lysine post-translational modification sites.Briefings in bioinformatics · 2019Review
- Analysis and prediction of human acetylation using a cascade classifier based on support vector machine.BMC bioinformatics · 2019Article
- Computational identification of microbial phosphorylation sites by the enhanced characteristics of sequence information.Scientific reports · 2019Article
- Review
- LAIPT: Lysine Acetylation Site Identification with Polynomial Tree.International journal of molecular sciences · 2018Article
- Using a Classifier Fusion Strategy to Identify Anti-angiogenic Peptides.Scientific reports · 2018Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lysine acetylation is a reversible post-translational modification, playing an important role in cytokine signaling, transcriptional regulation, and apoptosis. To fully understand acetylation mechanisms, identification of substrates and specific acetylation sites is crucial. Experimental identification is often time-consuming and expensive. Alternative bioinformatics methods are cost-effective and can be used in a high-throughput manner to generate relatively precise predictions. Here we develop a method termed as SSPKA for species-specific lysine acetylation prediction, using random forest classifiers that combine sequence-derived and functional features with two-step feature selection. Feature importance analysis indicates functional features, applied for lysine acetylation site prediction for the first time, significantly improve the predictive performance. We apply the SSPKA model to screen the entire human proteome and identify many high-confidence putative substrates that are not previously identified. The results along with the implemented Java tool, serve as useful resources to elucidate the mechanism of lysine acetylation and facilitate hypothesis-driven experimental design and validation.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.