ArticlePloS one2018
GPSuc: Global Prediction of Generic and Species-specific Succinylation Sites by aggregating multiple sequence features.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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34 citing papers in PubMed.
- Identifying protein succinylation sites using generative transformer and a two-dimensional representation with a deep capsule network.iScience · 2025Article
- SIRT5-mediated regulation of BMP2 succinylation enhances osteogenic differentiation of BMSCs and promotes tibial fracture healing.Stem cell research & therapy · 2025Article
- Succinylome Profiling the Function and Distribution of Lysine Succinylation in Saccharopolyspora erythraea.Applied biochemistry and biotechnology · 2025Article
- LMPTMSite: A Platform for PTM Site Prediction in Proteins Leveraging Transformer-Based Protein Language Models.Methods in molecular biology (Clifton, N.J.) · 2025Article
- ResLysEmbed: a ResNet-based framework for succinylated lysine residue prediction using sequence and language model embeddings.Bioinformatics advances · 2025Article
- Article
- KD_MultiSucc: incorporating multi-teacher knowledge distillation and word embeddings for cross-species prediction of protein succinylation sites.Biology methods & protocols · 2025Article
- PMTPred: machine-learning-based prediction of protein methyltransferases using the composition of k-spaced amino acid pairs.Molecular diversity · 2024Article
- Impact of Lysine Succinylation on the Biology of Fungi.Current issues in molecular biology · 2024Review
- Protein post-translational modification by lysine succinylation: Biochemistry, biological implications, and therapeutic opportunities.Genes & diseases · 2023Review
- pSuc-EDBAM: Predicting lysine succinylation sites in proteins based on ensemble dense blocks and an attention module.BMC bioinformatics · 2022Article
- Improving protein succinylation sites prediction using embeddings from protein language model.Scientific reports · 2022Article
- Prediction of serine phosphorylation sites mapping on Schizosaccharomyces Pombe by fusing three encoding schemes with the random forest classifier.Scientific reports · 2022Article
- Deep_KsuccSite: A novel deep learning method for the identification of lysine succinylation sites.Frontiers in genetics · 2022Article
- Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Structure, Biosynthesis, and Biological Activity of Succinylated Forms of Bacteriocin BacSp222.International journal of molecular sciences · 2021Article
- First comprehensive analysis of lysine succinylation in paper mulberry (Broussonetia papyrifera).BMC genomics · 2021Article
- PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features.International journal of molecular sciences · 2021Article
- IRC-Fuse: improved and robust prediction of redox-sensitive cysteine by fusing of multiple feature representations.Journal of computer-aided molecular design · 2021Article
- PUP-Fuse: Prediction of Protein Pupylation Sites by Integrating Multiple Sequence Representations.International journal of molecular sciences · 2021Article
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2 authors.
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Abstract
Lysine succinylation is one of the dominant post-translational modification of the protein that contributes to many biological processes including cell cycle, growth and signal transduction pathways. Identification of succinylation sites is an important step for understanding the function of proteins. The complicated sequence patterns of protein succinylation revealed by proteomic studies highlight the necessity of developing effective species-specific in silico strategies for global prediction succinylation sites. Here we have developed the generic and nine species-specific succinylation site classifiers through aggregating multiple complementary features. We optimized the consecutive features using the Wilcoxon-rank feature selection scheme. The final feature vectors were trained by a random forest (RF) classifier. With an integration of RF scores via logistic regression, the resulting predictor termed GPSuc achieved better performance than other existing generic and species-specific succinylation site predictors. To reveal the mechanism of succinylation and assist hypothesis-driven experimental design, our predictor serves as a valuable resource. To provide a promising performance in large-scale datasets, a web application was developed at http://kurata14.bio.kyutech.ac.jp/GPSuc/.
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