ArticleGenomics, proteomics & bioinformatics2020
SuccSite: Incorporating Amino Acid Composition and Informative k-spaced Amino Acid Pairs to Identify Protein Succinylation Sites.
Article in Genomics, proteomics & bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Mining lysine post-translational modification sites by integrating protein language model representations with structural context.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Identifying protein succinylation sites using generative transformer and a two-dimensional representation with a deep capsule network.iScience · 2025Article
- RLSuccSite: succinylation sites prediction based on reinforcement learning dynamic with balanced reward mechanism and three-peaks enhanced method for physicochemical property scores.Journal of cheminformatics · 2025Article
- KD_MultiSucc: incorporating multi-teacher knowledge distillation and word embeddings for cross-species prediction of protein succinylation sites.Biology methods & protocols · 2025Article
- Post-translational modification prediction via prompt-based fine-tuning of a GPT-2 model.Nature communications · 2024Article
- Identify Diabetes-related Targets based on ForgeNet_GPC.Current computer-aided drug design · 2024Article
- GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on feature fusion and GBDT classifier.BMC genomics · 2023Article
- Phosphoglycerate kinase (PGK) 1 succinylation modulates epileptic seizures and the blood-brain barrier.Experimental animals · 2023Article
- Protein post-translational modification by lysine succinylation: Biochemistry, biological implications, and therapeutic opportunities.Genes & diseases · 2023Review
- KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites.Genomics, proteomics & bioinformatics · 2023Article
- iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features.Frontiers in genetics · 2023Article
- Deep_KsuccSite: A novel deep learning method for the identification of lysine succinylation sites.Frontiers in genetics · 2022Article
- nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning.Briefings in bioinformatics · 2021Article
- Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties.Scientific reports · 2021Article
- Incorporating support vector machine with sequential minimal optimization to identify anticancer peptides.BMC bioinformatics · 2021Article
- Residue-Residue Contact Can Be a Potential Feature for the Prediction of Lysine Crotonylation Sites.Frontiers in genetics · 2021Article
- LSTMCNNsucc: A Bidirectional LSTM and CNN-Based Deep Learning Method for Predicting Lysine Succinylation Sites.BioMed research international · 2021Article
- Characterization and identification of lysine crotonylation sites based on machine learning method on both plant and mammalian.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein succinylation is a biochemical reaction in which a succinyl group (-CO-CH2-CH2-CO-) is attached to the lysine residue of a protein molecule. Lysine succinylation plays important regulatory roles in living cells. However, studies in this field are limited by the difficulty in experimentally identifying the substrate site specificity of lysine succinylation. To facilitate this process, several tools have been proposed for the computational identification of succinylated lysine sites. In this study, we developed an approach to investigate the substrate specificity of lysine succinylated sites based on amino acid composition. Using experimentally verified lysine succinylated sites collected from public resources, the significant differences in position-specific amino acid composition between succinylated and non-succinylated sites were represented using the Two Sample Logo program. These findings enabled the adoption of an effective machine learning method, support vector machine, to train a predictive model with not only the amino acid composition, but also the composition of k-spaced amino acid pairs. After the selection of the best model using a ten-fold cross-validation approach, the selected model significantly outperformed existing tools based on an independent dataset manually extracted from published research articles. Finally, the selected model was used to develop a web-based tool, SuccSite, to aid the study of protein succinylation. Two proteins were used as case studies on the website to demonstrate the effective prediction of succinylation sites. We will regularly update SuccSite by integrating more experimental datasets. SuccSite is freely accessible at http://csb.cse.yzu.edu.tw/SuccSite/.
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