ArticleBMC bioinformatics2018
Detecting Succinylation sites from protein sequences using ensemble support vector machine.
Article in BMC bioinformatics, 2018. 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.
- Identifying protein succinylation sites using generative transformer and a two-dimensional representation with a deep capsule network.iScience · 2025Article
- LMPTMSite: A Platform for PTM Site Prediction in Proteins Leveraging Transformer-Based Protein Language Models.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Impact of Lysine Succinylation on the Biology of Fungi.Current issues in molecular biology · 2024Review
- DLBWE-Cys: a deep-learning-based tool for identifying cysteine S-carboxyethylation sites using binary-weight encoding.Frontiers in genetics · 2024Article
- 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
- Protein Succinylation and Malonylation as Potential Biomarkers in Schizophrenia.Journal of personalized medicine · 2022Article
- iSuc-ChiDT: a computational method for identifying succinylation sites using statistical difference table encoding and the chi-square decision table classifier.BioData mining · 2022Article
- Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Article
- CL-ACP: a parallel combination of CNN and LSTM anticancer peptide recognition model.BMC bioinformatics · 2021Article
- MDCAN-Lys: A Model for Predicting Succinylation Sites Based on Multilane Dense Convolutional Attention Network.Biomolecules · 2021Article
- Structure, Biosynthesis, and Biological Activity of Succinylated Forms of Bacteriocin BacSp222.International journal of molecular sciences · 2021Article
- Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks.BMC bioinformatics · 2021Article
- LSTMCNNsucc: A Bidirectional LSTM and CNN-Based Deep Learning Method for Predicting Lysine Succinylation Sites.BioMed research international · 2021Article
- A Transfer Learning-Based Approach for Lysine Propionylation Prediction.Frontiers in physiology · 2021Article
- Succinylation Site Prediction Based on Protein Sequences Using the IFS-LightGBM (BO) Model.Computational and mathematical methods in medicine · 2020Article
- Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLysine succinylation is a new kind of post-translational modification which plays a key role in protein conformation regulation and cellular function control. To understand the mechanism of succinylation profoundly, it is necessary to identify succinylation sites in proteins accurately. However, traditional methods, experimental approaches, are labor-intensive and time-consuming. Computational prediction methods have been proposed recent years, and they are popular because of their convenience and high speed. In this study, we developed a new method to predict succinylation sites in protein combining multiple features, including amino acid composition, binary encoding, physicochemical property and grey pseudo amino acid composition, with a feature selection scheme (information gain). And then, it was trained using SVM (Support Vector Machine) and an ensemble learning algorithm.
resultsThe performance of this method was measured with an accuracy of 89.14% and a MCC (Matthew Correlation Coefficient) of 0.79 using 10-fold cross validation on training dataset and an accuracy of 84.5% and a MCC of 0.2 on independent dataset.
conclusionsThe conclusions made from this study can help to understand more of the succinylation mechanism. These results suggest that our method was very promising for predicting succinylation sites. The source code and data of this paper are freely available at https://github.com/ningq669/PSuccE .
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