ArticlePloS one2018
Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile bigrams.
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 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it, 75 citations in OpenAlex.
- Current computational tools for protein lysine acylation site prediction.Briefings in bioinformatics · 2024Pooled it
- ResLysEmbed: a ResNet-based framework for succinylated lysine residue prediction using sequence and language model embeddings.Bioinformatics advances · 2025Article
- Impact of Lysine Succinylation on the Biology of Fungi.Current issues in molecular biology · 2024Review
- GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on feature fusion and GBDT classifier.BMC genomics · 2023Article
- 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_KsuccSite: A novel deep learning method for the identification of lysine succinylation sites.Frontiers in genetics · 2022Article
- pSuc-FFSEA: Predicting Lysine Succinylation Sites in Proteins Based on Feature Fusion and Stacking Ensemble Algorithm.Frontiers in cell and developmental biology · 2022Article
- Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks.BMC bioinformatics · 2021Article
- FSL-Kla: A few-shot learning-based multi-feature hybrid system for lactylation site prediction.Computational and structural biotechnology journal · 2021Article
- LSTMCNNsucc: A Bidirectional LSTM and CNN-Based Deep Learning Method for Predicting Lysine Succinylation Sites.BioMed research international · 2021Article
- Article
- PupStruct: Prediction of Pupylated Lysine Residues Using Structural Properties of Amino Acids.Genes · 2020Article
- Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features.Genes · 2020Article
- Systematic analysis of lysine acetylome and succinylome reveals the correlation between modification of H2A.X complexes and DNA damage response in breast cancer.Oncology reports · 2020Article
- Mal-Light: Enhancing Lysine Malonylation Sites Prediction Problem Using Evolutionary-based Features.IEEE access : practical innovations, open solutions · 2020Article
- Classification of adaptor proteins using recurrent neural networks and PSSM profiles.BMC genomics · 2019Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Post-translational modification refers to the biological mechanism involved in the enzymatic modification of proteins after being translated in the ribosome. This mechanism comprises a wide range of structural modifications, which bring dramatic variations to the biological function of proteins. One of the recently discovered modifications is succinylation. Although succinylation can be detected through mass spectrometry, its current experimental detection turns out to be a timely process unable to meet the exponential growth of sequenced proteins. Therefore, the implementation of fast and accurate computational methods has emerged as a feasible solution. This paper proposes a novel classification approach, which effectively incorporates the secondary structure and evolutionary information of proteins through profile bigrams for succinylation prediction. The proposed predictor, abbreviated as SSEvol-Suc, made use of the above features for training an AdaBoost classifier and consequently predicting succinylated lysine residues. When SSEvol-Suc was compared with four benchmark predictors, it outperformed them in metrics such as sensitivity (0.909), accuracy (0.875) and Matthews correlation coefficient (0.75).
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Registered trials
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.