ArticleInternational journal of nanomedicine2017
A systematic identification of species-specific protein succinylation sites using joint element features information.
Article in International journal of nanomedicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Identifying protein succinylation sites using generative transformer and a two-dimensional representation with a deep capsule network.iScience · 2025Article
- ResLysEmbed: a ResNet-based framework for succinylated lysine residue prediction using sequence and language model embeddings.Bioinformatics advances · 2025Article
- KD_MultiSucc: incorporating multi-teacher knowledge distillation and word embeddings for cross-species prediction of protein succinylation sites.Biology methods & protocols · 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
- Investigating the Precise Identification of Citrullination Sites with High- Performance Score Metrics Using a Powerful Computation Predicting Tool.Combinatorial chemistry & high throughput screening · 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
- 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
- MDCAN-Lys: A Model for Predicting Succinylation Sites Based on Multilane Dense Convolutional Attention Network.Biomolecules · 2021Article
- First comprehensive analysis of lysine succinylation in paper mulberry (Broussonetia papyrifera).BMC genomics · 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
- An Improved Computational Prediction Model for Lysine Succinylation Sites Mapping onCurrent genomics · 2021Article
- LSTMCNNsucc: A Bidirectional LSTM and CNN-Based Deep Learning Method for Predicting Lysine Succinylation Sites.BioMed research international · 2021Article
- ProIn-Fuse: improved and robust prediction of proinflammatory peptides by fusing of multiple feature representations.Journal of computer-aided molecular design · 2020Article
- iLBE for Computational Identification of Linear B-cell Epitopes by Integrating Sequence and Evolutionary Features.Genomics, proteomics & bioinformatics · 2020Article
- Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction.Current genomics · 2020Review
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5 authors.
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Abstract
Lysine succinylation, an important type of protein posttranslational modification, plays significant roles in many cellular processes. Accurate identification of succinylation sites can facilitate our understanding about the molecular mechanism and potential roles of lysine succinylation. However, even in well-studied systems, a majority of the succinylation sites remain undetected because the traditional experimental approaches to succinylation site identification are often costly, time-consuming, and laborious. In silico approach, on the other hand, is potentially an alternative strategy to predict succinylation substrates. In this paper, a novel computational predictor SuccinSite2.0 was developed for predicting generic and species-specific protein succinylation sites. This predictor takes the composition of profile-based amino acid and orthogonal binary features, which were used to train a random forest classifier. We demonstrated that the proposed SuccinSite2.0 predictor outperformed other currently existing implementations on a complementarily independent dataset. Furthermore, the important features that make visible contributions to species-specific and cross-species-specific prediction of protein succinylation site were analyzed. The proposed predictor is anticipated to be a useful computational resource for lysine succinylation site prediction. The integrated species-specific online tool of SuccinSite2.0 is publicly accessible.
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