※ Computational resources of protein succinylation
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1. CPLM: A self-developed database of protein lysine modifications (Liu, et al., 2014).
2. PLMD: An updated data resource of protein lysine modifications (Xu, et al., 2017).
3. dbPTM: An integrated resource for protein post-translational modifications (Huang, et al., 2016).
4. PhosphoSitePlus: The database provides comprehensive information and tools for the study of protein post-translational modifications including phosphorylation, acetylation, succinylation and more. The web use is free for everyone including commercial (Hornbeck, et al., 2015).
<2> Prediction of Ksucc sites:
1. SucPred: Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique (Zhao, et al., 2015).
2. SuccFind: A novel succinylation sites online prediction tool via enhanced characteristic strategy (Xu, et al., 2015).
3. iSuc-PseAAC: Predicting lysine succinylation in proteins by incorporating peptide position-specific propensity (Xu, et al., 2015).
4. iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset (Jia, et al., 2016).
5. pSuc-Lys: Evolutionary and structural properties of amino acids prove effective for succinylation site prediction
(Jia,
et al., 2016).
6. SuccinSite: A computational tool for the prediction of protein succinylation sites by exploiting the amino acid patterns and properties
(Hasan,
et al. 2016). 7. pSuc-PseRat: Predicting Lysine Succinylation in Proteins by Exploiting the Ratios of Sequence Coupling and Properties (Ai,
et al., 2017 ). 8. SucStruct: Prediction of succinylated lysine residues by using structural properties of amino acids (López,
et al., 2017).
9. PSSM-Suc: Accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction (Dehzangi,
et al., 2017).
10. SuccinSite2.0: A systematic identification of species-specific protein succinylation sites using joint element features information (Hasan,
et al., 2017).
11. SSEvol-Suc: Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information
from profile bigrams (Dehzangi,
et al., 2018).
12. Success: Evolutionary and structural properties of amino acids prove effective for succinylation site prediction
(López,
et al., 2018).