※ Computational resources of protein succinylation

<1> Databases

<2> Prediction of Ksucc sites


<1> Databases:

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).