※ Computational resources of protein lysine modifications

Last updated: Nov. 3rd, 2016

Introduction:

Currently, protein lysine modifications have been greatly expanded while various types of modifications and a large number of modified lysines were identified. Since lysine modifications play important roles in most of biological processes, identification of lysine modified substrates is fundamental for understanding the molecular mechanisms of lysine modifications. Besides experimental approaches, prediction of potential candidates with computational methods has also attracted great attention for its convenience and fast-speed. Here, we present a summarization of computational resources of protein lysine modifications, including databases and predictors.

We apologized that the computational studies without any web links of databases or tools will not be included in this compendium, since it's not easy for experimentalists to use studies directly. We are grateful for users feedback. Please contact us to add, remove or update one or multiple web links below.

Index:

<1> Lysine Modifications Databases

<2> Prediction of Lysine Modifications Sites

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<1> Lysine Modifications Databases:

1. HPRD release 9 : HPRD currently contains information for 16,972 PTMs which belong to various categories such as acetylation (259), while phosphorylation (10,858), dephosphorylation (3,118) and glycosylation (1,860) form the majority of the annotated PTMs. At least one enzyme responsible for PTMs has been annotated for 8,960 PTMs, which resulted in the documentation of 7,253 enzyme - substrate relationships (Keshava Prasad, et al., 2009).

2. dbPTM 2016: dbPTM is an integrated resource for protein post-translational modifications (PTMs). Due to the importance of protein post-translational modifications (PTMs) in regulating biological processes, the dbPTM was developed as a comprehensive database by integrating experimentally verified PTMs from several databases and annotating the potential PTMs for all UniProtKB protein entries.(Huang, et al., 2016).

3. SysPTM 2.0: SysPTM provides a systematic and sophisticated platform for proteomic PTM research, equipped not only with a knowledge base of manually curated multi-type modification data, but also with five online tools developed, in-depth data mining tools. (Li, et al., 2014).

4. PhosphoSitePlus, 2014: a new version of PhosphoSite, is a web-based database to collect protein modification sites, including protein phosphorylation sites from scientific literature as well as high-throughput discovery programs. 36,665 acetylation sites were included. (Hornbeck, et al., 2014).

5. HHMD: a comprehensive database for human histone modifications, which focuses on integrating useful histone modification information from experimental data. The acetylation sites on human histones were included (Zhang, et al., 2010).

<2> Prediction of Lysine Modifications Sites:

1. GPS-MSP: Computational prediction of methylation types of covalently modified lysine and arginine residues in proteins. (Deng, et al., 2016).

2. GPS-PAIL 2.0: GPS-PAIL: prediction of lysine acetyltransferase-specific modification sites from protein sequences. (Deng, et al., 2016).

3. GPS-SUMO: GPS-SUMO: a tool for the prediction of sumoylation sites and SUMO-interaction motifs. (Zhao, et al., 2013).

4. GPS-PUP: GPS-PUP: Computational prediction of pupylation sites. (Liu, et al., 2011).

5. CKSAAP_UbSite : Prediction of ubiquitination sites by using the composition of k-spaced amino acid pairs. (Chen, et al., 2011).

6. UbiProber: Incorporating key position and amino acid residue features to identify general and species-specific Ubiquitin conjugation sites. (Chen, et al., 2013).

7. UbPred 1.1: Identification, analysis, and prediction of protein ubiquitination sites. (Li, et al., 2009).

8. SSPKA: Accurate in silico identification of species-specific acetylation sites by integrating protein sequence-derived and functional features. (Li, et al., 2014 ).

9. PredMod: combine experimental methods with clustering analysis of protein sequences to predict protein acetylation based on the sequence characteristics of acetylated lysines within histones. (Basu, et al., 2009).

10. LysAcet 1.1: prediction of lysine acetylation by support vector machines. (Li, et al., 2009).

11. PHOSIDA: a database as well as a predictor for in vivo human acetylation sites. (Gnad et al. 2010).

12. N-Ace: a web tool for predicting the protein Acetylation site based on Support Vector Machine (SVM). (Lee, et al., 2010 ).

13. PLMLA: prediction of lysine methylation and lysine acetylation by combining multiple features.(Shi, et al., 2012).

14. SUMOsp: a web server for sumoylation site prediction (Xue et al. 2006).

15. SUMOAMVR: Systematic Analysis of the Genetic Variability That Impacts SUMO Conjugation and Their Involvement in Human Disease (Xu, et al., 2015).

16. SuccFind: a novel succinylation sites online prediction tool via enhanced characteristic strategy. (Xu, et al., 2015).

17. BPB-PPMS: Computational identification of protein methylation sites through bi-profile Bayes feature extraction (Lee, et al., 2010 ).