※ Computational Resources for Histone Acetylation and Methylation:

    <1>. Histone related resources.

        (1) Histone Modification databases.

        (2) Histone Protein databases.

    <2>. Acetylation related resources.

        (1) Acetylation databases.

        (2) Prediction of Acetylation sites.

    <3>. Methylation related resources.

        (1) Prediction of Methylation sites.


<1>. Histone related resources.

1. Histone Modification databases.

(1) CPLM: a database of protein lysine modifications, which occur at active ε-amino groups of specific lysine residues in proteins and are critical for orchestrating various biological processes, including acetylation and methylation (Liu Z., et al., 2014).

(2) HHMD: the human histone modification database. focuses on the storage and integration of histone modification datasets that were obtained from laboratory experiments. The latest release of HHMD incorporates 43 location-specific histone modifications in human (Zhang, et al., 2010).

(3) PHOSIDA 2011: the posttranslational modification database which manage posttranslational modification sites of various species ranging from bacteria to human (Gnad F, et al., 2011).

(4) EpimiR: a database of curated mutual regulation between miRNAs and epigenetic modifications, which collects 1974 regulations between 19 kinds of epigenetic modifications, such as DNA methylation, histone acetylation, H3K4me3, H3S10p. (Dai E, et al., 2014).

(5) GED: a manually curated comprehensive resource for epigenetic modification of gametogenesis. The database integrates three kinds information of epigenetic modifications during gametogenesis (DNA methylation, histone modification and RNA regulation) (Bai W, et al., 2016).

(6) dbHiMo: a web-based epigenomics platform mainly for fungi histone-modifying enzymes (Choi J, et al., 2015).

(7) 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 four fully developed, in-depth data mining tools (Li H, et al., 2009).

(8) HistoneHits: a database for histone mutations and their phenotypes. This database combines assay results (phenotypes) with information about sequences, structures, post-translational modifications, and evolutionary conservation (Huang H, et al., 2009).

(9) PEpiD: a prostate epigenetic database in mammals. The Prostate Epigenetic Database archives the three extensively characterized epigenetic mechanisms DNA methylation, histone modification, and microRNA implicated in prostate cancer of human, mouse, and rat.(Shi J, et al., 2013).

(10) EpiFactors: a comprehensive database of human epigenetic factors and complexes, providing information about epigenetic regulators, their complexes, targets and products(Medvedeva, et al., 2015).

2. Histone Protein databases.

(1) The Histone Database: The Histone Sequence Database is a curated collection of sequences and structures of histones and non-histone proteins containing histone folds, assembled from major public databases (Mariño-Ramírez, et al., 2011).

(2) ChromDB: the chromatin database, displays chromatin-associated proteins, including RNAi-associated proteins, for a broad range of organisms.(Gendler, et al., 2008).

<2>. Acetylation related resources.

1. Acetylation Databases.

(1) PhosphoSitePlus: (PSP) is a comprehensive, manually curated and interactive resource on post-translational modifications (PTM). PSP contains encompasses 130000 non-redundant modification sites, manily on phosphorylation, ubiquitinylation and acetylation (Hornbeck, et al., 2004).

(2) g2pDB: A Database Mapping Protein Post-Translational Modifications to Genomic Coordinates. The original data comes mainly from published studies, many of which involve the investigation of post-translational modification acceptor site assignments, e.g., phosphorylation, ubiquitination, SUMOylation, acetylation, and N-linked glycosylation sites. (Keegan S, et al., 2016).

(3) dbPTM 2.0: integrates experimentally verified PTMs from several databases, and to annotate the predicted PTMs on Swiss-Prot proteins , 2,071 acetylation sites were included while most of which were N-alpha-terminal ones (Lee TY, et al., 2006) .

(4) 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. Prediction of acetylation sites.

(1) PAIL 1.0: Prediction of Nepsilon-acetylation on internal lysines implemented in Bayesian Discriminant Method (Li, et al., 2006).

(2) NetAcet 1.0: a web server predicts N-terminal acetylation sites. The method was trained on yeast data but, as mentioned in the article describing the method, it obtains similar performance values on mammalian substrates acetylated by NatA orthologs (Kiemer, et al., 2005).

(3) 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).

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

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

(6) EnsemblePail: lysine Acetylation sites prediction using ensembles of Support Vector Machine classifiers (Xu, et al., 2010).

(7) ASEB: a web server for KAT-specific acetylation site prediction (Li, et al., 2012).

(8) PSKAcePred: position-specific analysis and prediction for protein lysine acetylation based on multiple features (Suo, et al., 2012).

(9) bpbphka: accurate prediction of human lysine acetylation through bi-relative adapted binomial score Bayes feature representation (Shao, et al., 2012).

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

(11) LAceP: lysine acetylation site prediction using logistic regression classifiers (Hou, et al., 2014).

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

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

<3>. Methylation related resources.

1. Prediction of Methylation sites.

(1) AutoMotif server (Service stopped, AMS replaced it now): prediction of single residue post-translational modifications in proteins (Plewczynski, et al., 2005, Basu, et al., 2010).

(2) MeMo: a web tool for prediction of protein methylation modifications (Chen, et al., 2006).

(3) BPB-PPMS: Computational identification of protein methylation sites through bi-profile Bayes feature extraction (Shao, et al., 2009).

(4) MASA: Incorporating structural characteristics for identification of protein methylation sites (Shien, et al., 2009).

(5) Methy SVMIACO (The Methy SVMIACO can be acquired freely on request from the authors): Identification of protein methylation sites by coupling improved ant colony optimization algorithm and support vector machine (Li, et al., 2011).

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

(7) PMeS: Prediction of Methylation Sites Based on Enhanced Feature Encoding Scheme (Shi, et al., 2012).