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. 2011;6(12):e28228.
doi: 10.1371/journal.pone.0028228. Epub 2011 Dec 2.

Bioinformatic analysis and post-translational modification crosstalk prediction of lysine acetylation

Zhike Lu  1 , Zhongyi Cheng, Yingming Zhao, Samuel L Volchenboum
Affiliations

Bioinformatic analysis and post-translational modification crosstalk prediction of lysine acetylation

Zhike Lu et al. PLoS One. 2011.

Abstract

Recent proteomics studies suggest high abundance and a much wider role for lysine acetylation (K-Ac) in cellular functions. Nevertheless, cross influence between K-Ac and other post-translational modifications (PTMs) has not been carefully examined. Here, we used a variety of bioinformatics tools to analyze several available K-Ac datasets. Using gene ontology databases, we demonstrate that K-Ac sites are found in all cellular compartments. KEGG analysis indicates that the K-Ac sites are found on proteins responsible for a diverse and wide array of vital cellular functions. Domain structure prediction shows that K-Ac sites are found throughout a wide variety of protein domains, including those in heat shock proteins and those involved in cell cycle functions and DNA repair. Secondary structure prediction proves that K-Ac sites are preferentially found in ordered structures such as alpha helices and beta sheets. Finally, by mutating K-Ac sites in silico and predicting the effect on nearby phosphorylation sites, we demonstrate that the majority of lysine acetylation sites have the potential to impact protein phosphorylation, methylation, and ubiquitination status. Our work validates earlier smaller-scale studies on the acetylome and demonstrates the importance of PTM crosstalk for regulation of cellular function.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Data source and analyses.
Lysine acetylation sites were collected from three sources; PhosphositePlus, Uniprot, and from the work of Choudhary, et al. BLAST-P was used to map each acetylation site to the corresponding IPI protein. The data were condensed to remove redundant sites. Subsequent analyses included conservation studies, subcellular localization, pathway analysis, domain analysis, secondary structure prediction, and crosstalk prediction. Details and methods are outlined in the text.
Figure 2
Figure 2. Subcellular localization of acetylated lysine residues.
The gene ontology annotations for identified IPI genes were obtained from the GOA database. Of the 2384 lysine acetylation genes identified, 488 (20.4%) are exclusively nuclear, 182 (7.6%) are mitochondrial only, and 499 (20.9%) are only found in the cytoplasm. 570 proteins (23.9%) were not assigned to any compartment.
Figure 3
Figure 3. Biologic pathway analysis.
Using the Kyoto Encyclopedia of Genes and Genomes (KEGG), we identified pathways enriched for the acetylated proteins identified. False discovery rate control was used to correct for multiple hypothesis testing, the KEGG pathways with a corrected p-value<0.01 were considered significant. A diverse array of cellular pathways and functions were identified including those involved in cellular processes (light green), genetic information processing (red), human diseases (indigo), metabolism (yellow).
Figure 4
Figure 4. Domain analysis.
Proteins with acetylated lysines were aligned to the distribution of domains in the NCBI Conserved Domain Database using PRS-BLAST. Correction for multiple hypothesis testing was carried out using standard false discovery rate control methods, and domains with a corrected p-value<0.01 were considered significant. Domains with more than the number expected K-Ac sites were considered “overrepresented.”
Figure 5
Figure 5. Secondary structure prediction.
The secondary structure of acetylated proteins was derived from Uniprot and the Dictionary of Protein Secondary Structure (DSSP). There was significant enrichment of acetylated lysine sites in alpha helices and beta-sheets only.
Figure 6
Figure 6. Crosstalk prediction.
Top: (A) The influence of acetylated lysine on neighboring phosphorylation sites was predicted by substituting a leucine for a lysine at all predicted K-Ac sites and then predicting potential phosphorylation sites using NetPhosK 1.0. (B) In all, 51% of nearby phosphorylation sites were affected, and these were classified as Type I (gain of a phosphorylation site, 10%), Type II (loss of a phosphorylation site, 35%), Type III (retention of a phosphorylation site with gain of kinase binding site, 16%), Type IV (retention of a phosphorylation site with loss of kinase binding site, 29%), and Type V (loss of an endogenous kinase binding site with concomitant gain of a new one, 10%). This analysis was repeated by substituting glutamine for lysine (Panels C and D). This analysis was repeated for methylation using BPB-PPMS (middle) and ubiquitination using UbPred (bottom). For further details, see text.
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