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. 2019 Jan 8;47(D1):D298-D308.
doi: 10.1093/nar/gky1074.

dbPTM in 2019: exploring disease association and cross-talk of post-translational modifications

Kai-Yao Huang  1   2   3 , Tzong-Yi Lee  1   2   3 , Hui-Ju Kao  4 , Chen-Tse Ma  4 , Chao-Chun Lee  4 , Tsai-Hsuan Lin  4 , Wen-Chi Chang  5 , Hsien-Da Huang  1   2   3
Affiliations

dbPTM in 2019: exploring disease association and cross-talk of post-translational modifications

Kai-Yao Huang et al. Nucleic Acids Res. .

Abstract

The dbPTM (http://dbPTM.mbc.nctu.edu.tw/) has been maintained for over 10 years with the aim to provide functional and structural analyses for post-translational modifications (PTMs). In this update, dbPTM not only integrates more experimentally validated PTMs from available databases and through manual curation of literature but also provides PTM-disease associations based on non-synonymous single nucleotide polymorphisms (nsSNPs). The high-throughput deep sequencing technology has led to a surge in the data generated through analysis of association between SNPs and diseases, both in terms of growth amount and scope. This update thus integrated disease-associated nsSNPs from dbSNP based on genome-wide association studies. The PTM substrate sites located at a specified distance in terms of the amino acids encoded from nsSNPs were deemed to have an association with the involved diseases. In recent years, increasing evidence for crosstalk between PTMs has been reported. Although mass spectrometry-based proteomics has substantially improved our knowledge about substrate site specificity of single PTMs, the fact that the crosstalk of combinatorial PTMs may act in concert with the regulation of protein function and activity is neglected. Because of the relatively limited information about concurrent frequency and functional relevance of PTM crosstalk, in this update, the PTM sites neighboring other PTM sites in a specified window length were subjected to motif discovery and functional enrichment analysis. This update highlights the current challenges in PTM crosstalk investigation and breaks the bottleneck of how proteomics may contribute to understanding PTM codes, revealing the next level of data complexity and proteomic limitation in prospective PTM research.

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Figures

Figure 1.
Figure 1.
The process of investigating disease-associated PTM sites based on disease association of SAP.
Figure 2.
Figure 2.
A case study of disease-associated acetylation site (Lys153) on Serine/threonine-protein kinase Chk2 based on the surrounding amino acid variants derived from disease-associated nsSNPs
Figure 3.
Figure 3.
A case study for investigating into the crosstalk between O-glycosylation and phosphorylation.
Figure 4.
Figure 4.
A tutorial for accessing the modified proteins by using the advanced filter functions.
See this image and copyright information in PMC

References

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