Dataset Information

Evolutionary constraint and disease associations of post-translational modification sites in human genomes.

ABSTRACT: Interpreting the impact of human genome variation on phenotype is challenging. The functional effect of protein-coding variants is often predicted using sequence conservation and population frequency data, however other factors are likely relevant. We hypothesized that variants in protein post-translational modification (PTM) sites contribute to phenotype variation and disease. We analyzed fraction of rare variants and non-synonymous to synonymous variant ratio (Ka/Ks) in 7,500 human genomes and found a significant negative selection signal in PTM regions independent of six factors, including conservation, codon usage, and GC-content, that is widely distributed across tissue-specific genes and function classes. PTM regions are also enriched in known disease mutations, suggesting that PTM variation is more likely deleterious. PTM constraint also affects flanking sequence around modified residues and increases around clustered sites, indicating presence of functionally important short linear motifs. Using target site motifs of 124 kinases, we predict that at least ?180,000 motif-breaker amino acid residues that disrupt PTM sites when substituted, and highlight kinase motifs that show specific negative selection and enrichment of disease mutations. We provide this dataset with corresponding hypothesized mechanisms as a community resource. As an example of our integrative approach, we propose that PTPN11 variants in Noonan syndrome aberrantly activate the protein by disrupting an uncharacterized cluster of phosphorylation sites. Further, as PTMs are molecular switches that are modulated by drugs, we study mutated binding sites of PTM enzymes in disease genes and define a drug-disease network containing 413 novel predicted disease-gene links.

SUBMITTER: Reimand J

PROVIDER: S-EPMC4303425 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Evolutionary constraint and disease associations of post-translational modification sites in human genomes.

Reimand Jüri J Wagih Omar O Bader Gary D GD

PLoS genetics 20150122 1

Interpreting the impact of human genome variation on phenotype is challenging. The functional effect of protein-coding variants is often predicted using sequence conservation and population frequency data, however other factors are likely relevant. We hypothesized that variants in protein post-translational modification (PTM) sites contribute to phenotype variation and disease. We analyzed fraction of rare variants and non-synonymous to synonymous variant ratio (Ka/Ks) in 7,500 human genomes and ...[more]

PMID: 25611800

Similar Datasets

Project description:Intrinsically disordered regions (IDRs) of proteins play significant biological functional roles despite lacking a well-defined 3D structure. For example, IDRs provide efficient housing for large numbers of post-translational modification (PTM) sites in eukaryotic proteins. Here, we study the distribution of more than 15,000 experimentally determined human methylation, acetylation and ubiquitination sites (collectively termed 'MAU' sites) in ordered and disordered regions, and analyse their conservation across 380 eukaryotic species. Conservation signals for the maintenance and novel emergence of MAU sites are examined at 11 evolutionary levels from the whole eukaryotic domain down to the ape superfamily, in both ordered and disordered regions. We discover that MAU PTM is a major driver of conservation for arginines and lysines in both ordered and disordered regions, across the 11 levels, most significantly across the mammalian clade. Conservation of human methylatable arginines is very strongly favoured for ordered regions rather than for disordered, whereas methylatable lysines are conserved in either set of regions, and conservation of acetylatable and ubiquitinatable lysines is favoured in disordered over ordered. Notably, we find evidence for the emergence of new lysine MAU sites in disordered regions of proteins in deuterostomes and mammals, and in ordered regions after the dawn of eutherians. For histones specifically, MAU sites demonstrate an idiosyncratic significant conservation pattern that is evident since the last common ancestor of mammals. Similarly, folding-on-binding (FB) regions are highly enriched for MAU sites relative to either ordered or disordered regions, with ubiquitination sites in FBs being highly conserved at all evolutionary levels back as far as mammals. This investigation clearly demonstrates the complex patterns of PTM evolution across the human proteome and that it is necessary to consider conservation of sequence features at multiple evolutionary levels in order not to get an incomplete or misleading picture.

Project description:Lysine post-translational modifications (PTMs) play a crucial role in regulating diverse functions and biological processes of proteins. However, because of the large volumes of sequencing data generated from genome-sequencing projects, systematic identification of different types of lysine PTM substrates and PTM sites in the entire proteome remains a major challenge. In recent years, a number of computational methods for lysine PTM identification have been developed. These methods show high diversity in their core algorithms, features extracted and feature selection techniques and evaluation strategies. There is therefore an urgent need to revisit these methods and summarize their methodologies, to improve and further develop computational techniques to identify and characterize lysine PTMs from the large amounts of sequence data. With this goal in mind, we first provide a comprehensive survey on a large collection of 49 state-of-the-art approaches for lysine PTM prediction. We cover a variety of important aspects that are crucial for the development of successful predictors, including operating algorithms, sequence and structural features, feature selection, model performance evaluation and software utility. We further provide our thoughts on potential strategies to improve the model performance. Second, in order to examine the feasibility of using deep learning for lysine PTM prediction, we propose a novel computational framework, termed MUscADEL (Multiple Scalable Accurate Deep Learner for lysine PTMs), using deep, bidirectional, long short-term memory recurrent neural networks for accurate and systematic mapping of eight major types of lysine PTMs in the human and mouse proteomes. Extensive benchmarking tests show that MUscADEL outperforms current methods for lysine PTM characterization, demonstrating the potential and power of deep learning techniques in protein PTM prediction. The web server of MUscADEL, together with all the data sets assembled in this study, is freely available at http://muscadel.erc.monash.edu/. We anticipate this comprehensive review and the application of deep learning will provide practical guide and useful insights into PTM prediction and inspire future bioinformatics studies in the related fields.

Project description:Most of pyruvoyl-dependent proteins observed in prokaryotes and eukaryotes are critical regulatory enzymes, which are primary targets of inhibitors for anti-cancer and anti-parasitic therapy. These proteins undergo an autocatalytic, intramolecular self-cleavage reaction in which a covalently bound pyruvoyl group is generated on a conserved serine residue. Traditional detections of the modified serine sites are performed by experimental approaches, which are often labor-intensive and time-consuming. In this study, we initiated in an attempt for the computational predictions of such serine sites with Feature Selection based on a Random Forest. Since only a small number of experimentally verified pyruvoyl-modified proteins are collected in the protein database at its current version, we only used a small dataset in this study. After removing proteins with sequence identities >60%, a non-redundant dataset was generated and was used, which contained only 46 proteins, with one pyruvoyl serine site for each protein. Several types of features were considered in our method including PSSM conservation scores, disorders, secondary structures, solvent accessibilities, amino acid factors and amino acid occurrence frequencies. As a result, a pretty good performance was achieved in our dataset. The best 100.00% accuracy and 1.0000 MCC value were obtained from the training dataset, and 93.75% accuracy and 0.8441 MCC value from the testing dataset. The optimal feature set contained 9 features. Analysis of the optimal feature set indicated the important roles of some specific features in determining the pyruvoyl-group-serine sites, which were consistent with several results of earlier experimental studies. These selected features may shed some light on the in-depth understanding of the mechanism of the post-translational self-maturation process, providing guidelines for experimental validation. Future work should be made as more pyruvoyl-modified proteins are found and the method should be evaluated on larger datasets. At last, the predicting software can be downloaded from http://www.nkbiox.com/sub/pyrupred/index.html.

Dataset Information

Evolutionary constraint and disease associations of post-translational modification sites in human genomes.

Publications

Evolutionary constraint and disease associations of post-translational modification sites in human genomes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets