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Proteome-wide analysis of human disease mutations in short linear motifs: neglected players in cancer?

Uyar B, Weatheritt RJ, Dinkel H, Davey NE, Gibson TJ - Mol Biosyst (2014)

Bottom Line: Moreover, compared to neutral missense mutations, disease mutations more frequently impact functionally important residues of SLiMs, cause changes in the physicochemical properties of SLiMs, and disrupt more SLiM-mediated interactions.Analysis of these mutations resulted in a comprehensive list of experimentally validated or predicted SLiMs disrupted in disease.Our results suggest that an increased focus on SLiMs in the coming decades will improve our understanding of human diseases and aid in the development of targeted treatments.

View Article: PubMed Central - PubMed

Affiliation: Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117, Heidelberg, Germany. bora.uyar@embl.de toby.gibson@embl.de.

ABSTRACT
Disease mutations are traditionally thought to impair protein functionality by disrupting the folded globular structure of proteins. However, 22% of human disease mutations occur in natively unstructured segments of proteins known as intrinsically disordered regions (IDRs). This therefore implicates defective IDR functionality in various human diseases including cancer. The functionality of IDRs is partly attributable to short linear motifs (SLiMs), but it remains an open question how much defects in SLiMs contribute to human diseases. A proteome-wide comparison of the distribution of missense mutations from disease and non-disease mutation datasets revealed that, in IDRs, disease mutations are more likely to occur within SLiMs than neutral missense mutations. Moreover, compared to neutral missense mutations, disease mutations more frequently impact functionally important residues of SLiMs, cause changes in the physicochemical properties of SLiMs, and disrupt more SLiM-mediated interactions. Analysis of these mutations resulted in a comprehensive list of experimentally validated or predicted SLiMs disrupted in disease. Furthermore, this in-depth analysis suggests that 'prostate cancer pathway' is particularly enriched for proteins with disease-related SLiMs. The contribution of mutations in SLiMs to disease may currently appear small when compared to mutations in globular domains. However, our analysis of mutations in predicted SLiMs suggests that this contribution might be more substantial. Therefore, when analysing the functional impact of mutations on proteins, SLiMs in proteins should not be neglected. Our results suggest that an increased focus on SLiMs in the coming decades will improve our understanding of human diseases and aid in the development of targeted treatments.

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Analysis of mutations in short linear motifs. (A) Pipeline for the analysis of mutations in SLiMs. (B) Proteins shared by mutation datasets (OMIM: Inherited Disease Mutations from UniProt, COSMIC: Catalog of Somatic Mutations in Cancer, 1000GP: missense mutations from the 1000 Genomes Project). (C) Mutated sites shared by mutation datasets.
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fig1: Analysis of mutations in short linear motifs. (A) Pipeline for the analysis of mutations in SLiMs. (B) Proteins shared by mutation datasets (OMIM: Inherited Disease Mutations from UniProt, COSMIC: Catalog of Somatic Mutations in Cancer, 1000GP: missense mutations from the 1000 Genomes Project). (C) Mutated sites shared by mutation datasets.

Mentions: With the exception of post-translational modification sites,15–17 the impact of disease-related mutations on SLiMs and the association of SLiMs with human diseases have not been studied at a proteome-wide scale with a specific focus on SLiMs. One of the previous notable studies has provided a literature review of the disease-related mutations in SLiMs.31 Another study has investigated whether mutations in IDRs that shift the disordered state of a residue into an ordered state (called disorder-to-order transition mutations) are enriched in experimentally validated SLiMs.2 However, no significant enrichment of disorder-to-order transition mutations was observed for disease-related mutations compared to neutral missense mutations. In this work, we report a proteome-wide analysis of disease-related mutations with a specific focus on SLiMs. We utilise the growing knowledge of disease and non-disease mutations generated by high-throughput sequencing and compiled by resources such as the “Catalog of Somatic Mutations In Cancer” (COSMIC)32 and the “1000 Genomes Project” (1000GP).33 We complement our analysis by mutation data annotated in UniProt34 for inherited human diseases compiled by “Online Mendelian Inheritance in Man” (OMIM).35 By comparing the distribution of disease and non-disease mutation datasets, we show that disease-related mutations are enriched in SLiMs in IDRs and they occur more frequently at functionally important residues of SLiMs. Also, in the context of protein interaction networks, we show that the number of interactions mediated by a SLiM correlates with the likelihood that a mutation affecting that SLiM will be disease-related. Based on these analyses, we report a comprehensive list of experimentally validated and predicted disease-related SLiMs. This list reveals that ‘KEGG human prostate cancer pathway’ is the pathway most enriched for proteins containing cancer-related SLiMs (see the analysis pipeline in Fig. 1A).


Proteome-wide analysis of human disease mutations in short linear motifs: neglected players in cancer?

Uyar B, Weatheritt RJ, Dinkel H, Davey NE, Gibson TJ - Mol Biosyst (2014)

Analysis of mutations in short linear motifs. (A) Pipeline for the analysis of mutations in SLiMs. (B) Proteins shared by mutation datasets (OMIM: Inherited Disease Mutations from UniProt, COSMIC: Catalog of Somatic Mutations in Cancer, 1000GP: missense mutations from the 1000 Genomes Project). (C) Mutated sites shared by mutation datasets.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4306509&req=5

fig1: Analysis of mutations in short linear motifs. (A) Pipeline for the analysis of mutations in SLiMs. (B) Proteins shared by mutation datasets (OMIM: Inherited Disease Mutations from UniProt, COSMIC: Catalog of Somatic Mutations in Cancer, 1000GP: missense mutations from the 1000 Genomes Project). (C) Mutated sites shared by mutation datasets.
Mentions: With the exception of post-translational modification sites,15–17 the impact of disease-related mutations on SLiMs and the association of SLiMs with human diseases have not been studied at a proteome-wide scale with a specific focus on SLiMs. One of the previous notable studies has provided a literature review of the disease-related mutations in SLiMs.31 Another study has investigated whether mutations in IDRs that shift the disordered state of a residue into an ordered state (called disorder-to-order transition mutations) are enriched in experimentally validated SLiMs.2 However, no significant enrichment of disorder-to-order transition mutations was observed for disease-related mutations compared to neutral missense mutations. In this work, we report a proteome-wide analysis of disease-related mutations with a specific focus on SLiMs. We utilise the growing knowledge of disease and non-disease mutations generated by high-throughput sequencing and compiled by resources such as the “Catalog of Somatic Mutations In Cancer” (COSMIC)32 and the “1000 Genomes Project” (1000GP).33 We complement our analysis by mutation data annotated in UniProt34 for inherited human diseases compiled by “Online Mendelian Inheritance in Man” (OMIM).35 By comparing the distribution of disease and non-disease mutation datasets, we show that disease-related mutations are enriched in SLiMs in IDRs and they occur more frequently at functionally important residues of SLiMs. Also, in the context of protein interaction networks, we show that the number of interactions mediated by a SLiM correlates with the likelihood that a mutation affecting that SLiM will be disease-related. Based on these analyses, we report a comprehensive list of experimentally validated and predicted disease-related SLiMs. This list reveals that ‘KEGG human prostate cancer pathway’ is the pathway most enriched for proteins containing cancer-related SLiMs (see the analysis pipeline in Fig. 1A).

Bottom Line: Moreover, compared to neutral missense mutations, disease mutations more frequently impact functionally important residues of SLiMs, cause changes in the physicochemical properties of SLiMs, and disrupt more SLiM-mediated interactions.Analysis of these mutations resulted in a comprehensive list of experimentally validated or predicted SLiMs disrupted in disease.Our results suggest that an increased focus on SLiMs in the coming decades will improve our understanding of human diseases and aid in the development of targeted treatments.

View Article: PubMed Central - PubMed

Affiliation: Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117, Heidelberg, Germany. bora.uyar@embl.de toby.gibson@embl.de.

ABSTRACT
Disease mutations are traditionally thought to impair protein functionality by disrupting the folded globular structure of proteins. However, 22% of human disease mutations occur in natively unstructured segments of proteins known as intrinsically disordered regions (IDRs). This therefore implicates defective IDR functionality in various human diseases including cancer. The functionality of IDRs is partly attributable to short linear motifs (SLiMs), but it remains an open question how much defects in SLiMs contribute to human diseases. A proteome-wide comparison of the distribution of missense mutations from disease and non-disease mutation datasets revealed that, in IDRs, disease mutations are more likely to occur within SLiMs than neutral missense mutations. Moreover, compared to neutral missense mutations, disease mutations more frequently impact functionally important residues of SLiMs, cause changes in the physicochemical properties of SLiMs, and disrupt more SLiM-mediated interactions. Analysis of these mutations resulted in a comprehensive list of experimentally validated or predicted SLiMs disrupted in disease. Furthermore, this in-depth analysis suggests that 'prostate cancer pathway' is particularly enriched for proteins with disease-related SLiMs. The contribution of mutations in SLiMs to disease may currently appear small when compared to mutations in globular domains. However, our analysis of mutations in predicted SLiMs suggests that this contribution might be more substantial. Therefore, when analysing the functional impact of mutations on proteins, SLiMs in proteins should not be neglected. Our results suggest that an increased focus on SLiMs in the coming decades will improve our understanding of human diseases and aid in the development of targeted treatments.

Show MeSH
Related in: MedlinePlus