Inferring the functional effects of mutation through clusters of mutations in homologous proteins

Yue, Peng; Forrest, William F.; Kaminker, Joshua S.; Lohr, Scott; Zhang, Zemin; Cavet, Guy

doi:10.1002/humu.21194

Cited by 49 publications

(51 citation statements)

References 48 publications

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“…One class of methods makes use of the distribution and type of cancer mutations, including the density of mutations in specific genes 1; 2; 13 and the ratio of synonymous to non-synonymous mutations to identify selection pressure on particular genes 3; 14 . The second class of methods groups genes in which cancer mutations occur into pathways or gene networks.…”

Section: Introductionmentioning

confidence: 99%

Structural and Functional Impact of Cancer-Related Missense Somatic Mutations

Shi

Moult

2011

Journal of Molecular Biology

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A number of large scale cancer somatic genome sequencing projects are now identifying genetic alterations in cancers. Evaluation of the effects of these mutations is essential for understanding their contribution to tumorigenesis. We have used SNPs3D, a software suite originally developed for analyzing non-synonymous germ line variants, to identify single base mutations with a high impact on protein structure and function. Two machine learning methods are used, one identifying mutations that destabilize protein three dimensional structure, and the other utilizing sequence conservation, and detecting all types of effects on in vivo protein function. Incorporation of detailed structure information into the analysis allows detailed interpretation of the functional effects of mutations in specific cases. Data from a set of breast and colorectal tumors were analyzed. In known cancer genes, approaching 100% of mutations are found to impact protein function, supporting the view that these methods are appropriate for identifying driver mutations. Overall, 50% to 60% of all somatic missense mutations are predicted to have a high impact on structural stability or to more generally affect the function of the corresponding proteins. This value is similar to the fraction of all possible missense mutations that have high impact, and much higher than the corresponding one for human population SNPs, at about 30%. The majority of mutations in tumor suppressors destabilize protein structure, while mutations in oncogenes operate in more varied ways, including destabilization of the less active conformational states. The set of high impact mutations encompass the possible drivers.

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Section: Introductionmentioning

confidence: 99%

Structural and Functional Impact of Cancer-Related Missense Somatic Mutations

Shi

Moult

2011

Journal of Molecular Biology

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“…mCluster17 was used to consolidate and visualize analogous sites of EPHB4 mutations within other TKD-containing proteins and determine whether mutations have been detected at these sites. PyMOL software (Schrödinger, Portland OR) was used to visualize mutation sites within the EPHB4 protein structure.…”

Section: Methodsmentioning

confidence: 99%

Novel EPHB4 Receptor Tyrosine Kinase Mutations and Kinomic Pathway Analysis in Lung Cancer

Ferguson

Tan

Kanteti

et al. 2015

Sci Rep

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Lung cancer outcomes remain poor despite the identification of several potential therapeutic targets. The EPHB4 receptor tyrosine kinase (RTK) has recently emerged as an oncogenic factor in many cancers, including lung cancer. Mutations of EPHB4 in lung cancers have previously been identified, though their significance remains unknown. Here, we report the identification of novel EPHB4 mutations that lead to putative structural alterations as well as increased cellular proliferation and motility. We also conducted a bioinformatic analysis of these mutations to demonstrate that they are mutually exclusive from other common RTK variants in lung cancer, that they correspond to analogous sites of other RTKs’ variations in cancers, and that they are predicted to be oncogenic based on biochemical, evolutionary, and domain-function constraints. Finally, we show that EPHB4 mutations can induce broad changes in the kinome signature of lung cancer cells. Taken together, these data illuminate the role of EPHB4 in lung cancer and further identify EPHB4 as a potentially important therapeutic target.

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“…CanPredict 12,13 was used to predict whether a non-synonymous variation would be deleterious to the structure and function of EPHB4 using biochemical, evolutionary, and domain-function constraints [14][15][16] . mCluster 17 was used to consolidate and visualize analogous sites of EPHB4 mutations within other TKD-containing proteins and determine whether mutations have been detected at these sites. PyMOL software (Schrödinger, Portland OR) was used to visualize mutation sites within the EPHB4 protein structure.…”

Section: Methodsmentioning

confidence: 99%