Mechismo: predicting the mechanistic impact of mutations and modifications on molecular interactions

Betts, Matthew J.; Lu, Qianhao; Drusko, Armin; Wichmann, Oliver; Utz, Mathias; Valtierra-Gutiérrez, Ilse A.; Schlesner, Matthias; Jaeger, Natalie; Jones, David; Pfister, Stefan M.; Lichter, Peter; Eils, Roland; Siebert, Reiner; Bork, Peer; Apic, Gordana; Gavin, Anne‐Claude; Russell, Robert B.

doi:10.1093/nar/gku1094

Cited by 86 publications

(86 citation statements)

References 63 publications

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“…For example, MuPIT 7 , an extension of LS-SNP/PDB 8 , maps sequence variants onto protein structures, Interactome3D 9 annotates protein-protein interactions with structural details, other web tools 10–12 map and visualize variants on protein structures, SpacePAC 13 identifies mutation clusters via simulation, CLUMPS 14 clusters cancer genes and examines protein-protein interactions where at least one protein is known to be cancer related, and Mechismo identifies interaction sites contributing to the binding forces between proteins and other peptides 15 . However, no system yet provides comprehensive analysis for understanding mutational consequences or implications for drug delivery.…”

Section: Introductionmentioning

confidence: 99%

Protein-structure-guided discovery of functional mutations across 19 cancer types

et al. 2016

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Local concentrations of mutations are well-known in human cancers. However, their 3-dimensional (3D) spatial relationships have yet to be systematically explored. We developed a computational tool, HotSpot3D, to identify such spatial hotspots (clusters) and to interpret the potential function of variants within them. We applied HotSpot3D to >4,400 TCGA tumors across 19 cancer types, discovering >6,000 intra- and inter-molecular clusters, some of which showed tumor/tissue specificity. In addition, we identified 369 rare mutations from genes including TP53, PTEN, VHL, EGFR, and FBXW7 and 99 medium recurrence mutations from genes such as RUNX1, MTOR, CA3, PI3, and PTPN11, all residing within clusters having potential functional implications. As a proof of concept, we validated our predictions in EGFR using high throughput phosphorylation data and cell-line based experimental evaluation. Finally, drug-mutation cluster/network analysis predicted over 800 promising candidates of druggable mutations, raising new possibilities for designing personalized treatments for patients carrying specific mutations.

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Section: Introductionmentioning

confidence: 99%

Protein-structure-guided discovery of functional mutations across 19 cancer types

et al. 2016

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“…Sift (http://sift.jcvi.org/, (Kumar et al, 2009), PolyPhen (http://genetics.bwh.harvard.edu/pph2/, (Adzhubei et al, 2013), Mechismo (http://mechismo.russelllab.org/, (Betts et al, 2015) and the umd-predictor (http://umd-predictor.eu, (Beroud et al, 2000) were used to predict the potential effect of mutations detected applying the TruSight Sequencing Panel. Targeted validation sequencing of recurrently mutated genes (≥2 cases) was performed by Sanger sequencing as described above (Table SIII).…”

Section: Mutational Analysismentioning

confidence: 99%

Genes encoding members of the JAK‐STAT pathway or epigenetic regulators are recurrently mutated in T‐cell prolymphocytic leukaemia

et al. 2016

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T-cell prolymphocytic leukaemia (T-PLL) is an aggressive leukaemia. The primary genetic alteration in T-PLL are the inv(14)(q11q32)/t(14;14)(q11;q32) leading to TRD/TRA-TCL1A fusion, or the t(X;14)(q28;q11) associated with TRD/TRA-MTCP1 fusion. However, additional cooperating abnormalities are necessary for emergence of the full neoplastic phenotype. Though the pattern of secondary chromosomal aberrations is remarkably conserved, targets of the changes are largely unknown. We analysed a cohort of 43 well-characterized T-PLL for hotspot mutations in the genes JAK3, STAT5B and RHOA. Additionally, we selected a subset of 23 T-PLL cases for mutational screening of 54 genes known to be recurrently mutated in T-cell and other haematological neoplasms. Activating mutations in the investigated regions of the JAK3 and STAT5B genes were detected in 30% (13/43) and 21% (8/39) of the cases, respectively, and were mutually exclusive. Further, we identified mutations in the genes encoding the epigenetic regulators EZH2 in 13% (3/23), TET2 in 17% (4/23) and BCOR in 9% (2/23) of the cases. We confirmed that the JAK-STAT pathway is a major mutational target, and identified epigenetic regulators recurrently mutated in T-PLL. These findings complement the mutational spectrum of secondary aberrations in T-PLL and underscore the potential therapeutical relevance of epigenetic regulators in T-PLL.

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“…If that were the case, since all the cancer samples with mutations in the disordered region of CDH11 have higher levels of immune infiltrate, the mutations should be strengthening the CDH11/CTNNB1 interaction. To test this hypothesis, we used MECHISMO(34) to predict the consequences of six different CDH11 mutations. MECHISMO is a method that, among other things, uses an empirical score of how likely it is for two amino acids to be together in an interface, to estimate the effect of a specific mutation.…”

Section: Resultsmentioning

confidence: 99%

Mutation Drivers of Immunological Responses to Cancer

Porta-Pardo

Godzik

2016

Cancer Immunology Research

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In cancer immunology, somatic missense mutations have been mostly studied regarding their role in the generation of neoantigens. However, growing evidence suggests that mutations in certain genes, such as CASP8 or TP53, influence the immune response against a tumor by other mechanisms. Identifying these genes and mechanisms is important because, just as the identification of cancer driver genes led to the development of personalized cancer therapies, a comprehensive catalog of such cancer immunity drivers will aid in the development of therapies aimed at restoring antitumor immunity. Here we present an algorithm, domainXplorer, that can be used to identify potential cancer immunity drivers. To demonstrate its potential, we used it to analyze a dataset of 5,164 tumor samples from TCGA and to identify protein domains whose mutation status correlates with the presence of immune cells in cancer tissue (immune infiltrate). We identified 122 such protein regions including several that belong to proteins with known roles in immune response, such as C2, CD163L1, or FCγR2A. In several cases we show that mutations within the same protein can be associated with more or less immune cell infiltration, depending on the specific domain mutated. These results expand the catalog of potential cancer immunity drivers and highlight the importance of taking into account the structural context of somatic mutations when analyzing their potential association with immune phenotypes.

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Mechismo: predicting the mechanistic impact of mutations and modifications on molecular interactions

Cited by 86 publications

References 63 publications

Protein-structure-guided discovery of functional mutations across 19 cancer types

Protein-structure-guided discovery of functional mutations across 19 cancer types

Genes encoding members of the JAK‐STAT pathway or epigenetic regulators are recurrently mutated in T‐cell prolymphocytic leukaemia

Mutation Drivers of Immunological Responses to Cancer

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