dSysMap: exploring the edgetic role of disease mutations

Mosca, Roberto; Tenorio-Laranga, Jofre; Olivella, Roger; Alcalde, Víctor; Céol, Arnaud; Soler-López, Montserrat; Aloy, Patrick

doi:10.1038/nmeth.3289

Cited by 91 publications

(75 citation statements)

References 5 publications

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“…Several studies have established that alteration of protein interactions plays a key role in many diseases (16,(38)(39)(40)(41)(42), including cancer (9,16,43). We tested for enrichment of missense mutations at known interfaces (inferred from structurally resolved complexes, such as cocrystals, from the PDB) of proteins, mediating interactions with other proteins, small molecule or ion ligands, DNA, and RNA.…”

Section: Mutation Clustering At Biomolecular Interaction Interfaces Mmentioning

confidence: 99%

Comprehensive assessment of cancer missense mutation clustering in protein structures

Kamburov

Lawrence

Polak

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

204

226

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Large-scale tumor sequencing projects enabled the identification of many new cancer gene candidates through computational approaches. Here, we describe a general method to detect cancer genes based on significant 3D clustering of mutations relative to the structure of the encoded protein products. The approach can also be used to search for proteins with an enrichment of mutations at binding interfaces with a protein, nucleic acid, or small molecule partner. We applied this approach to systematically analyze the PanCancer compendium of somatic mutations from 4,742 tumors relative to all known 3D structures of human proteins in the Protein Data Bank. We detected significant 3D clustering of missense mutations in several previously known oncoproteins including HRAS, EGFR, and PIK3CA. Although clustering of missense mutations is often regarded as a hallmark of oncoproteins, we observed that a number of tumor suppressors, including FBXW7, VHL, and STK11, also showed such clustering. Beside these known cases, we also identified significant 3D clustering of missense mutations in NUF2, which encodes a component of the kinetochore, that could affect chromosome segregation and lead to aneuploidy. Analysis of interaction interfaces revealed enrichment of mutations in the interfaces between FBXW7-CCNE1, HRAS-RASA1, CUL4B-CAND1, OGT-HCFC1, PPP2R1A-PPP2R5C/PPP2R2A, DICER1-Mg 2+ , MAX-DNA, SRSF2-RNA, and others. Together, our results indicate that systematic consideration of 3D structure can assist in the identification of cancer genes and in the understanding of the functional role of their mutations.cancer | cancer genetics | mutation clustering | protein structures | interaction interfaces

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Section: Mutation Clustering At Biomolecular Interaction Interfaces Mmentioning

confidence: 99%

Comprehensive assessment of cancer missense mutation clustering in protein structures

Kamburov

Lawrence

Polak

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

204

226

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show abstract

“…Edgetic alterations resulting from genetic variants can be identified experimentally or predicted by high-resolution, three-dimensional models of protein structure and protein–protein interactions (FIG. 1a) 66 . This concept is helpful to associate specific alterations in protein interactions with disease states.…”

Section: Molecular Interactions In Admentioning

confidence: 99%

Genetic variants in Alzheimer disease — molecular and brain network approaches

et al. 2016

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Genetic studies in late-onset Alzheimer disease (LOAD) are aimed at identifying core disease mechanisms and providing potential biomarkers and drug candidates to improve clinical care for AD. However, due to the complexity of LOAD, including pathological heterogeneity and disease polygenicity, extracting actionable guidance from LOAD genetics has been challenging. Past attempts to summarize the effects of LOAD-associated genetic variants have used pathway analysis and collections of small-scale experiments to hypothesize functional convergence across several variants. In this review, we discuss how the study of molecular, cellular and brain networks provides additional information on the effect of LOAD-associated genetic variants. We then discuss emerging combinations of omic data types in multiscale models, which provide a more comprehensive representation of the effect of LOAD-associated genetic variants at multiple biophysical scales. Further, we highlight the clinical potential of mechanistically coupling genetic variants and disease phenotypes with multiscale brain models.

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“…Cancer3D is a user-friendly database to analyze somatic missense mutations in the context of protein 3D structures [18]. Mosca et al developed dSysMap, a resource for the systematic mapping of disease-related missense mutations on the structurally annotated binary human interactome [19].…”

Section: Ngs Data Resourcesmentioning

confidence: 99%

Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes

Cheng¹,

Zhao²,

Zhao³

2015

Brief Bioinform

133

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Cancer is often driven by the accumulation of genetic alterations, including single nucleotide variants, small insertions or deletions, gene fusions, copy-number variations, and large chromosomal rearrangements. Recent advances in nextgeneration sequencing technologies have helped investigators generate massive amounts of cancer genomic data and catalog somatic mutations in both common and rare cancer types. So far, the somatic mutation landscapes and signatures of >10 major cancer types have been reported; however, pinpointing driver mutations and cancer genes from millions of available cancer somatic mutations remains a monumental challenge. To tackle this important task, many methods and computational tools have been developed during the past several years and, thus, a review of its advances is urgently needed. Here, we first summarize the main features of these methods and tools for whole-exome, whole-genome and wholetranscriptome sequencing data. Then, we discuss major challenges like tumor intra-heterogeneity, tumor sample saturation and functionality of synonymous mutations in cancer, all of which may result in false-positive discoveries. Finally, we highlight new directions in studying regulatory roles of noncoding somatic mutations and quantitatively measuring circulating tumor DNA in cancer. This review may help investigators find an appropriate tool for detecting potential driver or actionable mutations in rapidly emerging precision cancer medicine.

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dSysMap: exploring the edgetic role of disease mutations

Cited by 91 publications

References 5 publications

Comprehensive assessment of cancer missense mutation clustering in protein structures

Comprehensive assessment of cancer missense mutation clustering in protein structures

Genetic variants in Alzheimer disease — molecular and brain network approaches

Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes

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