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IRB Barcelona's dSysMap Combines Mutation, Protein Structure Data to Improve Disease Studies


NEW YORK (GenomeWeb) – Researchers from the Spanish Institute for Research in Biomedicine (IRB Barcelona) have developed a free interactive tool called Disease-mutations Systemic Mapping (dSysMap), which combines mutation information with protein interaction data to provide researchers with a clearer picture of the functional changes that are associated with the development of complex diseases such as cancer and Alzheimer's.

According to a correspondence piece published last week in Nature Methods, the web-based resource currently contains more than 23,000 missense mutations mapped to over 2,000 proteins related to at least 2,800 disease phenotypes. DSysMap includes structural data for over 70 percent of the proteins it contains; and 61 percent of the proteins "are involved in at least one experimentally known binary interaction," the researchers wrote. Of the more than 14,000 mutations that can be mapped to protein structures in the system, at least 9,000 of these are surface mutations and about 23 percent of those map to an interaction interface, according to the paper. 

Being able to view and explore pathological mutations in a broader biological context can help clarify underlying genotype-phenotype relationships, the researchers wrote. A map that combines these two kinds of data can, for instance, make it possible to distinguish genetic changes that suppress gene products from those "that might modulate the way in which the protein interacts with its partners," they said.  

Changes in the mechanisms by which proteins interact with each other "provide a plausible explanation for some complex phenomena connecting, for instance, mutations related to the same phenotype found on different genes (locus heterogeneity) when they affect the same interaction interface," the researchers wrote. These changes can also explain how mutations on the same gene might cause different phenotypes; as well as connect molecular mechanisms of diseases to modes of inheritance, the paper states

Roberto Mosca, a staff scientist at the lab and the first author on the paper, told GenomeWeb that he and others in IRB-Barcelona's structural bioinformatics and network biology laboratory where the tool was developed have been working for some years on mapping mutations to protein structures on a small scale — typically involving one or two proteins —and then exploring how these changes impact protein-protein interactions.

As part of their efforts, they developed Interactome3D — described in a separate Nature Methods paper published in 2013 —which is a tool for modeling and structural annotation of protein-protein interactions and networks. At the time of the paper's publication, the tool offered information on over 12,000 protein-protein interactions from eight model organisms including Escherichia coli, yeast, and human proteins. The next logical step was to combine the mutation information with the protein data but to do it on a much larger scale than they had done in earlier studies, Mosca said. Combining these datasets would let researchers look not just at a single protein's effect on its immediate neighbors but would provide a much richer, systemic picture of the entire neighborhood of interactions and the impacts of genetic variants on those interactions, he said.

With dSysMap, users can visualize the networks for all human diseases contained in the group's database, or can input a list of proteins and build a network around that list. They can also interactively navigate through the network, viewing the positions of disease-related mutations on nodes and edges as well as on high-resolution structures of proteins and complexes.

Also, the tool includes a RESTful service that supports programmatic access to dSysMap, Mosca said. Currently, the system includes mutation information related to inherited diseases from repositories such as the Online Mendelian Inheritance in Man database and the team is also in the process of adding cancer-related mutation data to it, he said.

In a statement, Patrick Aloy, and one of the authors on the paper, described dSysMap as a "a hypothesis-generating system" that also provides "mechanistic details at the molecular level in order to better understand complex diseases of genetic origin." Because it places mutations within their biological context, it provides researchers with a more complete view of their effects, he added.

A potential use case for the solution would be the International Cancer Genome Consortium's Pan Cancer genomes project — which has indicated interest in using dSysMap. Different groups involved in the project could add genetic mutations that they discover in tumors to the tool "in order to gain a more global view of their effects on the biological processes involved and to formulate new hypotheses," Aloy said.

DSysMap's development was supported with grants from the EU's 7th Framework Program and the European Research Council.