Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: a proposed framework
Year: 2017
Type: article
Abstract: The translation of personal genomics to precision medicine depends on the accurate interpretation of the multitude of genetic variants observed for each individual. However, even when genetic variants are predicted to modify a protein, their functional implications may be unclear. Many diseases are caused by genetic variants affecting important protein features, such as enzyme active sites or interaction interfaces. The scientific community has catalogued millions of genetic variants in genomic databases and thousands of protein structures in the Protein Data Bank. Mapping mutations onto three-dimensional (3D) structures enables atomic-level analyses of protein positions that may be important for the stability or formation of interactions; these may explain the effect of mutations and in some cases even open a path for targeted drug development. To accelerate progress in the integration of these data types, we held a two-day Gene Variation to 3D (GVto3D) workshop to report on the latest advances and to discuss unmet needs. The overarching goal of the workshop was to address the question: what can be done together as a community to advance the integration of genetic variants and 3D protein structures that could not be done by a single investigator or laboratory? Here we describe the workshop outcomes, review the state of the field, and propose the development of a framework with which to promote progress in this arena. The framework will include a set of standard formats, common ontologies, a common application programming interface to enable interoperation of the resources, and a Tool Registry to make it easy to find and apply the tools to specific analysis problems. Interoperability will enable integration of diverse data sources and tools and collaborative development of variant effect prediction methods.
Source: Genome Medicine
Authors: Gustavo Glusman, Peter W. Rose, Andreas Prlić, Jennifer Dougherty, José M. Duarte, Andrew S. Hoffman, Geoffrey J. Barton, Emøke Bendixen, Timothy Bergquist, Christian Bock, Elizabeth Brunk, Marija Buljan, Stephen K. Burley, Binghuang Cai, Hannah Carter, JianJiong Gao, Adam Godzik, Michael Heuer, Michael Hicks, Thomas Hrabe, Rachel Karchin, Julia Koehler Leman, Lydie Lane, David L. Masica, Sean D. Mooney, John Moult, Gilbert S. Omenn, Frances Pearl, Vikas Pejaver, Sheila M. Reynolds, Ariel Rokem, Torsten Schwede, Sicheng Song, Hagen Tilgner, Yana Valasatava, Yang Zhang, Eric W. Deutsch
Institutions: Institute for Systems Biology, San Diego Supercomputer Center, University of California San Diego, University of Washington, University of Dundee, Aarhus University, ETH Zurich, Institute for Molecular Systems Biology, Rutgers, The State University of New Jersey, Memorial Sloan Kettering Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, University of California, Berkeley, Human Longevity (United States), Johns Hopkins University, Johns Hopkins Medicine, Johns Hopkins Hospital, Simons Foundation, Flatiron Institute, New York University, University of Geneva, SIB Swiss Institute of Bioinformatics, Institute for Bioscience and Biotechnology Research, University of Maryland, College Park, University of Michigan, University of Sussex, University of Basel, Weill Cornell Medicine
Language: English
Topic: Genomics and Rare Diseases
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,290
Funders: National Science Foundation, University of Washington, Johns Hopkins University, Universität Basel, Flatiron Health, York University, University of Sussex, Université de Genève, Swiss Institute of Bioinformatics, National Institutes of Health, Weill Cornell Medical College, Biotechnology and Biological Sciences Research Council, National Human Genome Research Institute, National Cancer Institute, National Center for Advancing Translational Sciences
Awards: G1942787488, G2588893951, G4102770861, BD Spokes: PLANNING: WEST: Collaborative: Increasing collaborations in proteogenomics applications of genetic data, G4416346435, G4453785545, G4583515593, G463876665, The Dundee Resource for Protein Structure Prediction and Sequence Analysis, G526700612, G6777264179, The Jalview Resource for Sequence Analysis and Annotation, BD Spokes: PLANNING: WEST: Collaborative: Increasing collaborations in proteogenomics applications of genetic data, G7782214537, G9000003249, G9000045693, Cancer Center Support Grant, The Jalview Resource for Sequence Analysis and Annotation - www.jalview.org
OpenAlex ID: W2779822641