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Genome Med. 2017 Dec 18;9(1):113. doi: 10.1186/s13073-017-0509-y.

Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: a proposed framework.

Author information

1
Institute for Systems Biology, Seattle, WA, 98109, USA. Gustavo@SystemsBiology.org.
2
San Diego Supercomputer Center, University of California San Diego, La Jolla, CA, 98093, USA.
3
RCSB Protein Data Bank, University of California San Diego, La Jolla, CA, 98093, USA.
4
Institute for Systems Biology, Seattle, WA, 98109, USA.
5
Human Centered Design & Engineering, University of Washington, Seattle, WA, 98195, USA.
6
Division of Computational Biology, School of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK.
7
Department of Molecular Biology and Genetics, Aarhus University, 8000, Aarhus, Denmark.
8
Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, 98109, USA.
9
University of California San Diego, La Jolla, CA, 92093, USA.
10
Institute of Molecular Systems Biology, ETH Zurich, CH-8093, Zurich, Switzerland.
11
Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
12
Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
13
SBP Medical Discovery Institute, La Jolla, CA, 92037, USA.
14
AMPLab, University of California, Berkeley, CA, 94720, USA.
15
Human Longevity, Inc, San Diego, CA, 92121, USA.
16
Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, 21218, USA.
17
Department of Oncology, Johns Hopkins Medicine, Baltimore, MD, 21287, USA.
18
Flatiron Institute, Center for Computational Biology, Simons Foundation, New York, NY, 10010, USA.
19
Department of Biology and Center for Genomics and Systems Biology, New York University, New York, NY, 10003, USA.
20
SIB Swiss Institute of Bioinformatics and University of Geneva, CH-1211, Geneva, Switzerland.
21
Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD, 20850, USA.
22
Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, 20742, USA.
23
Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, 48109-2218, USA.
24
School of Life Sciences, University of Sussex, Brighton, BN1 9QG, UK.
25
The University of Washington eScience Institute, Seattle, WA, 98195, USA.
26
SIB Swiss Institute of Bioinformatics and Biozentrum University of Basel, CH-4056, Basel, Switzerland.
27
Brain and Mind Research Institute, Weill Cornell Medicine, New York City, NY, 10021, USA.

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.

PMID:
29254494
PMCID:
PMC5735928
DOI:
10.1186/s13073-017-0509-y
[Indexed for MEDLINE]
Free PMC Article

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