Review

. 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

Gustavo Glusman¹, Peter W Rose², Andreas Prlić^{2

3}, Jennifer Dougherty⁴, José M Duarte³, Andrew S Hoffman⁵, Geoffrey J Barton⁶, Emøke Bendixen⁷, Timothy Bergquist⁸, Christian Bock⁸, Elizabeth Brunk⁹, Marija Buljan¹⁰, Stephen K Burley^{2

3

11}, Binghuang Cai⁸, Hannah Carter⁹, JianJiong Gao¹², Adam Godzik¹³, Michael Heuer¹⁴, Michael Hicks¹⁵, Thomas Hrabe¹³, Rachel Karchin^{16

17}, Julia Koehler Leman^{18

19}, Lydie Lane²⁰, David L Masica¹⁶, Sean D Mooney⁸, John Moult^{21

22}, Gilbert S Omenn^{4

23}, Frances Pearl²⁴, Vikas Pejaver^{8

25}, Sheila M Reynolds⁴, Ariel Rokem²⁵, Torsten Schwede²⁶, Sicheng Song⁸, Hagen Tilgner²⁷, Yana Valasatava³, Yang Zhang²³, Eric W Deutsch⁴

Affiliations

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.

PMID: 29254494
PMCID: PMC5735928
DOI: 10.1186/s13073-017-0509-y

Review

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

Gustavo Glusman et al. Genome Med. 2017.

. 2017 Dec 18;9(1):113.

doi: 10.1186/s13073-017-0509-y.

Authors

Affiliations

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.

PMID: 29254494
PMCID: PMC5735928
DOI: 10.1186/s13073-017-0509-y

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.

PubMed Disclaimer

Conflict of interest statement

Competing interests

GG holds stock options in Arivale, Inc. Arivale, Inc. did not fund the study and was not involved in its design, implementation, or reporting. MH is an employee at Human Longevity, Inc. The other authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

**Fig. 1**
Components of the GVto3D portal. The Tools Registry contains a searchable description and metadata for tools, resources, and reference data sets for third-party variant effect prediction and annotation services. Standardized application programming interfaces (*APIs*) provide interoperability for data input and output of these third-party tools. Custom adapters can provide limited interoperability for tools that cannot adopt the API. A mapping service provides bidirectional mappings from reference genome coordinates to UniProt protein positions and to Protein Data Bank (*PDB*) residue positions. The tools can use the mapping service to accept variant positions in any of the three coordinate systems. A beacon system enables queries about variant positions where three-dimensional (3D) structural information and annotation are available

See this image and copyright information in PMC

References

1. Glusman G. Clinical applications of sequencing take center stage. Genome Biol. 2013;14:303. doi: 10.1186/gb-2013-14-3-303. - DOI - PMC - PubMed
1. Auffray C, Chen Z, Hood L. Systems medicine: the future of medical genomics and healthcare. Genome Med. 2009;1:2. doi: 10.1186/gm2. - DOI - PMC - PubMed
1. Price ND, Magis AT, Earls JC, Glusman G, Levy R, Lausted C, et al. A wellness study of 108 individuals using personal, dense, dynamic data clouds. Nat Biotechnol. 2017;35:747–56. doi: 10.1038/nbt.3870. - DOI - PMC - PubMed
1. Katsonis P, Koire A, Wilson SJ, Hsu T-K, Lua RC, Wilkins AD, et al. Single nucleotide variations: biological impact and theoretical interpretation. Protein Sci Publ Protein Soc. 2014;23:1650–66. doi: 10.1002/pro.2552. - DOI - PMC - PubMed
1. Kassahn KS, Scott HS, Caramins MC. Integrating massively parallel sequencing into diagnostic workflows and managing the annotation and clinical interpretation challenge. Hum Mutat. 2014;35:413–23. doi: 10.1002/humu.22525. - DOI - PubMed

Publication types

Actions

MeSH terms

Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions

Grants and funding

LinkOut - more resources

Full Text Sources
Other Literature Sources
- The Lens - Patent Citations Database
- scite Smart Citations

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

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

Affiliations

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

Authors

Affiliations

Abstract

Conflict of interest statement

Competing interests

Publisher’s Note

Figures

References

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

Other Literature Sources