Format

Send to

Choose Destination
Nat Biotechnol. 2018 Mar;36(3):272-281. doi: 10.1038/nbt.4072. Epub 2018 Feb 19.

Recon3D enables a three-dimensional view of gene variation in human metabolism.

Author information

1
Department of Bioengineering, University of California, San Diego, San Diego,California, USA.
2
The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark.
3
Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-Sur-Alzette, Luxembourg.
4
RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, California, USA.
5
Department of Computer Science, Arizona State University, Tempe, AZ 85281, Arizona, USA.
6
Applied Bioinformatics Group, Center for Bioinformatics Tübingen (ZBIT), University of Tübingen, Tübingen, Germany.
7
Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg,Sweden.
8
Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Institute for Quantitative Biomedicine, and Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA.
9
Division of Analytical Biosciences, Leiden Academic Centre for Drug Research, Faculty of Science, University of Leiden, Leiden, the Netherlands.
10
Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.

Abstract

Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life.

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for PubMed Central
Loading ...
Support Center