Format

Send to

Choose Destination
Elife. 2018 Jan 29;7. pii: e31097. doi: 10.7554/eLife.31097.

Prediction of enzymatic pathways by integrative pathway mapping.

Author information

1
Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, United States.
2
Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, United States.
3
Institute for Genomic Biology, University of Illinois, Urbana, United States.
4
Department of Biochemistry, University of Illinois, Urbana, United States.
5
Department of Chemistry, University of Illinois, Urbana, United States.
6
Sanford Burnham Prebys Medical Discovery Institute, La Jolla, United States.
7
A.A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia.
8
Department of Biochemistry, Albert Einstein College of Medicine, New York, United States.
9
Resource for Biocomputing, Visualization and Informatics, Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, United States.
10
California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, United States.
#
Contributed equally

Abstract

The functions of most proteins are yet to be determined. The function of an enzyme is often defined by its interacting partners, including its substrate and product, and its role in larger metabolic networks. Here, we describe a computational method that predicts the functions of orphan enzymes by organizing them into a linear metabolic pathway. Given candidate enzyme and metabolite pathway members, this aim is achieved by finding those pathways that satisfy structural and network restraints implied by varied input information, including that from virtual screening, chemoinformatics, genomic context analysis, and ligand -binding experiments. We demonstrate this integrative pathway mapping method by predicting the L-gulonate catabolic pathway in Haemophilus influenzae Rd KW20. The prediction was subsequently validated experimentally by enzymology, crystallography, and metabolomics. Integrative pathway mapping by satisfaction of structural and network restraints is extensible to molecular networks in general and thus formally bridges the gap between structural biology and systems biology.

KEYWORDS:

biophysics; computational biology; enzyme function annotation; integrative pathway mapping; l-gulonate catabolic pathway; none; pathway prediction; structural biology; structure based pathway discovery; systems biology

PMID:
29377793
PMCID:
PMC5788505
DOI:
10.7554/eLife.31097
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for eLife Sciences Publications, Ltd Icon for PubMed Central
Loading ...
Support Center