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.
Highlights
Problem and approachThe functions of most sequenced proteins have not been determined by experiment (Gerlt et al, 2011; Jacobson et al, 2014; Schnoes et al, 2009)
For the gulonate pathway, we identified five metabolic enzymes that are conserved in the genome neighborhood of the TRAP transporter gene by constructing a genome neighborhood network (GNN) (Figure 1—figure supplement 2); the GNN approach has been demonstrated to accurately predict enzymes and transporters that function together in metabolic pathways based on conserved protein families in genome neighborhoods across different species (Zhao et al, 2014)
With the top 500 metabolites docked-andranked against each of the enzymes, the pathway enzymes may be linked by the similarity of their high-ranking docked ligands, here using the chemoinformatic Similarity Ensemble Approach (SEA) (Keiser et al, 2007; Lin et al, 2013); other related approaches can be used (Besnard et al, 2012; Gregori-Puigjaneand Mestres, 2006; Mestres et al, 2006; Nidhi et al, 2006; Paolini et al, 2006)
Summary
The functions of most sequenced proteins have not been determined by experiment (Gerlt et al, 2011; Jacobson et al, 2014; Schnoes et al, 2009). They are difficult to predict for enzymes with less than 60% sequence identity to characterized enzymes (Radivojac et al, 2013). The problem is much greater when seeking to predict the functions of entire metabolic pathways.
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