Prediction of enzymatic pathways by integrative pathway mapping

Sara Calhoun; Magdalena Korczynska; Daniel J Wichelecki; Brian San Francisco; Suwen Zhao; Dmitry A Rodionov; Matthew W Vetting; Nawar F Al-Obaidi; Henry Lin; Matthew J O'Meara; David A Scott; John H Morris; Daniel Russel; Steven C Almo; Andrei L Osterman; John A Gerlt; Matthew P Jacobson; Brian K Shoichet; Andrej Sali

Prediction of enzymatic pathways by integrative pathway mapping

2018

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.

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