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ACS Chem Biol. 2018 Jan 19;13(1):225-234. doi: 10.1021/acschembio.7b00996. Epub 2017 Dec 20.

Determinants and Prediction of Esterase Substrate Promiscuity Patterns.

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Institute of Catalysis, Consejo Superior de Investigaciones Científicas , 28049 Madrid, Spain.
Barcelona Supercomputing Center (BSC) , 08034 Barcelona, Spain.
Biozentrum Klein Flottbek, Mikrobiologie & Biotechnologie, Universität Hamburg , 22609 Hamburg, Germany.
Department of Chemical Engineering and Applied Chemistry, University of Toronto , M5S 3E5 Toronto, Ontario, Canada.
School of Biological Sciences, Bangor University , LL57 2UW Bangor, United Kingdom.
Institut für Molekulare Enzymtechnologie, Heinrich-Heine-Universität Düsseldorf , 52425 Jülich, Germany.
Department of Functional Biology-IUBA, Universidad de Oviedo , 33006 Oviedo, Spain.
Uni Research AS, Center for Applied Biotechnology , 5006 Bergen, Norway.
Department of Biology and KG Jebsen Centre for Deep Sea Research, University of Bergen , 5020 Bergen, Norway.
Structural Biology Center, Biosciences Division, Argonne National Laboratory , Argonne, 60439 Illinois, United States.
Institute of Biochemistry and Technical Biochemistry, University of Stuttgart , 70569 Stuttgart, Germany.
Jacobs University Bremen gGmbH , Bremen, Germany.
Max Planck Institute for Marine Microbiology , 28359 Bremen, Germany.
University of Oxford , Oxford e-Research Centre, Oxford, United Kingdom.
Institute for Coastal Marine Environment , Consiglio Nazionale delle Ricerche, 98122 Messina, Italy.
Immanuel Kant Baltic Federal University , 236041 Kaliningrad, Russia.
Institute for Bio- and Geosciences IBG-1: Biotechnology, Forschunsgzentrum Jülich GmbH , 52425 Jülich, Germany.
Institució Catalana de Recerca i Estudis Avançats (ICREA) , 08010 Barcelona, Spain.


Esterases receive special attention because of their wide distribution in biological systems and environments and their importance for physiology and chemical synthesis. The prediction of esterases' substrate promiscuity level from sequence data and the molecular reasons why certain such enzymes are more promiscuous than others remain to be elucidated. This limits the surveillance of the sequence space for esterases potentially leading to new versatile biocatalysts and new insights into their role in cellular function. Here, we performed an extensive analysis of the substrate spectra of 145 phylogenetically and environmentally diverse microbial esterases, when tested with 96 diverse esters. We determined the primary factors shaping their substrate range by analyzing substrate range patterns in combination with structural analysis and protein-ligand simulations. We found a structural parameter that helps rank (classify) the promiscuity level of esterases from sequence data at 94% accuracy. This parameter, the active site effective volume, exemplifies the topology of the catalytic environment by measuring the active site cavity volume corrected by the relative solvent accessible surface area (SASA) of the catalytic triad. Sequences encoding esterases with active site effective volumes (cavity volume/SASA) above a threshold show greater substrate spectra, which can be further extended in combination with phylogenetic data. This measure provides also a valuable tool for interrogating substrates capable of being converted. This measure, found to be transferred to phosphatases of the haloalkanoic acid dehalogenase superfamily and possibly other enzymatic systems, represents a powerful tool for low-cost bioprospecting for esterases with broad substrate ranges, in large scale sequence data sets.

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