Genomic Science Program
U.S. Department of Energy | Office of Science | Biological and Environmental Research Program

Understanding the Molecular Rules of Transporter Specificity to Engineer Biofuel-Relevant Efflux Pumps

Authors:

Silas Miller* (stmiller2@wisc.edu); Srivatsan Raman; Timothy J. Donohue

Institutions:

University of Wisconsin–Madison

Goals

Researchers used deep mutational scanning to learn the molecular rules of specificity for a multidrug efflux pump. Next, the team will apply these rules to engineer transporters to efflux and confer resistance to toxins found in lignocellulosic hydrolysates, a major barrier to efficient biofuel production.

Abstract

Transporter engineering offers the ability to precisely control which molecules remain in a cell. This could greatly improve the cost and efficiency of microbial biofuel production, for example by exporting biofuel end products for easier recovery, removing reaction byproducts to prevent toxic buildup, and conferring efflux-mediated resistance to lignocellulosic hydrolysate (LCH) inhibitors. LCH inhibitors can reduce the efficiency and yield of microbial biofuel production and are costly to remove from pretreated plant biomass. Researchers aim to engineer LCH inhibitor efflux pumps using bacterial multidrug resistance (MDR) transporters as a platform. In doing so, researchers will learn design rules for engineering of other biofuel-relevant transporters. To achieve this, Researchers must first understand the sequence determinants of transport specificity: how does each residue contribute to efflux of different types of substrates? To this end, researchers have used deep mutational scanning to characterize all single missense mutants of a bacterial MDR transporter in the context of several toxic molecules, including LCH inhibitors.

Exposure of a variant library to toxic transporter substrates results in enrichment of active variants when analyzed by deep sequencing. Researchers have identified general and substrate-specific functional hotspots and gain-of-function pathways using this method. Next, the team will use this data in combination with machine learning protein design techniques to design LCH inhibitor efflux pumps.

Funding Information

This material is based upon work supported in part by the Great Lakes Bioenergy Research Center, DOE, Office of Science, BER program under Award Number DE-SC0018409.