Enzyme-based biosensors for plant wound detection

e92d1b75-6d87-488c-982a-6dbfd0793fd1

Closed

BBSRC
BBSRC
BBSRC
BBSRC
BBSRC

No funds listed.

Sept. 30, 2018

Sept. 30, 2022

Plants produce volatile chemicals when they are wounded by insects. This project aims to create a biosensor for benzyl cyanide (BnCN), which is produced when caterpillars attack the leaves of oilseed rape. The sensor will enable precision agriculture, for example, only using pesticides where they are needed. Lower pesticide use can also delay the evolution of pesticide resistance.

At the heart of the proposed sensor is a redox-active enzyme, nitrile reductase, that converts a nitrile into a primary amine. This basic transformation is carried out by the QueF enzyme, but it does not currently transform BnCN. We are creating mutants of QueF with the goal of expanding the range of reactants (substrates) that the enzyme can transform. Computational methods such as QM/MM will be used to guide the mutagenesis, with the goals of suggesting unexpected target sites outside the active site and predicting the electrochemical driving force that the transformation will produce. The most active enzymes will be immobilised on electrodes made of graphite or carbon nanomaterials such as nanotubes or graphene. These will be use to probe the reduction mechanism and to create prototype biosensors. We expect that these sensors will be tested in model plant systems by the end of the project. The nitrile-to-amine transformation is also valuable in organic synthesis. Successful engineering of the nitrile reductase will produce a selective catalyst for atom-efficient transformations that works in a more benign aqueous environment. It could replace harsh, non-selective transformations in organic solvents using LiAlH4 or precious metals like palladium.

This project will train the candidate in a wide range of transferrable skills: genetic engineering, protein expression, computational modelling, industrial biotechnology, surface engineering and enzyme electrochemistry. The supervision team consists of Sam P. de Visser, Nick J. Turner and Christopher F. Blanford. De Visser and his group will provide training in computational chemistry, specifically in how to model substrate-protein docking and how to produce quantum chemical models of the reaction mechanism and reduction potential. The Turner group will teach the molecular biology skills needed to express the protein and test its biocatalytic activity in solution. The Blanford group will provide training in protein electrochemistry and creation of prototype sensors. Field testing will be carried out in collaboration with researchers at the University of Sheffield's Department of Animal and Plant Sciences.