ELECTROANALYTICAL QUANTIFICATION OF INHIBITION IN MULTICOPPER OXIDASES

Abstract

The aim of this work is the electroanalytical quantification of chloride inhibition in multicopper oxidases (MCOs), primarily using one fungal and one bacterial bilirubin oxidase (BOD) named Magnaporthe oryzae BOD (MoBOD) and Bacillus pumilus BOD (BpBOD), respectively. The extensive use of MCOs as biocatalysts in a wide range of biotechnological applications whose catalytic activity, however, is often interrupted by the presence of halide ions. In addition, the possible interaction between the metal centre of the enzyme with the nature of the buffer leads to a present investigation of MoBOD purified in phosphate (phosphate MoBOD) and 3-(N-morpholino)propanesulfonic acid (MOPS MoBOD), and of BpBOD purified in borate (borate BpBOD) and 3-(cyclohexylamino)-2-hydroxy-1-propanesulfonic acid (CAPSO BpBOD). Therefore, the modes of chloride inhibition/activation upon the O2 reduction reaction in phosphate MoBOD, MOPS MoBOD, borate BpBOD and CAPSO BpBOD are particularly studied using the protein film electrochemistry technique and are fit to a model with non-linear regression method in a variety of temperatures, O2 concentrations, and chloride concentrations. phosphate MoBOD and MOPS MoBOD both favour uncompetitive inhibition mode while BpBOD and CAPSO BpBOD unexpectedly positively affect the enzyme catalytic activity which is modelled based on enzyme-modifier taxonomy. In addition, the reaction and inhibition parameters are studied in a potential-dependent manner and their relationship with the observed electrocatalytic waveshapes is also simulated based on interfacial electron transfer methods under four different electron transfer models. Moreover, albeit not included in this thesis, I manage to co-develop a Python code to automate the data processing and construct the light source with multiple wavelengths for protein film photoelectrochemistry work. This thesis is presented based on the Journal Format set by The University of Manchester where the main results chapters are written in a format fit for submission with the addition of theory and general methodology sections and a published review paper that serves as the background for this work.

Date of Award	31 Dec 2023
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Lu Shin Wong (Supervisor) & Christopher Blanford (Supervisor)