Prediction of substrate binding geometry and strength in promiscuous enzymes: computational chemistry applicable to the design of pharmaceuticals

Ourselves and others have developed computational methods that are applicable to two key states involved in enzyme activity and inhibition: the reversible substrate/inhibitor complex and reaction transition states in the active site. The Quantum Cluster method and related theozyme method have been used to quantify and understand the origins of differences between enzyme-substrate and enzyme-Transition State binding.

This difference is critical for understanding the kcat that is achieved by enzymes. The Leach group has applied the related theoceptor method to understand and predict binding strength of inhibitors that bind and block enzymes. A significant recent development permits us to perform these latter calculations at massively higher throughput (thousands of calculations per week rather than ~10).

In this project, we aim to unify these two approaches in order to develop methods for computing quantitative properties relating to all steps of an enzymatic reaction. We will use a number of promiscuous enzymes to drive this project forward. Objectives to achieve this aim will be: 1) Develop model of CYP 1A2.

2) Extend to other CYP isoforms. 3) Develop models for a range of glutathione S-transferases. 4) Apply methods to enzymes in the RetroBioCat system. 5) Establish consistent features of substrate binding.

Methods: The calculations will employ our recently modified version of the theoceptor approach in which constrained geometry optimisations are performed using Grimme's GFN2-xTB method. The geometries and energies obtained can then be used directly or else higher level DFT calculations can be initiated in either ORCA or Gaussian. All tools are available via the CSF and any code developed during the project will be made available via GitHub.

Outcomes from the project will be: 1) predictive models for substrate affinity to key CYP enzymes involved in metabolism; this will be directly applicable widely across drug design in academic and industrial settings. 2) similar models for the GSTs; this will be applicable to drug design and also to the design of agrochemicals. 3) General principles underpinning substrate recognition by promiscuous enzymes.

Interdisciplinary training: 1) Informatic studies to collate and understand the available structural and binding data for the CYP and GST enzymes will be undertaken in the Leach group. An introduction to methods such as QSAR and matched molecular pair analysis will also be developed, particularly during the placement with Medchemica Ltd. 2) The student will learn how to prepare, conduct and analyse a molecular dynamics simulation of GST and two substrates, providing skills useful for analysing how the substrates influence one another's binding geometry and energy. 3) The student will learn about planning and performing synthesis of molecules for biological study to help understand how synthesis planning tools like RetroBioCat can be applied.