ABSTRACT
The eradication of Malaria is threatened by the emergence of drug resistance to many of our front-line treatments. These challenges require the rethinking of our approaches to drug design and application. In my PhD, I investigated if recent advances in high-powered computing, biophysical simulations, and AI-based structure prediction can rise to the challenges posed by multidrug resistant Plasmodium falciparum.

Specifically, my thesis focused on three contemporary problems: 1. How is the polyspecific peptide transporter, PfCRT, able to transport such a wide variety of compounds and how do resistance mutations contribute to fitness trade-offs? 2. How does the G358S mutation in the sodium exporter, PfATP4, contribute to Cipargamin resistance and can we predict its effect on other antiplasmodial compounds? 3. Can large, proteome-scale structure prediction by Alphafold2 provide a basis for accurate small-molecule docking, particularly for neglected-disease causing organisms like P.falciparum?

Through the use of Molecular dynamics simulations and docking, we show the power of these computational approaches to rationalise resistance mechanisms. We demonstrate how the charged residue K76, the primary chloroquine resistance marker loci, is essential to stabilising PfCRT’s diverse peptide binding modes, and how its removal allows chloroquine to bind and enter the protein’s cavity. We also make progress in detailing the binding modes of a number of other drugs that are transported by PfCRT. To aid in understanding the mechanism of Cipargmain resistance, we demonstrate how the G358S mutation in PfATP4, which Cipargamin targets, creates a steric block to its binding site.

We demonstrated the fickle nature of molecular docking in the case of predicting the effects of the G358S on a series of other PfATP4 inhibitors. Further to this, we found that structures predicted by Alphafold2 were in most cases not appropriate starting points for use in molecular docking, and there is a challenge in extrapolating results beyond their validation sets.

We make key insights into antimalarial resistance mechanisms and delineate the utility of cutting-edge computational approaches that will go on to inform future drug design strategies and efforts.