Kyle Le Huray

Investigation into the determinants of protein-lipid interactions using molecular dynamics simulations, machine learning and mass spectrometry

About me

In 2018, I completed an integrated masters degree in chemistry with pharmaceutical chemistry at Heriot-Watt University. During my studies I developed strong interests in physical chemistry, biophysics and computer science and the overlap of these subjects at the cutting edge of biochemical research. For my masters project, I used voltage clamp electrophysiology to study silver nanoparticle interactions with human cell membranes, and after harpooning hundreds of cells with microelectrodes I won the prize for the best pharmaceutical research project in my year. I knew that I wanted to undertake a PhD which combined biomolecular simulations with either machine learning or physical experiments, and I knew that the right project would be hard to find. So I worked as a bicycle courier (great fun but dangerous and cold) for a year while self-studying and waiting for the right research opportunity. While I do miss life on two wheels, I am delighted to now be working on my dream project in the labs of Antreas Kalli, Frank Sobott and He Wang at the University of Leeds. This multidisciplinary project combines molecular dynamics simulations, machine learning and mass spectrometry to study protein-lipid interactions.

My project

Lipids are the essential components of cell membranes and the role of protein-lipid interactions in modulating the activity and stability of membrane proteins is gaining recognition. Despite fast-growing data that describe these interactions, the molecular and chemical details of the interactions of most membrane proteins with their lipid environment remain elusive. Molecular dynamics simulations have recently proven to be a powerful tool for the identification of lipid binding sites and the study of lipid-protein interactions. During this project, I aim to simulate the interactions of entire families of peripheral and integral membrane proteins with membrane bilayers, providing unprecedented comparative insight into the lipid interactions within and between the families. Machine learning will be used to learn the interactions, identify patterns and to attempt to make sequence-based predictions about lipid interactions for proteins in the families for which the structure is unknown. Native mass spectrometry will be used to validate predictions made by the computational methods and to provide complementary insights into the protein-lipid interactions