Protein/lipid interactions: Determinants of lipid interactions with membrane proteins investigated by machine learning, molecular simulations and mass

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Background: Despite a fast-growing number of data that demonstrate that interactions of membrane proteins with lipids regulate their function (e.g. cholesterol or PIPs regulates the function of signaling receptors), the molecular/chemical details of such interactions remain elusive. Molecular dynamics simulations (MDS) have become an established technique for predicting protein/lipid interactions but they are too computationally prohibitive. Additionally, it is often challenging to use lab-based methodologies to study protein/lipid interactions. These are major limitations that impede the research.Objectives: Our aim is to combine MDS and mass spectrometry with artificial intelligence (AI)/machine learning (ML) to create a new approach that will significantly accelerate the prediction processes for protein/lipid interactions. In this approach we will use MDS to identify structural motifs on proteins that interreact with specific lipids using a set of proteins for which their 3D structure is known; the AI methods can learn from such data and predict lipid binding sites for other similar proteins.Novelty and Timeliness:Given the large increase of 3D membrane protein structures (some in complex with lipids), this research is timely in utilizing the cutting-edge AI/ML technologies to identify structural motifs on membrane proteins that interact with specific lipid types. Experimental approach: The student will use known 3D protein structures from the PDB and molecular simulations to identify how regions of different membrane protein families interact with specific lipid types. Then, AI/ML approaches will be developed to learn the interactions, to identify patterns in protein/lipid interactions, and to provide predictions for the interactions of other proteins. Native mass spectrometry will be used to evaluate and refine some of the results of the AI/ML methodology.