# NIH grant: Molecular determinants of lipid-protein recognition (1R35GM165612-01) — retained record

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[original source ↗](https://reporter.nih.gov/project-details/1R35GM165612-01)

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NIH grant 1R35GM165612-01: Molecular determinants of lipid-protein recognition
Institute: {'code': 'GM', 'abbreviation': 'NIGMS', 'name': 'National Institute of General Medical Sciences', 'admin_org_id': '12500024', 'admin_funding_url': ''}. PI: Neville  Bethel. Award: $384394.
Start: 2026-08-01T00:00:00.
Project Summary
Lipids form the membranes that are the primary boundary of organelles. The chemistry and physical properties
of these membranes are incredibly heterogeneous and dictate their signaling and mechanical properties. The
shape, charge, and flexibility of lipid headgroups will often dictate the binding or regulation of protein activity.
Changes in the regulation and distribution of these membrane lipids are a key contributor to diseases including
cancer and neurodegeneration. While the composition of these membranes is of key biomedical interest, they
are also very poorly understood and difficult to dissect with traditional biochemical and biophysical methods. Our
laboratory seeks to integrate deep-learning, molecular dynamics simulation and de novo protein design to
investigate critical biophysical interactions that confer binding affinity and specificity of membrane-associated
proteins with lipid headgroups, the plasma membrane or organellar surfaces. We utilize these methods to build
generalizable models of protein binding to determine how binding terms like shape complementarity, hydrogen
bonding, hydrophobic effect and entropic penalty confer specificity at the heterogenous solvent environment of
the membrane surface. In our first approach we will employ de novo protein design to generate a library of
headgroup binding proteins with distinct folds and binding pockets. We will use this library to rigorously
characterize the energetics of membrane-protein association both computationally and experimentally. Using
this library and the resulting dataset, we will generate a model for predicting the thermodynamics and kinetics of
lipid-protein binding in native proteins. In our second approach, we will focus on a specific lipid, GM2, which is a
glycosphingolipid whose dysregulation is a key, but poorly understood, contributor to Niemann-Pick Disease
Type C (NPC). We will focus on generating novel de novo lipid-biosensors for GM2 to measure the trafficking
of this lipid in live cells and to better understand the connection between disease mutations in NPC and the mis
trafficking of GM2. Both approaches, if carried out, will enrich our understanding of how a protein’s binding
surface and biophysical properties influence membrane-protein association and how the dysregulation of these
resident lipids contribute to disease. In addition, the data we extract from our simulations and experimental
assays will allow us to re-evaluate our theoretical models of lipid-protein interactions, which will allow us to better
generalize machine learning models of protein interactions with metabolites, small molecule drugs, and other
biological macromolecules. Long term, we intend to be able to predict a protein’s lipid target and affinity from its
sequence, and to develop novel biosensors for every lipid type.
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