# Developing computational methods to determine the thermodynamics of lipid phase coexistence

> **NIH NIH R21** · UNIVERSITY OF ROCHESTER · 2021 · $192,500

## Abstract

Project Description
Recent years have brought a new appreciation for the role of phase separation in
biology, and in particular phase coexistence in the lipid membranes that define
the boundaries in and around our cells. Studying this phenomenon using
molecular simulation methods remains challenging because of the time- and
length-scales involved, and while coarse-grained simulations have reproduced
the existence of phase coexistence, to date there are no methods for determining
the thermodynamics governing the process.
We will develop a new method that will fill this gap, built around the weighted
ensemble simulation technique using a contact-based collective variable. We will
implement and test a protocol for measuring the free energy to demix 3-
component membranes, using coarse-grained simulation with the MARTINI
force field as our test suite. In addition, we will implement and validate a new
method to couple weighted ensemble with temperature replica exchange, as a
way to further speed statistical convergence of the calculations. Finally, we will
extend the method to handle the partitioning and aggregation of surface-bound
peptides between coexisting phases, using the antifungal lipopeptide fengycin as
a test case.

## Key facts

- **NIH application ID:** 10204062
- **Project number:** 5R21GM138970-02
- **Recipient organization:** UNIVERSITY OF ROCHESTER
- **Principal Investigator:** Alan Grossfield
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $192,500
- **Award type:** 5
- **Project period:** 2020-07-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10204062

## Citation

> US National Institutes of Health, RePORTER application 10204062, Developing computational methods to determine the thermodynamics of lipid phase coexistence (5R21GM138970-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10204062. Licensed CC0.

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