# DMS/NIGMS 2: Integrated Analysis of Fusion Protein Conformational Changes for Virus Entry

> **NIH NIH R01** · WASHINGTON STATE UNIVERSITY · 2024 · $284,953

## Abstract

Virus infections remain major threats to human health worldwide as demonstrated by COVID-19 
pandemic caused by SARS-CoV-2 and its variants. All enveloped viruses must fuse with host membranes 
to initiate the infection process. Membrane fusion is a critical, but poorly understood biological process 
that is driven by protein conformational changes. Membrane fusion is a highly complex, multistage and 
multiscale process, which is difficult to investigate through scale-specific techniques (both experimentally 
and numerically). In this project, we propose to investigate the structural changes of fusion proteins and 
virus fusion through a combination of multiscale modeling, machine learning, and complementary 
experimentation. Because of our decades long experience in working with the herpes simplex virus 
(HSV), we will utilize the herpesvirus fusogen, gB, a class Ill fusion protein, as a model protein, to 
elucidate protein conformational changes during virus fusion. The specific research aims are: (1) To 
delineate the conformational changes of viral fusion proteins, through development of the machine 
learning facilitated enhanced sampling scheme for fusion proteins and delineation of the sequential 
conformation changes of gB protein for HSV fusion by a combination of machine learning and 
experiments; (2) To elucidate membrane fusion driven by viral fusion protein conformational changes, 
through development of a multiscale model for membrane fusion, assessment of the role of membrane 
fluidity in fusion and elucidation of the importance of the gB membrane proximal region on gB 
conformational changes and membrane fusion through combined simulations and experiments. 
This research will establish experimentally validated, powerful modeling platforms for exploration of the 
protein conformational changes and will bridge the multiple spatial and temporal scales involved in the 
fusion process. The machine learning method for enhanced sampling is highly innovative and crucial to 
capture the large-scale structural (conformational) changes and associated energy profiles of fusion 
proteins, and to identify the appropriate pathways during the fusion process. The integration of the 
gradient-based optimization method to the machine learning algorithm is novel to identify the most 
appropriate reaction coordinates.

## Key facts

- **NIH application ID:** 10934341
- **Project number:** 5R01GM152745-02
- **Recipient organization:** WASHINGTON STATE UNIVERSITY
- **Principal Investigator:** Jin Liu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $284,953
- **Award type:** 5
- **Project period:** 2023-09-25 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10934341

## Citation

> US National Institutes of Health, RePORTER application 10934341, DMS/NIGMS 2: Integrated Analysis of Fusion Protein Conformational Changes for Virus Entry (5R01GM152745-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10934341. Licensed CC0.

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