# Development & application of computational methods for the study of protein dynamics with PmHMGR as a model system

> **NIH NIH F31** · UNIVERSITY OF NOTRE DAME · 2024 · $48,974

## Abstract

PROJECT SUMMARY
 Diseases are frequently caused by dysfunction of proteins in the body, perhaps
due to maladapted genetics or from a wide variety of other causes. Researchers can gain
a glimpse into this function through the study of a protein’s mechanism and dynamics.
Ideally, a complete understanding of the role of a protein in biophysical interactions would
describe the entire mechanistic pathway on an atomistic and dynamic level. However,
this cannot be attained with experimental studies alone with today’s capabilities.
Computational studies can provide experimentally inaccessible quantitative and atomistic
information so they serve as powerful tools for better understanding diseases and
identifying targets for experimental follow-up and potential treatment, but they carry little
weight without rigorous experimental validation. We seek to reconcile experimental and
computational data, equipping researchers with a method to produce the aforementioned
continuous and atomistic information on protein dynamics so that they can elucidate the
long timescale dynamics of proteins on an atomic level. When deconvolving time-resolved
crystallographic data, I will substitute the typical static crystallographic initial inputs with
structures from molecular dynamics simulations and predictive models to improve the
continuity and accuracy of deconvoluted data. The objective of this work is to produce the
aforementioned ideal dynamics information for a significant portion of the mechanism of
PmHMGR as a demonstration and refinement of the proposed Markov State informed
Multilinear Singular Value Decomposition (MSiMSVD) method which reconciles
experimental and computational data. Application of the MSiMSVD method to slow
dynamical events, such as the PmHMGR 2nd hydride transfer, is limited by the ability of
molecular dynamics to perform accurate long-timescale simulations. This often requires
Transition State Force Fields (TSFFs), but their parameterization for biomolecules often
falls into local optimization minima due to high dimensionality. To reduce local minima
trapping and make TSFF generation more accessible for biophysical research, I will apply
constraints and swarm intelligence techniques to improve current TSFF parameterization.
 Collectively, these aims will provide a means by which experimental and
computational techniques can work synergistically to produce the continuous atomistic
protein dynamics information ideal for the investigation of proteins and their related
functions and diseases.

## Key facts

- **NIH application ID:** 10913299
- **Project number:** 5F31LM014204-02
- **Recipient organization:** UNIVERSITY OF NOTRE DAME
- **Principal Investigator:** Mikaela Farrugia
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $48,974
- **Award type:** 5
- **Project period:** 2023-08-25 → 2025-08-24

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10913299

## Citation

> US National Institutes of Health, RePORTER application 10913299, Development & application of computational methods for the study of protein dynamics with PmHMGR as a model system (5F31LM014204-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10913299. Licensed CC0.

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