# Site-Directed  Modification of Muscle Protein Structural Dynamics

> **NIH NIH R37** · UNIVERSITY OF MINNESOTA · 2020 · $475,805

## Abstract

The long-term goal of the parent grant is to understand the functional and structural consequences of oxidation
in muscle proteins, in order to illuminate the mechanisms by which oxidative stress affects human health and
aging, with the two aims focusing on two key muscle proteins – calmodulin and myosin. The present
administrative supplement extends the second aim, on myosin, to include studies on sex differences in animal
models. These studies will focus particular attention on the role of estrogen modulating post-translational
modifications of myosin. The high potential impact of this work is made possible by a collaboration with an
expert in mouse models of aging, oxidation, and estrogen. Thus this work starts with the novel approaches of
muscle biophysics and structural biology in the parent grant, and is augmented by the complementary
expertise in muscle physiology and sex differences in the collaborator’s laboratory. This supplemental project
offers a unique and innovative combination of approaches, all focused on a timely goal – to explain how sex
hormones, particularly the less studied female sex hormone, impact the complex effects of oxidation and aging
on muscle protein function, structure, and dynamics.

## Key facts

- **NIH application ID:** 9902266
- **Project number:** 5R37AG026160-15
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** David D Thomas
- **Activity code:** R37 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $475,805
- **Award type:** 5
- **Project period:** 2004-09-15 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9902266

## Citation

> US National Institutes of Health, RePORTER application 9902266, Site-Directed  Modification of Muscle Protein Structural Dynamics (5R37AG026160-15). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9902266. Licensed CC0.

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