# The interaction of myosin and the thin filament: how mutations cause allosteric dysfunction and their connection to genetic cardiomyopathy

> **NIH NIH R01** · UNIVERSITY OF ARIZONA · 2021 · $537,089

## Abstract

Project Summary:
The long-term goal of this research program is to develop a rigorously experimentally validated all-atom
computational model of the cardiac thin filament (CTF) bound to myosin S1 which provides a unique and
accessible platform to identify novel, high resolution disease mechanisms linked to Hypertrophic
Cardiomyopathy (HCM). In the prior funding period, we refined and extended our existing CTF computational
model and successfully employed it to identify unique and clinically relevant allosteric disease mechanisms
including HCM mutation-induced changes in myofilament Ca2+ kinetics, mutation-specific molecular causes of
differential cardiac remodeling and disease progression. This included an in vivo validation via the
development of a novel transgenic mouse model of cTnT-linked dilated cardiomyopathy and a predictive
algorithm to determine the pathogenicity of cTnT mutations that out-performed existing computational
approaches in a preliminary test. The key to these advances has been the ability of the current model to
precisely identify and locate allosteric changes caused by mutations throughout all components of the CTF
followed by closely coupled experimental validation and eventual in vivo model correlation. We now propose to
significantly expand the biological complexity of the model to include myosin S1, the molecular motor that
drives contraction and the second most common genetic cause of HCM. This important and challenging
advance will facilitate a deeper understanding of disease pathogenesis by, for the first time, incorporating the
role of molecular allosteric mechanisms between myosin S1 and thin filament. This new computational –
experimental platform will be used for both mechanistic insight (for example used for the identification of novel
myofilament disease targets,) and the development of a comprehensive deep-learning predictive algorithm to
assign pathogenicity to both myosin and thin filament HCM mutations. The latter represents the first use of
high-resolution structure, dynamics and function to predict HCM disease allele pathogenicity, a central
challenge in the clinical management of these complex patients. Both the training and testing components of
the deep learning development will utilize data from the highly annotated and curated SHaRe HCM registry
thus greatly improving translational power. Two Specific Aims will be pursued: Aim 1 will utilize state of the art
rare event simulation methods developed in one of our groups and refinement of existing unstructured domains
of the CTF via FRET to establish the new model. Aim 2 will employ an extensive program of computational
analysis and subsequent in vitro validation using pathogenic, variants of unknown significance and non-
pathogenic HCM alleles derived from SHaRe to provide inputs to the machine learning environment for
algorithm development. Novel disease mechanisms for myosin and thin filament HCM that include crosstalk
between the two components will...

## Key facts

- **NIH application ID:** 10240327
- **Project number:** 5R01HL107046-11
- **Recipient organization:** UNIVERSITY OF ARIZONA
- **Principal Investigator:** STEVEN D SCHWARTZ
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $537,089
- **Award type:** 5
- **Project period:** 2010-12-15 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10240327

## Citation

> US National Institutes of Health, RePORTER application 10240327, The interaction of myosin and the thin filament: how mutations cause allosteric dysfunction and their connection to genetic cardiomyopathy (5R01HL107046-11). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10240327. Licensed CC0.

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