Computational and Experimental Models of Myosin Variants

About This Grant

PROJECT SUMMARY/ABSTRACT The goal of this project is to develop tools to determine the mechanisms by which single amino acid variants affect myosin structure and function at multiple scales. Major challenges have been that: 1) a limited number of human β-myosin structures; 2) the computational expense (CPU time) to simulate the dynamics of such a large protein, and 3) difficulty in obtaining tissue from patients with disease causing variants. To overcome these challenges, we developed new structure-based, dynamics models of the chemo-mechanical cycle of β-myosin. These models are generated from new human crystal and cryo-EM structures, augmented by known structures available from bovine muscle. Our Molecular and Brownian Dynamics models contain F-actin structure to study how its interactions with β-myosin affects signaling between the nucleotide binding pocket, the actin-myosin interface, converter domain and other regions of myosin. These simulations are augmented by our novel inter- protein pathway analysis based on graph theory. We will use stochastic-kinetic models of sarcomeres to simulate contraction and relaxation with variable amounts of myosin variants. We will use gene edited human inducible pluripotent stem cells (hiPSCs) to validate our results. Our culture conditions result in myofibrils expressing predominantly β-myosin and demonstrate kinetics of adult myofibrils. We will purify myosin from these hiPSC- CMs for biochemical kinetic measures of the chemo-mechanical cycle. Each of these mechanical states results from structural changes in myosin and its association with actin. Additional studies will be performed at the level of myofibrils to study how mutations affect contractile function. We will also use single molecule, super-resolution to study high resolution structural changes in thick filaments from hiPSC-CMs, and Molecular Dynamics models of the Interacting Heads Motif structure of myosin on the thick filament backbone. This platform will be used to predict the effect of a selected group of variants of uncertain significance (VUSs).