Multiscale Model Predictions of X-Ray Diffraction Patterns in Contracting Skeletal Muscle

Abstract

In order to explain time-resolved x-ray diffraction data, enabled by recent advances in synchrotron small-angle diffraction instruments, we explored the feasibility of using dynamic 3D models of muscle contraction to predict x-ray diffraction patterns. This approach differs radically from previous attempts, which merely aimed to provide a “best fit” structure for defined quasi-static states, by providing a tool to generate families of structures that evolve in time that explains both the structural (x-ray) and the mechanical data simultaneously. Specifically, we exploit the computational platform MUSICO which was developed originally to model muscle mechanics data, by extending this framework to simulate x-ray diffraction patterns using 3D multiscale models. These models take into account (i) biochemical states of myosin interacting with actin; (ii) rate constants in the actomyosin ATP hydrolysis cycle; (iii) function of myosin molecular motors in a 3D sarcomere lattice; (iv) Ca2+ regulation of myosin binding to actin; (v) extensibility of actin and myosin filaments; and (vi) multiple sarcomeres in series and in parallel. The platform is conceived primarily as a hypothesis-testing tool in which model predictions are tested against the best available mechanical and x-ray diffraction data on the same system. Our preliminary simulations provided dynamic x-ray diffraction patterns during force development and relaxation in skeletal muscle. The simulated patterns generally predicted well changes in repetitive molecular spacings and displayed similarity with experimental data. Once fully developed, this tool will enable extraction of maximum information from the x-ray patterns, in combination with the physiological data, and therefore provide a template to test hypotheses concerning crossbridge and regulatory protein action in working muscle. Our approach can be extended to any muscle system, and it could ultimately provide an interpretive framework for studying the mechanisms of inherited or acquired diseases.