Bond type and discretization of non-muscle myosin II are critical for simulated contractile dynamics

Abstract

Molecular motors drive cytoskeletal rearrangements to change cell shape. Myosins are the motors that move, crosslink, and modify the actin cytoskeleton. The primary force generator in contractile actomyosin networks is non-muscle myosin II (NMMII), a molecular motor that assembles into ensembles that bind, slide, and crosslink actin filaments (F-actin). The multivalence of NMMII ensembles and their multiple roles have confounded the resolution of crucial questions including how the number of NMMII subunits affects dynamics, and what affects the relative contribution of ensembles crosslinking versus motoring activities. Since biophysical measurements of ensembles are sparse, modeling of actomyosin networks has aided in discovering the complex behaviors of NMMII ensembles. Myosin ensembles have been modeled via several strategies with variable discretization/coarse-graining and unbinding dynamics, and while general assumptions that simplify motor ensembles result in global contractile behaviors, it remains unclear which strategies most accurately depict cellular activity. Here, we used an agent-based platform, Cytosim, to implement several models of NMMII ensembles. Comparing the effects of bond type, we found that ensembles of catch-slip and catch motors were the best force generators and binders of filaments. Slip motor ensembles were capable of generating force but unbound frequently, resulting in slower contractile rates of contractile networks. Coarse-graining of these ensemble types from two sets of 16 motors on opposite ends of a stiff rod to two binders, each representing 16 motors, reduced force generation, contractility, and the total connectivity of filament networks for all ensemble types. A parallel cluster model (PCM) previously used to describe ensemble dynamics via statistical mechanics, allowed better contractility with coarse-graining, though connectivity was still markedly reduced for this ensemble type with coarse-graining. Together our results reveal substantial trade-offs associated with the process of coarse-graining NMMII ensembles and highlight the robustness of discretized catch-slip ensembles in modeling actomyosin networks. STATEMENT OF SIGNIFICANCEAgent-based simulations of contractile networks allow us to explore the mechanics of actomyosin contractility, which drives many cell shape changes including cytokinesis, the final step of cell division. Such simulations should be able to predict the mechanics and dynamics of non-muscle contractility, however recent work has highlighted a lack of consensus on how to best model the non-muscle myosin II. These ensembles of approximately 32 motors are the key components responsible for driving contractility. Here, we explored different methods for modeling non-muscle myosin II ensembles within the context of contractile actomyosin networks. We show that the level of coarse-graining and the choice of unbinding model used to model motor unbinding under load indeed has profound effects on contractile network dynamics.