Dilated cardiomyopathy (DCM) is often associated with sarcomere protein mutations that confer reduced myofilament tension–generating capacity. We demonstrated that cardiac twitch tension-time integrals can be targeted and tuned to prevent DCM remodeling in hearts with contractile dysfunction. We employed a transgenic murine model of DCM caused by the D230N-tropomyosin (Tm) mutation and designed a sarcomere-based intervention specifically targeting the twitch tension-time integral of D230N-Tm hearts using multiscale computational models of intramolecular and intermolecular interactions in the thin filament and cell-level contractile simulations. Our models predicted that increasing the calcium sensitivity of thin filament activation using the cardiac troponin C (cTnC) variant L48Q can sufficiently augment twitch tension-time integrals of D230N-Tm hearts. Indeed, cardiac muscle isolated from double-transgenic hearts expressing D230N-Tm and L48Q cTnC had increased calcium sensitivity of tension development and increased twitch tension-time integrals compared with preparations from hearts with D230N-Tm alone. Longitudinal echocardiographic measurements revealed that DTG hearts retained normal cardiac morphology and function, whereas D230N-Tm hearts developed progressive DCM. We present a computational and experimental framework for targeting molecular mechanisms governing the twitch tension of cardiomyopathic hearts to counteract putative mechanical drivers of adverse remodeling and open possibilities for tension-based treatments of genetic cardiomyopathies.

2’-deoxy-ATP (dATP) improves cardiac function by increasing the rate of crossbridge cycling and Ca 2 + transient decay. However, the mechanisms of these effects and how therapeutic responses to dATP are achieved when dATP is only a small fraction of the total ATP pool remain poorly understood. Here, we used a multiscale computational modeling approach to analyze the mechanisms by which dATP improves ventricular function. We integrated atomistic simulations of prepowerstroke myosin and actomyosin association, filament-scale Markov state modeling of sarcomere mechanics, cell-scale analysis of myocyte Ca 2 + dynamics and contraction, organ-scale modeling of biventricular mechanoenergetics, and systems level modeling of circulatory dynamics. Molecular and Brownian dynamics simulations showed that dATP increases the actomyosin association rate by 1.9 fold. Markov state models predicted that dATP increases the pool of myosin heads available for crossbridge cycling, increasing steady-state force development at low dATP fractions by 1.3 fold due to mechanosensing and nearest-neighbor cooperativity. This was found to be the dominant mechanism by which small amounts of dATP can improve contractile function at myofilament to organ scales. Together with faster myocyte Ca 2 + handling, this led to improved ventricular contractility, especially in a failing heart model in which dATP increased ejection fraction by 16% and the energy efficiency of cardiac contraction by 1%. This work represents a complete multiscale model analysis of a small molecule myosin modulator from single molecule to organ system biophysics and elucidates how the molecular mechanisms of dATP may improve cardiovascular function in heart failure with reduced ejection fraction.

2’-Deoxy-ATP (dATP), a naturally occurring near analog of ATP, is a well-documented myosin activator that has been shown to increase contractile force, improve pump function, and enhance lusitropy in the heart. Calcium transients in cardiomyocytes with elevated levels of dATP show faster calcium decay compared with cardiomyocytes with basal levels of dATP, but the mechanisms behind this are unknown. Here, we design and utilize a multiscale computational modeling framework to test the hypothesis that dATP acts on the sarcoendoplasmic reticulum calcium-ATPase (SERCA) pump to accelerate calcium re-uptake into the sarcoplasmic reticulum during cardiac relaxation. Gaussian accelerated molecular dynamics simulations of human cardiac SERCA2A in the E1 apo, ATP-bound and dATP-bound states showed that dATP forms more stable contacts in the nucleotide binding pocket of SERCA and leads to increased closure of cytosolic domains. These structural changes ultimately lead to changes in calcium binding, which we assessed using Brownian dynamics simulations. We found that dATP increases calcium association rate constants to SERCA and that dATP binds to apo SERCA more rapidly than ATP. Using a compartmental ordinary differential equation model of human cardiomyocyte excitation-contraction coupling, we found that these increased association rate constants contributed to the accelerated rates of calcium transient decay observed experimentally. This study provides clear mechanistic evidence of enhancements in cardiac SERCA2A pump function due to interactions with dATP.

The long, complex, and costly process of drug development makes early, reliable toxicity assessment a central challenge in pharmaceutical research. Accurately predicting drug‐induced toxicity and mutagenicity is crucial for guiding decision‐making and reducing late‐stage attrition. Deep learning approaches can predict the results of the Ames mutagenicity test, a standard screening method; however, most existing models rely on molecular descriptors that limit structural representation, lack interpretability, and do not account for Ames results of individual bacterial strains. Graph neural networks (GNNs) are chemically intuitive and overcome these limitations. This work presents a deep learning, multitask GNN model for predicting Ames mutagenicity. The proposed approach outperforms several existing models, including those from the Ames/QSAR International Challenge, on the dataset presented in this work. Furthermore, the model is highly interpretable. Explainability analysis of atom‐level contributions reveal that the model is capable of learning known structural alerts associated with mutagenicity, along with additional molecular patterns and 3D structural properties. Importantly, these insights are captured in a strain‐specific manner, highlighting the value of a multitask approach. Finally, the model is able to identify previously unreported molecular substructures potentially associated with mutagenicity, supporting its utility in drug design and development.