Cardiac contraction relies on the precise coordination of sarcomeric protein interactions, yet the dynamics of these molecular machineries remain challenging to quantify, particularly under pathological conditions. The interplay between calcium handling, sarcomeric force generation, and electrophysiology creates a tightly coupled feedback system that is difficult to dissect using traditional modeling techniques. To address this, we present a computational framework aimed at identifying and constraining parameters governing sarcomeric protein kinetics, with a focus on calcium–troponin C binding and myofilament activation dynamics. Two integrated electrophysiology–mechanics models were developed, incorporating sarcomeric kinetics from established myofilament models. Parameter estimation targeted key protein-level processes such as calcium binding to troponin C and cooperative cross-bridge recruitment. We compared a population of models approach, which captures variability but offers limited parameter precision, with a maximum likelihood estimation (MLE) method, which identifies optimal parameter sets by fitting model output to experimental measurements of sarcomere shortening and intracellular calcium transients. To quantify uncertainty and assess model robustness, we used profile likelihood analysis, which allows parameter space to be reduced while retaining essential model behaviour. This approach revealed improved parameter identifiability and provided confidence intervals that improve the interpretability of protein-level dynamics. Our results demonstrate that MLE, combined with likelihood profiling, provides a rigorous method for characterising sarcomeric protein interactions and reducing uncertainty in multiscale cardiac models. By focusing on the molecular machinery of the sarcomere, this framework supports the development of more reliable predictive models of cardiac function, particularly under disease-relevant mechanical perturbations. This protein-centric perspective provides a pathway to link changes at the molecular level to whole-cell behaviour, providing insights into the mechanisms underlying contractile dysfunction.