Point mutations found on myofibrillar proteins have been shown to affect muscle contractility and lead to cardiomyopathies and skeletal muscle disease syndromes of varying severity. Over 30 mutations localize to residues on the actin-tropomyosin interface and modify the actin-tropomyosin energy landscape, influence tropomyosin positioning on actin, and perturb allosteric cooperativity between actin, tropomyosin, troponin and myosin. Here, energy landscape computation, in combination with known actin-tropomyosin sequence and structural information was used prospectively to identify potential effects of post-translational modifications generated to rescue regulatory imbalances. For instance, our interaction energy calculations show that HCM-associated E62Q tropomyosin mutation weakens residue-residue specific actin-tropomyosin binding. We then predicted that phosphorylation of neighboring S61 would rescue the deficit, which was corroborated by further energy landscape determination. To validate these in silico results experimentally, the sliding velocity of tropomyosin-troponin decorated thin filaments was measured as a function of added calcium for thin filaments containing the E62Q, phosphomimetic S61D and E62Q-S61D mutant tropomyosins. In vitro motility assays showed actin associated with E62Q mutant tropomyosin requires lower Ca2+ to fully activate troponin-tropomyosin regulated mutant filaments when compared to activation of wild-type filaments (as expected from earlier reports based on acto-S1 ATPase work). In contrast, the double mutant E62Q-S61D restores Ca2+-sensitivity toward normal while slightly reducing sliding velocity. Thus, the shift in Ca2+-sensitivity by E62Q and subsequent reversal by S61D parallel the blocked-state phosphorylation-dependent stabilization of actin-tropomyosin noted by our landscape measurements. Likewise, a decrease in Ca2+-sensitivity produced by the single mutant S61D alone likely resulted from blocked site stabilization. Hence, shifts observed in pCa50 for the mutant tropomyosins could be accurately predicted by in silico calculation of energy landscapes. Further, in silico predictions were used to guide the design of post-translational modifications to rescue regulatory imbalances.