Rational drug design and understanding of how mutations cause disease are largely based on the hypothesis that a protein's sequence determines its structure, which determines its function. However, designing drugs and interpreting new variants remain difficult, suggesting there is a missing factor in this sequence-structure-function paradigm. There is good reason to believe that a sequence-ensemble-function paradigm that better accounts for the fact that proteins are not rigid bodies, but are dynamic entities that are endlessly hopping through a set of different structures (called an ensemble) would be far more powerful. However, realizing this potential has been slow because it is even harder to get an atomically-detailed picture of an entire ensemble than a single protein structure. The PI and his lab have been developing tools that combine atomically-detailed computer simulations, biophysical experiments, and machine learning to overcome this challenge. They have made significant progress on relatively small proteins with limited dynamics, enabling a deeper understanding of how mutations modulate function and the design of new drug-like molecules for controlling function. The objective of this work is to test the applicability of these tools to much larger and more complicated proteins that are of significant importance in both fundamental biology and drug design, myosin motors. Myosins are responsible for a broad range of biological functions, from muscle contraction to hearing. As a result, they are important targets for treating diseases ranging from heart failure to parasitic infections. To function, myosins must undergo a complex series of structural changes. The PI and his lab will test whether their tools for accounting for these extensive dynamics enable more accurate predictions of sequence-function relationships and the rational design of new drug-like molecules for controlling motor function. They will focus on β-cardiac myosin because of its importanc