For the binary classification models made with our statistical analysis module, the two metrics used to score each feature were the mutual information (MI) and the Jensen-Shannon distance (JS-Distance). The MI was calculated using the function available from scikit-learn. In preparation for the JS-Distance calculations, features (already separated into their classes) were described using a Gaussian kernel density estimation (KDE), using a bandwidth of 0.02. The implementation of the JS-Distance calculation available from SciPy 79 was then used. For the case of a continuous target variable (i.e., regression), the two metrics used were the MI or linear correlation (LC), using the implementations available from scikitlearn. Per residue scores were obtained by summing together all the per feature scores in which a given residue was found and then scaling the results so that the top residue had a score of 1. If the per feature/interaction scores were non-linearly scaled (such as those obtained from the MI calculations), they were first rescaled to be linearly scaled, before calculating the per residue scores.