Before revision:  We explore the relationship between the information content of the input image and the predictive power of the model, achieving state of the art performance on homologous superfamily prediction with maps of interatomic distance. Our findings contribute further evidence that inclusion of both amino acid alpha and beta carbon geometry in these maps improves classification performanceover purely alpha carbon representations, and show that side-chain information may not be necessary for fine-grained structure predictions.
After revision:  We explore the relationship between the information content of the input representation on classification performance, showing that side-chain information may not be necessary for fine-grained structure predictions.