  Structure determination is key to understanding protein function at a
molecular level. Whilst significant advances have been made in predicting
structure and function from amino acid sequence, researchers must still rely on
expensive, time-consuming analytical methods to visualise detailed protein
conformation. In this study, we demonstrate that it is possible to make
accurate ($\geq$80%) predictions of protein class and architecture from
structures determined at low ($>$3A) resolution, using a deep convolutional
neural network trained on high-resolution ($\leq$3A) structures represented as
2D matrices. Thus, we provide proof of concept for high-speed, low-cost protein
structure classification at low resolution, and a basis for extension to
prediction of function. We investigate the impact of the input representation
on classification performance, showing that side-chain information may not be
necessary for fine-grained structure predictions. Finally, we confirm that
high-resolution, low-resolution and NMR-determined structures inhabit a common
feature space, and thus provide a theoretical foundation for boosting with
single-image super-resolution.
