  Identifying novel functional protein structures is at the heart of molecular
engineering and molecular biology, requiring an often computationally
exhaustive search. We introduce the use of a Deep Convolutional Generative
Adversarial Network (DCGAN) to classify protein structures based on their
functionality by encoding each sample in a grid object structure using three
features in each object: the generic atom type, the position atom type, and its
occupancy relative to a given atom. We train DCGAN on 3-dimensional (3D) decoy
and native protein structures in order to generate and discriminate 3D protein
structures. At the end of our training, loss converges to a local minimum and
our DCGAN can annotate functional proteins robustly against adversarial protein
samples. In the future we hope to extend the novel structures we found from the
generator in our DCGAN with more samples to explore more granular functionality
with varying functions. We hope that our effort will advance the field of
protein structure prediction.
