  Much scientific enquiry across disciplines is founded upon a mechanistic
treatment of dynamic systems that ties form to function. A highly visible
instance of this is in molecular biology, where an important goal is to
determine functionally-relevant forms/structures that a protein molecule
employs to interact with molecular partners in the living cell. This goal is
typically pursued under the umbrella of stochastic optimization with algorithms
that optimize a scoring function. Research repeatedly shows that current
scoring function, though steadily improving, correlate weakly with molecular
activity. Inspired by recent momentum in generative deep learning, this paper
proposes and evaluates an alternative approach to generating
functionally-relevant three-dimensional structures of a protein. Though
typically deep generative models struggle with highly-structured data, the work
presented here circumvents this challenge via graph-generative models. A
comprehensive evaluation of several deep architectures shows the promise of
generative models in directly revealing the latent space for sampling novel
tertiary structures, as well as in highlighting axes/factors that carry
structural meaning and open the black box often associated with deep models.
The work presented here is a first step towards interpretative, deep generative
models becoming viable and informative complementary approaches to protein
structure prediction.
