  Massive molecular simulations of drug-target proteins have been used as a
tool to understand disease mechanism and develop therapeutics. This work
focuses on learning a generative neural network on a structural ensemble of a
drug-target protein, e.g. SARS-CoV-2 Spike protein, obtained from
computationally expensive molecular simulations. Model tasks involve
characterizing the distinct structural fluctuations of the protein bound to
various drug molecules, as well as efficient generation of protein
conformations that can serve as an complement of a molecular simulation engine.
Specifically, we present a geometric autoencoder framework to learn separate
latent space encodings of the intrinsic and extrinsic geometries of the protein
structure. For this purpose, the proposed Protein Geometric AutoEncoder
(ProGAE) model is trained on the protein contact map and the orientation of the
backbone bonds of the protein. Using ProGAE latent embeddings, we reconstruct
and generate the conformational ensemble of a protein at or near the
experimental resolution, while gaining better interpretability and
controllability in term of protein structure generation from the learned latent
space. Additionally, ProGAE models are transferable to a different state of the
same protein or to a new protein of different size, where only the dense layer
decoding from the latent representation needs to be retrained. Results show
that our geometric learning-based method enjoys both accuracy and efficiency
for generating complex structural variations, charting the path toward scalable
and improved approaches for analyzing and enhancing high-cost simulations of
drug-target proteins.
