Variational Autoencoders for Protein Structure Prediction

The universe of protein structures contains many dark regions beyond the reach of experimental techniques. Yet, knowledge of the tertiary structure(s) that a protein employs to interact with partners in the cell is critical to understanding its biological function(s) and dysfunction(s). Great progress has been made in silico by methods that generate structures as part of an optimization. Recently, generative models based on neural networks are being debuted for generating protein structures. There is typically limited to showing that some generated structures are credible. In this paper, we go beyond this objective. We design variational autoencoders and evaluate whether they can replace existing, established methods. We evaluate various architectures via rigorous metrics in comparison with the popular Rosetta framework. The presented results are promising and show that once seeded with sufficient, physically-realistic structures, variational autoencoders are efficient models for generating realistic tertiary structures.