Generative Adversarial Learning of Protein Tertiary Structures

Protein molecules are inherently dynamic and modulate their interactions with different molecular partners by accessing different tertiary structures under physiological conditions. Elucidating such structures remains challenging. Current momentum in deep learning and the powerful performance of generative adversarial networks (GANs) in complex domains, such as computer vision, inspires us to investigate GANs on their ability to generate physically-realistic protein tertiary structures. The analysis presented here shows that several GAN models fail to capture complex, distal structural patterns present in protein tertiary structures. The study additionally reveals that mechanisms touted as effective in stabilizing the training of a GAN model are not all effective, and that performance based on loss alone may be orthogonal to performance based on the quality of generated datasets. A novel contribution in this study is the demonstration that Wasserstein GAN strikes a good balance and manages to capture both local and distal patterns, thus presenting a first step towards more powerful deep generative models for exploring a possibly very diverse set of structures supporting diverse activities of a protein molecule in the cell.

Automated summary

The study reveals that current GAN models struggle to capture complex structural patterns in protein molecules, and that mechanisms believed to stabilize training may not be effective. Additionally, performance based solely on loss may not correlate with the quality of generated datasets. The novel contribution of the study is the demonstration that Wasserstein GAN strikes a good balance in capturing both local and distal patterns in protein tertiary structures, paving the way for deeper generative models in exploring diverse protein activities.