Generative adversarial learning of protein tertiary structures

• Authors: Taseef Rahman, George Mason University; Yuanqi Du, George Mason University; Liang Zhao, Emory University; Amarda Shehu, George Mason University
• Journal Title: Molecules
• Volume: Volume 26, Number 5
• Publisher: MDPI | 2021-03-01
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.3390/molecules26051209
• Subjects: Computer Science; Engineering, Biomedical
• Keywords: deep learning; generative adversarial learning; protein modeling; tertiary structure; Neural Networks, Computer; Protein Structure, Tertiary; Proteins

Abstract

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 thatWasserstein 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.