Generative Adversarial Learning of Protein Tertiary Structures.

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 base

SUBMITTER: Rahman T 

PROVIDER: S-EPMC7956369 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature