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Generative Adversarial Learning of Protein Tertiary Structures. | LitMetric

Generative Adversarial Learning of Protein Tertiary Structures.

Molecules

Department of Computer Science, George Mason University, Fairfax, VA 22030, USA.

Published: February 2021

AI Article Synopsis

  • Protein molecules are dynamic and change their structures to interact with different partners, but identifying these structures is difficult.
  • The study explores the use of generative adversarial networks (GANs) to create realistic protein structures and finds that many models struggle to accurately represent complex patterns.
  • A key finding is that the Wasserstein GAN is effective in capturing both local and distant structural features, offering a promising direction for developing better models to study protein diversity and function.

Article 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 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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7956369PMC
http://dx.doi.org/10.3390/molecules26051209DOI Listing

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