Fully differentiable full-atom protein backbone generation
The fast generation and refinement of protein backbones would constitute a major advancement to current methodology for the design and development of de novo proteins.In this study, we train Generative Adversarial Networks to generate fixed-length full-atom protein backbones, with the goal of sampling from the distribution of realistic 3-D backbone fragments.We represent protein structures by pairwise distances between all backbone atoms, and present a method for directly recovering and refining the corresponding backbone coordinates in a differentiable manner.We show that interpolations in the latent space of the generator correspond to smooth deformations of the output backbones, and that test set structures not seen by the generator during training exist in its image.Finally, we perform sequence design, relaxation, and ab initio folding of a subset of generated structures, and show that in some cases we can recover the generated folds after forward-folding.Together, these results suggest a mechanism for fast protein structure refinement and folding using external energy functions.
We train a GAN to generate and recover full-atom protein backbones , and we show that in select cases we can recover the generated proteins after sequence design and ab initio forward-folding.A generative model for protein backbone which uses a GAN, autoencoder-like network, and refinement process, and a set of qualitative evaluations suggesting positive results.This paper presents an end-to-end approach for generating protein backbones using generative adversarial networks.