De novo protein design by deep network hallucination.

ABSTRACT: There has been considerable recent progress in protein structure prediction using deep neural networks to predict inter-residue distances from amino acid sequences^1-3. Here we investigate whether the information captured by such networks is sufficiently rich to generate new folded proteins with sequences unrelated to those of the naturally occurring proteins used in training the models. We generate random amino acid sequences, and input them into the trRosetta structure prediction network to predict starting residue-residue distance maps, which, as expected, are quite featureless. We then carry out Monte Carlo sampling in amino acid sequence space, optimizing the contrast (Kullback-Leibler divergence) between the inter-residue distance distributions predicted by the network and

SUBMITTER: Anishchenko I 

PROVIDER: S-EPMC9293396 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature