Conditional generative modeling for de novo protein design with hierarchical functions.

ABSTRACT:

Motivation

Protein design has become increasingly important for medical and biotechnological applications. Because of the complex mechanisms underlying protein formation, the creation of a novel protein requires tedious and time-consuming computational or experimental protocols. At the same time, machine learning has enabled the solving of complex problems by leveraging large amounts of available data, more recently with great improvements on the domain of generative modeling. Yet, generative models have mainly been applied to specific sub-problems of protein design.

Results

Here, we approach the problem of general-purpose protein design conditioned on functional labels of the hierarchical Gene Ontology. Since a canonical way to evaluate generative models in this domain is m

SUBMITTER: Kucera T 

PROVIDER: S-EPMC9237736 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature