Efficient protein structure generation with sparse denoising models.

ABSTRACT: Proteins play diverse roles in all domains of life and are extensively harnessed as biomolecules in biotechnology, with applications spanning from fundamental research to biomedicine. Therefore, there is considerable interest in computationally designing proteins with specified properties. Protein structure generative models provide a means to design protein structures in a controllable manner and have been successfully applied to address various protein design tasks. Such models are paired with protein sequence and structure predictors to produce and select protein sequences for experimental testing. However, current protein structure generators face important limitations for proteins with more than 400 amino acids and require retraining for protein design tasks unseen during model traini

SUBMITTER: Jendrusch M 

PROVIDER: S-EPMC7618410 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature