DIProT: A deep learning based interactive toolkit for efficient and effective Protein design.

ABSTRACT: The protein inverse folding problem, designing amino acid sequences that fold into desired protein structures, is a critical challenge in biological sciences. Despite numerous data-driven and knowledge-driven methods, there remains a need for a user-friendly toolkit that effectively integrates these approaches for in-silico protein design. In this paper, we present DIProT, an interactive protein design toolkit. DIProT leverages a non-autoregressive deep generative model to solve the inverse folding problem, combined with a protein structure prediction model. This integration allows users to incorporate prior knowledge into the design process, evaluate designs in silico, and form a virtual design loop with human feedback. Our inverse folding model demonstrates competitive performance in ter

SUBMITTER: He J 

PROVIDER: S-EPMC10876589 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature