Modeling the structure-conditioned sequence landscape for large-scale protein design with TriFlow.

ABSTRACT: Generative models have revolutionized computational protein design, and the design of high-quality sequences given backbone structure is a critical component for success. Current state-of-the-art design pipelines utilize sequence design methods with local structural context and autoregressive generation. To improve efficiency and quality of sequence design, we developed TriFlow, a model that combines a RoseTTAFold-like three-track architecture for global structural context with discrete flow-matching for efficient few-step sequence generation. We trained TriFlow on a large dataset of interacting protein chains from PDB and interacting domains from AlphaFold protein structure Database to enrich its knowledge of natural protein/domain interfaces. While showing improvements across diverse ben

SUBMITTER: Srinivasan H 

PROVIDER: S-EPMC12707284 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature