Rapid and automated design of two-component protein nanomaterials using ProteinMPNN.

ABSTRACT: The design of protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. Deep learning methods promise to simplify protein-protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here, we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were s

SUBMITTER: de Haas RJ 

PROVIDER: S-EPMC10990136 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature