ProteinMPNN Recovers Complex Sequence Properties of Transmembrane β-barrels.

ABSTRACT: Recent deep-learning (DL) protein design methods have been successfully applied to a range of protein design problems, including the de novo design of novel folds, protein binders, and enzymes. However, DL methods have yet to meet the challenge of de novo membrane protein (MP) and the design of complex β-sheet folds. We performed a comprehensive benchmark of one DL protein sequence design method, ProteinMPNN, using transmembrane and water-soluble β-barrel folds as a model, and compared the performance of ProteinMPNN to the new membrane-specific Rosetta Franklin2023 energy function. We tested the effect of input backbone refinement on ProteinMPNN performance and found that given refined and well-defined inputs, ProteinMPNN more accurately captures global sequence properties de

SUBMITTER: Dolorfino M 

PROVIDER: S-EPMC10862708 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature