Empirical validation of ProteinMPNN's efficiency in enhancing protein fitness.

ABSTRACT: Introduction: Protein engineering, which aims to improve the properties and functions of proteins, holds great research significance and application value. However, current models that predict the effects of amino acid substitutions often perform poorly when evaluated for precision. Recent research has shown that ProteinMPNN, a large-scale pre-training sequence design model based on protein structure, performs exceptionally well. It is capable of designing mutants with structures similar to the original protein. When applied to the field of protein engineering, the diverse designs for mutation positions generated by this model can be viewed as a more precise mutation range. Methods: We collected three biological experimental datasets and compared the design results of Protein

SUBMITTER: Wang T 

PROVIDER: S-EPMC10808456 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature