Empirical validation of ProteinMPNN’s efficiency in enhancing protein fitness - Wang, Tianshu; Jin, Xiaocheng; Lu, Xiaoli; Min, Xiaoping; Ge, Shengxiang; Li, Shaowei (Frontiers in Genetics, 2024)
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 mainly 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.As verified by experimental datasets, ProteinMPNN has demonstrated its capacity to design sequences with enhanced properties and functions. The method of obtaining mutants using sequence design models also provides a new approach to protein engineering, offering strong support for guiding biological experiments and applications in bioengineering.