A0816
Title: Graphormer supervised de novo protein design method and function validation
Authors: Ting Wei - Shanghai Jiao Tong Univeristy (China) [presenting]
Abstract: Protein design is central to nearly all protein engineering problems, as it can enable the creation of proteins with new biological functions, such as improving the catalytic efficiency of enzymes. One key facet of protein design, fixed-backbone protein sequence design, seeks to design new sequences that will conform to a prescribed protein backbone structure. Nonetheless, existing sequence design methods present limitations, such as low sequence diversity and shortcomings in experimental validation of the designed functional proteins. To improve these limitations, the Graphormer-based protein design (GPD) model is initially developed. This model utilizes the transformer on a graph-based representation of 3D protein structures and incorporates Gaussian noise and a sequence random mask to node features, thereby enhancing sequence recovery and diversity. The performance of the GPD model was significantly better than that of the state-of-the-art ProteinMPNN model on multiple independent tests, especially for sequence diversity. GPD is employed to design CalB hydrolase, and nine artificially designed CalB proteins are generated. The results show significant improvement in the catalytic activity, which is 1.7 times higher than the CalB wild type. Thus, the GPD method could be used for the de novo design of industrial enzymes and protein drugs with specific functions.
