Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures.

ABSTRACT: Enzymes are crucial in numerous biological processes, with the Enzyme Commission (EC) number being a commonly used method for defining enzyme function. However, current EC number prediction technologies have not fully recognized the importance of enzyme active sites and structural characteristics. Here, we propose GraphEC, a geometric graph learning-based EC number predictor using the ESMFold-predicted structures and a pre-trained protein language model. Specifically, we first construct a model to predict the enzyme active sites, which is utilized to predict the EC number. The prediction is further improved through a label diffusion algorithm by incorporating homology information. In parallel, the optimum pH of enzymes is predicted to reflect the enzyme-catalyzed reactions. Experiments dem

SUBMITTER: Song Y 

PROVIDER: S-EPMC11411130 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature