Prediction of Enzyme Function Based on a Structure Relation Network

Traditional biological experimental methods for enzyme function prediction have not been able to meet the increasing number of newly discovered enzymes measured by X-ray crystallography or magnetic resonance. A good computational model and protein feature representation for predicting enzymatic function can quickly annotate the functions of enzymes in chemical reactions. Existing machine learning methods usually compress protein 3D structure information into pictures convenient for convolutional neural networks (CNNs) and discard a large amount of relation information. Therefore, we proposed a method using the relation between amino acids directly to predict enzyme function. First, in addition to common structural features, we introduced a new structural feature, the relative angle of the amino acid (C -Cα -C) plane. Additionally, all protein structure features were organized into a new representation. Then, a structure relation network (SRN) to learn four features of the enzyme was established. Finally, the proposed model was evaluated on a large dataset containing 42,699 enzymes and achieved 92.08% classification accuracy, showing improvements compared with previous works.