ToxGIN: an <i>In silico</i> prediction model for peptide toxicity via graph isomorphism networks integrating peptide sequence and structure information
Qi Yu, Zhixing Zhang, Guixia Liu, Weihua Li, Yun Tang
Abstract
Peptide drugs have demonstrated enormous potential in treating a variety of diseases, yet toxicity prediction remains a significant challenge in drug development. Existing models for prediction of peptide toxicity largely rely on sequence information and often neglect the three-dimensional (3D) structures of peptides. This study introduced a novel model for short peptide toxicity prediction, named ToxGIN. The model utilizes Graph Isomorphism Network (GIN), integrating the underlying amino acid sequence composition and the 3D structures of peptides. ToxGIN comprises three primary modules: (i) Sequence processing module, converting peptide 3D structures and sequences into information of nodes and edges; (ii) Feature extraction module, utilizing GIN to learn discriminative features from nodes and edges; (iii) Classification module, employing a fully connected classifier for toxicity prediction. ToxGIN performed well on the independent test set with F1 score = 0.83, AUROC = 0.91, and Matthews correlation coefficient = 0.68, better than existing models for prediction of peptide toxicity. These results validated the effectiveness of integrating 3D structural information with sequence data using GIN for peptide toxicity prediction. The proposed ToxGIN and data can be freely accessible at https://github.com/cihebiyql/ToxGIN.