Towards the prediction of non-peptidic epitopes

In-silico methods for the prediction of epitopes can support and improve workflows for vaccine design, antibody production, and disease therapy. So far, the scope of B cell and T cell epitope prediction has been directed exclusively towards peptidic antigens. Nevertheless, various non-peptidic molecular classes can be recognized by immune cells. These compounds have not been systematically studied yet, and prediction approaches are lacking. The ability to predict the epitope activity of non-peptidic compounds could have vast implications; for example, for immunogenic risk assessment of the vast number of drugs and other xenobiotics. Here we present the first general attempt to predict the epitope activity of non-peptidic compounds using the Immune Epitope Database (IEDB) as a source for positive samples. The molecules stored in the Chemical Entities of Biological Interest (ChEBI) database were chosen as background samples. The molecules were clustered into eight homogeneous molecular groups, and classifiers were built for each cluster with the aim of separating the epitopes from the background. Different molecular feature encoding schemes and machine learning models were compared against each other. For those models where a high performance could be achieved based on simple decision rules, the molecular features were then further investigated. Additionally, the findings were used to build a web server that allows for the immunogenic investigation of non-peptidic molecules (). The prediction quality was tested with samples from independent evaluation datasets, and the implemented method received noteworthy Receiver Operating Characteristic-Area Under Curve (ROC-AUC) values, ranging from 0.69-0.96 depending on the molecule cluster.& nbsp;Author summary & nbsp;Small molecules found in cosmetics, foodstuffs, dyes, and industrial materials, but also those produced by plants, bacteria, and animals can trigger strong reactions of the human immune system and can therefore be hazardous to health. In the present work, several thousand immune-reactive small molecules (so-called non-peptidic epitopes) were classified by molecular structure and studied with the aim of identifying specific parts of the molecules responsible for such immune responses. Using a machine-learning approach (random forests and neural networks), we identified some substructures that appear strikingly often in non-peptidic epitopes and which may be responsible for the hazardous immune response. Such knowledge may help to explain allergic reactions to chemicals and also to minimize the health risks of new chemicals in industrial production. To support this endeavor, we have implemented the method in a publicly available web application. This can be used for the prediction and identification of non-peptidic epitopes and their underlying substructures.

Automated summary

In this study, the authors attempted to predict the epitope activity of non-peptidic compounds and developed a web server for immunogenic investigation. They identified specific molecular substructures that are frequently found in non-peptidic epitopes and may be responsible for immune responses. This knowledge can help explain allergic reactions to chemicals and reduce health risks in industrial production.