ProInterVal: Validation of Protein-Protein Interfaces through Learned Interface Representations

Abstract

Proteins are vital components of the biological world, serving a multitude of functions. They interact with other molecules through their interfaces and participate in crucial cellular processes. Disruptions to these interactions can have negative effects on the organism, highlighting the importance of studying protein-protein interfaces for developing targeted therapies for diseases. Therefore, the development of a reliable method for investigating protein-protein interactions is of paramount importance. In this research, we present an approach for validating protein-protein interfaces using learned interface representations. The approach involves using a graph-based contrastive autoencoder architecture and a transformer to learn representations of proteinprotein interaction interfaces from unlabeled data, then validating them through learned representations with a graph neural network. Our method achieves an accuracy of 0.91 for the test set, outperforming existing GNN-based methods. We demonstrate the effectiveness of our approach on a benchmark dataset and show that it provides a promising solution for validating protein-protein interfaces.