Ovek Damla, Keskin Ozlem, Gursoy Attila
KUIS AI Center, Koç University, Istanbul 34450, Turkey.
Computer Engineering, Koç University, Istanbul 34450, Turkey.
J Chem Inf Model. 2024 Apr 22;64(8):2979-2987. doi: 10.1021/acs.jcim.3c01788. Epub 2024 Mar 25.
Proteins are vital components of the biological world and serve a multitude of functions. They interact with other molecules through their interfaces and participate in crucial cellular processes. Disruption of these interactions can have negative effects on organisms, 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 work, 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 protein-protein interaction interfaces from unlabeled data and 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 data set and show that it provides a promising solution for validating protein-protein interfaces.
蛋白质是生物世界的重要组成部分，具有多种功能。它们通过其界面与其他分子相互作用，并参与关键的细胞过程。这些相互作用的破坏会对生物体产生负面影响，凸显了研究蛋白质-蛋白质界面以开发针对疾病的靶向疗法的重要性。因此，开发一种可靠的方法来研究蛋白质-蛋白质相互作用至关重要。在这项工作中，我们提出了一种使用学习到的界面表示来验证蛋白质-蛋白质界面的方法。该方法包括使用基于图的对比自动编码器架构和变换器从未标记数据中学习蛋白质-蛋白质相互作用界面的表示，然后通过图神经网络使用学习到的表示对其进行验证。我们的方法在测试集上的准确率达到了0.91，优于现有的基于图神经网络的方法。我们在一个基准数据集上证明了我们方法的有效性，并表明它为验证蛋白质-蛋白质界面提供了一个有前景的解决方案。
