Towards Stable Representations for Protein Interface Prediction

Ziqi Gao, Zijing Liu, Yu Li, Jia Li

Keywords: protein interface, graph learning, adversarial training, stable representation

TL;DR: We regard the protein flexibility as an attack on the trained model and aim to defend against it for improving protein interface prediction.

Abstract: The knowledge of protein interactions is crucial but challenging for drug discovery applications. This work focuses on protein interface prediction, which aims to determine whether a pair of residues from different proteins interact. Existing data-driven methods have made significant progress in effectively learning protein structures. Nevertheless, they overlook the conformational changes (i.e., flexibility) within proteins upon binding, leading to poor generalization ability. In this paper, we regard the protein flexibility as an attack on the trained model and aim to defend against it for improved generalization. To fulfill this purpose, we propose ATProt, an adversarial training framework for protein representations to robustly defend against the attack of protein flexibility. ATProt can theoretically guarantee protein representation stability under complicated protein flexibility. Experiments on various benchmarks demonstrate that ATProt consistently improves the performance for protein interface prediction. Moreover, our method demonstrates broad applicability, performing the best even when provided with testing structures from structure prediction models like ESMFold and AlphaFold2.

Primary Area: Machine learning for other sciences and fields

Submission Number: 15171