CROSS-PROTEIN WASSERSTEIN TRANSFORMER FOR PROTEIN-PROTEIN INTERACTIONS Paper ID: 3822

Abstract

Previous studies reveal intimate relationships between the structure and function of proteins. Motivated by this, for protein-protein interactions (PPIs), we hypothesize that cross-protein structural correspondence, including both global correlation and local co-occurrence, poses a great influence. Accordingly, a novel deep learning framework named Cross-Protein Wasserstein Transformer (CPWT) is proposed to predict PPI sites through fine-grained cross-graph structural modeling. Considering the irregular architecture of acid sequences, for a pair of proteins, graphs are constructed to describe them. Then, a core Cross-Graph Transformer (CGT) module of two branches (e.g. ligand and receptor branches) is proposed for cross-protein structural modeling. Specifically, in this module, Wasserstein affinity across graphs is calculated through cross-graph query (i.e. ligand (query) -receptor (key) or the converse), based on which the multi-head attention is derived to adaptively mine fine-grained cues of PPI sites. By stacking CGT modules, the two branches in CGT are co-evolved in a deep architecture during forward inference, hence being powerful and advantageous in cross-protein structural representation and fine-grained learning. We verify the effectiveness of our CPWT framework by conducting comprehensive experiments on multiple PPI datasets, and further visualize the learned fine-grained saliencies for intuitive understanding.

1. INTRODUCTION

Proteins are chains of amino acids, and physical interaction between proteins is essential to life. The protein-protein interaction (PPI) (Figure 1 shows an example) determines molecular and cellular mechanisms, and thus plays a crucial role in biological processes including the gene expression, proliferation of cells, etc. Moreover, as proteins are predominant drug targets, characterizing PPIs at the fine-grained level, e.g. identifying protein interaction sites for PPIs, would provide significant insight into biological mechanisms, and has important application in drug design, disease treatment (Ryan & Matthews, 2005) , and target discovery. For this reason, the research on PPI prediction has drawn increasing attention. In the early stage, sundry experimental assays have been widely applied to PPI identification, such as nuclear magnetic resonance(NMR) (Wuthrich, 1989), X-ray crystallography (Svergun et al., 2001) and high-throughput screening methods (Song et al., 2011) . However, identifying the binding sites based on these methods is often time-consuming and resource-expensive. Then, with more and more protein structure data available (Berman et al., 2000) , computational methods are developed to predict protein-protein interaction sites, which can be divided into two categories, i.e. protein-protein docking and data-driven methods. For the protein-protein docking (Porter et al., 2019; Halperin et al., 2002) , the fundamental principle is the steric complementarity at protein-protein interfaces. However, it suffers from the tremendous search space and requirement of expert-defined scoring functions in the searching and scoring processes for predicting complex structures. For data-driven methods, a number of machine learning algorithms with shallow structures are first proposed for PPIs in the early stage. Generally, they can be divided into three categories, i.e. sequencebased methods (Murakami & Mizuguchi, 2010; Zhang et al., 2019; Jurtz et al., 2017; Haberal & Ogul, 2017; Zheng et al., 2018 ), structure-based methods (Du et al., 2016; Bradford & Westhead, 2005; Neuvirth et al., 2004) and those ones based on mixed information (Afsar Minhas et al., 2014; Li et al., 2012; Northey et al., 2018; Porollo & Meller, 2007) . Then, inspired by the success of deep learning in vision tasks, deep neural networks are employed for protein science, e.g. the success of DeepMind's Alphafold2 (Jumper et al., 2021) for structure prediction. Specifically for PPIs, to utilize the powerful structure modeling ability, recent works (Bryant et al., 2022; Gao et al., 2022; Evans et al., 2021) including AF2Complex (Gao et al., 2022) and AlphaFold-Multimer (Evans et al., 2021) propose to

