Geometric Graph Learning for Predicting Protein Mutation Effect

Proteins govern a wide range of biological systems. Evaluating the changes in protein properties upon protein mutation is a fundamental application of protein design, where modeling the 3D protein structure is a principal task for AI-driven computational approaches. Existing deep learning (DL) approaches represent the protein structure as a 3D geometric graph and simplify the graph modeling to different degrees, thereby failing to capture the low-level atom patterns and high-level amino acid patterns simultaneously. In addition, limited training samples with ground truth labels and protein structures further restrict the effectiveness of DL approaches. In this paper, we propose a new graph learning framework, Hierarchical Graph Invariant Network (HGIN), a fine-grained and data-efficient graph neural encoder for encoding protein structures and predicting the mutation effect on protein properties. For fine-grained modeling, HGIN hierarchically models the low-level interactions of atoms and the high-level interactions of amino acid residues by Graph Neural Networks. For data efficiency, HGIN preserves the invariant encoding for atom permutation and coordinate transformation, which is an intrinsic inductive bias of property prediction that bypasses data augmentations. We integrate HGIN into a Siamese network to predict the quantitative effect on protein properties upon mutations. Our approach outperforms 9 state-of-the-art approaches on 3 protein datasets. More inspiringly, when predicting the neutralizing ability of human antibodies against COVID-19 mutant viruses, HGIN achieves an absolute improvement of 0.23 regarding the Spearman coefficient.