Pi-SAGE: Permutation-invariant surface-aware graph encoder for binding affinity prediction

TL;DR

Pi-SAGE introduces a permutation-invariant, surface-aware graph encoder that explicitly encodes protein surface features, significantly improving the prediction of binding affinity changes in protein complexes.

Contribution

The paper presents Pi-SAGE, a novel graph encoder that explicitly encodes protein surface features and enhances structure-based binding affinity prediction models.

Findings

Explicit surface feature encoding improves prediction accuracy.

Augmenting existing models with Pi-SAGE features enhances performance.

Surface-aware encoding outperforms implicit surface learning methods.

Abstract

Protein surface fingerprint encodes chemical and geometric features that govern protein-protein interactions and can be used to predict changes in binding affinity between two protein complexes. Current state-of-the-art models for predicting binding affinity change, such as GearBind, are all-atom based geometric models derived from protein structures. Although surface properties can be implicitly learned from the protein structure, we hypothesize that explicit knowledge of protein surfaces can improve a structure-based model's ability to predict changes in binding affinity. To this end, we introduce Pi-SAGE, a novel Permutation-Invariant Surface-Aware Graph Encoder. We first train Pi-SAGE to create a protein surface codebook directly from the structure and assign a token for each surface-exposed residue. Next, we augment the node features of the GearBind model with surface features from domain-adapted Pi-SAGE to predict binding affinity change on the SKEMPI dataset. We show that explicitly incorporating local, context-aware chemical properties of residues enhances the predictive power of all-atom graph neural networks in modeling binding affinity changes between wild-type and mutant proteins.