Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design

TL;DR

This paper introduces an iterative graph neural network model that co-designs antibody CDR sequences and structures, improving the generation of antibodies with enhanced binding and neutralization capabilities, notably against SARS-CoV-2.

Contribution

It presents a novel iterative refinement approach for simultaneous antibody sequence and structure generation using graph neural networks, advancing beyond prior structure-conditioned methods.

Findings

Achieves higher log-likelihood scores than previous models.

Outperforms baselines in designing SARS-CoV-2 neutralizing antibodies.

Demonstrates effective co-design of antibody sequences and structures.

Abstract

Antibodies are versatile proteins that bind to pathogens like viruses and stimulate the adaptive immune system. The specificity of antibody binding is determined by complementarity-determining regions (CDRs) at the tips of these Y-shaped proteins. In this paper, we propose a generative model to automatically design the CDRs of antibodies with enhanced binding specificity or neutralization capabilities. Previous generative approaches formulate protein design as a structure-conditioned sequence generation task, assuming the desired 3D structure is given a priori. In contrast, we propose to co-design the sequence and 3D structure of CDRs as graphs. Our model unravels a sequence autoregressively while iteratively refining its predicted global structure. The inferred structure in turn guides subsequent residue choices. For efficiency, we model the conditional dependence between residues inside and outside of a CDR in a coarse-grained manner. Our method achieves superior log-likelihood on the test set and outperforms previous baselines in designing antibodies capable of neutralizing the SARS-CoV-2 virus.