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

By Wengong Jin, Jeremy Wohlwend, Regina Barzilay, Tommi S. Jaakkola. 2021

172 citations recorded by the source corpus.

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.

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Citations in the corpus

TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction (2022, background): “These works have been shown great potential in various applications like protein structure modeling [13, 17–19], molecular low-energy generation prediction [24–26], property/function prediction [22, 23] and molecule design [20, 21].”
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (2021, background): “antibodies (Jin et al., 2021)) or for use cases when protein docking models are just a component of significantly larger end-to-end architectures targeting more involved biological scenarios, for example representing a drug’s mechanism of action or modeling cellular processes with a single machine l”
Masked Inverse Folding with Sequence Transfer for Protein Representation Learning (2023, background): “Deep learning-based inverse folding is usually parametrized as a graph neural network (GNN) (Ingra-ham et al., 2019; Strokach et al., 2020; Jin et al., 2021; Jing et al., 2020; Hsu et al., 2022; Dauparas et al., 2022; Shi et al., 2022) or SE(3)-equivariant transformer (McPartlon et al., 2022) that…”
A 3D Generative Model for Structure-Based Drug Design (2022, background): “Another line of work studies generating molecules directly in 3D space [8, 28, 29, 20, 30, 15].”