Related papers: A Hierarchical Training Paradigm for Antibody Structure-sequence Co-design

A Hierarchical Training Paradigm for Antibody Structure-sequence Co-design

URL: http://arxiv.org/abs/2311.16126v1
Date: Mon, 30 Oct 2023 02:39:15 GMT
Title: A Hierarchical Training Paradigm for Antibody Structure-sequence Co-design
Authors: Fang Wu, Stan Z. Li
Abstract summary: We propose a hierarchical training paradigm (HTP) for the antibody sequence-structure co-design. HTP consists of four levels of training stages, each corresponding to a specific protein modality. Empirical experiments show that HTP sets the new state-of-the-art performance in the co-design problem.
Score: 54.30457372514873
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Therapeutic antibodies are an essential and rapidly expanding drug modality. The binding specificity between antibodies and antigens is decided by complementarity-determining regions (CDRs) at the tips of these Y-shaped proteins. In this paper, we propose a hierarchical training paradigm (HTP) for the antibody sequence-structure co-design. HTP consists of four levels of training stages, each corresponding to a specific protein modality within a particular protein domain. Through carefully crafted tasks in different stages, HTP seamlessly and effectively integrates geometric graph neural networks (GNNs) with large-scale protein language models to excavate evolutionary information from not only geometric structures but also vast antibody and non-antibody sequence databases, which determines ligand binding pose and strength. Empirical experiments show that HTP sets the new state-of-the-art performance in the co-design problem as well as the fix-backbone design. Our research offers a hopeful path to unleash the potential of deep generative architectures and seeks to illuminate the way forward for the antibody sequence and structure co-design challenge.

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