计算机科学
生成语法
互补性(分子生物学)
序列(生物学)
生成模型
图形
计算生物学
互补决定区
先验与后验
算法
人工智能
肽序列
生物
理论计算机科学
基因
遗传学
认识论
哲学
作者
Wengong Jin,Jeremy Wohlwend,Regina Barzilay,Tommi S. Jaakkola
出处
期刊:Cornell University - arXiv
日期:2021-10-09
被引量:2
标识
DOI:10.48550/arxiv.2110.04624
摘要
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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