Scaffolding protein functional sites using deep learning.

Scaffolding protein functional sites using deep learning.
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使用深度学习构建蛋白质功能位点。

DOI:
10.1126/science.abn2100
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发表时间:
2022-07-22
期刊:
Science (New York, N.Y.)
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其他
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蛋白质的结合和催化功能通常是由蛋白质整体结构保持在适当位置的少量功能残基介导的。我们描述了用于支架这样的功能站点的深度学习方法,而不需要预先指定支架的折叠或二级结构。第一种方法,“受限幻觉”,对序列进行优化,使其预测的结构包含所需的功能位点。第二种方法,“修复”,从功能部位开始,填充额外的序列和结构,通过专门训练的RosettaFold网络,在一次正向传递中创建一个可行的蛋白质支架。我们使用这些方法来设计候选免疫原、受体陷阱、金属蛋白、酶和蛋白质结合蛋白,并使用计算机和实验测试相结合的方法来验证设计。
The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. We describe deep learning approaches for scaffolding such functional sites without needing to pre-specify the fold or secondary structure of the scaffold. The first approach, “constrained hallucination”, optimizes sequences such that their predicted structures contain the desired functional site. The second approach, “inpainting”, starts from the functional site and fills in additional sequence and structure to create a viable protein scaffold in a single forward pass through a specifically trained RosettaFold network. We use the methods to design candidate immunogens, receptor traps, metalloproteins, enzymes, and protein-binding proteins, and validate the designs using a combination of in silico and experimental tests.
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