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. Here, we describe deep learning approaches for scaffolding such functional sites without needing to prespecify 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 these two 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.

中文翻译：

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使用深度学习搭建蛋白质功能位点


蛋白质的结合和催化功能通常由整个蛋白质结构中固定的少量功能残基介导。在这里，我们描述了用于搭建此类功能位点的深度学习方法，而无需预先指定支架的折叠或二级结构。第一种方法“受限幻觉”优化序列，使其预测的结构包含所需的功能位点。第二种方法是“修复”，从功能位点开始，填充额外的序列和结构，通过经过专门训练的 RoseTTAFold 网络，在单次正向传递中创建可行的蛋白质支架。我们使用这两种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，并结合计算机模拟和实验测试来验证设计。