De novo designing immunoglobulin‐like frameworks that allow for functional loop diversification shows great potential for crafting antibody‐like scaffolds with fully customizable structures and functions. In this work, we combined de novo parametric design with deep‐learning methods for protein structure prediction and design to explore the structural landscape of 7‐stranded immunoglobulin domains. After screening folding of nearly 4 million designs, we have assembled a structurally diverse library of ~50,000 immunoglobulin domains with high‐confidence AlphaFold2 predictions and structures diverging from naturally occurring ones. The designed dataset enabled us to identify structural requirements for the correct folding of immunoglobulin domains, shed light on β‐sheet–β‐sheet rotational preferences and how these are linked to functional properties. Our approach eliminates the need for preset loop conformations and opens the route to large‐scale de novo design of immunoglobulin‐like frameworks.

中文翻译：

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通过大规模从头设计的免疫球蛋白折叠的结构景观

从头设计允许功能环多样化的类免疫球蛋白框架，显示出制作具有完全可定制结构和功能的类抗体支架的巨大潜力。在这项工作中，我们将从头参数设计与蛋白质结构预测和设计的深度学习方法相结合，以探索 7 链免疫球蛋白结构域的结构景观。在筛选了近 400 万种设计的折叠后，我们组装了一个包含约 50,000 个免疫球蛋白结构域的结构多样的库，其中具有高置信度的 AlphaFold2 预测和与自然发生的结构不同的结构。设计的数据集使我们能够确定免疫球蛋白结构域正确折叠的结构要求，阐明 β-片层-β-片层旋转偏好以及它们如何与功能特性相关。我们的方法消除了对预设环构象的需要，并为免疫球蛋白样框架的大规模从头设计开辟了道路。