The structural landscape of the immunoglobulin fold by large‐scale de novo design

Abstract

AbstractDe 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.