Challenges in antibody structure prediction

Abstract

The tremendous advances in structural biology and the exponential increase of high-quality experimental structures available in the PDB motivated numerous studies to tackle the grand challenge of predicting protein structures. AlphaFold2 revolutionized the field of protein structure prediction, by combining artificial intelligence with evolutionary information. Antibodies are one of the most important classes of biotherapeutic proteins. Accurate structure models are a prerequisite to advance biophysical property predictions and consequently antibody design. Various specialized tools are available to predict antibody structures based on different principles and profiting from current advances in protein structure prediction based on artificial intelligence. Here, we want to emphasize the importance of reliable protein structure models and highlight the enormous advances in the field. At the same time, we want to raise the awareness that protein structure models—and in particular antibody models—may suffer from structural inaccuracies, namely incorrect cis-amid bonds, wrong stereochemistry or clashes. We show that these inaccuracies affect biophysical property predictions such as surface hydrophobicity. Thus, we stress the significance of carefully reviewing protein structure models before investing further computing power and setting up experiments. To facilitate the assessment of model quality, we provide a tool “TopModel” to validate structure models.