Challenges in antibody structure prediction

Advances in structural biology and the exponential increase in the amount of high-quality experimental structural data available in the Protein Data Bank has motivated numerous studies to tackle the grand challenge of predicting protein structures. In 2020 AlphaFold2 revolutionized the field using a combination of artificial intelligence and the evolutionary information contained in multiple sequence alignments. 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. Specialized tools used to predict antibody structures based on different principles have profited from current advances in protein structure prediction based on artificial intelligence. Here, we emphasize the importance of reliable protein structure models and highlight the enormous advances in the field, but we also aim to increase awareness that protein structure models, and in particular antibody models, may suffer from structural inaccuracies, namely incorrect cis-amide bonds, wrong stereochemistry or clashes. We show that these inaccuracies affect biophysical property predictions such as surface hydrophobicity. Thus, we stress the importance 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.

Automated summary

The increased availability of high-quality experimental structural data and advances in structural biology have driven research on predicting protein structures. In 2020, AlphaFold2 revolutionized the field by combining artificial intelligence with evolutionary information from multiple sequence alignments. Accurate protein structure models are crucial for biophysical property predictions and antibody design. However, it is important to be aware of potential inaccuracies in protein structure models, particularly in antibody models, such as incorrect bonds and stereochemistry. We emphasize the need to carefully review these models before further computational analysis and experimental studies. To aid in assessing model quality, we provide a tool called TopModel for validating structure models.