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...

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Bibliographic Details
Published in:mAbs 2023-12, Vol.15 (1), p.2175319
Main Authors: Fernández-Quintero, Monica L., Kokot, Janik, Waibl, Franz, Fischer, Anna-Lena M., Quoika, Patrick K., Deane, Charlotte M., Liedl, Klaus R.
Format: Article
Language:English
Subjects:
Antibodies
antibody structure
Artificial Intelligence
biophysical surface properties
Computational Biology
Databases, Protein
Protein Conformation
protein structure prediction
Proteins - chemistry
Research Paper
structural inaccuracies
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Summary: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.
ISSN:1942-0862
1942-0870
1942-0870
DOI:10.1080/19420862.2023.2175319