Challenges in antibody structure prediction

Abstract

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.

Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability

“…These observations align with recent reports by others (33,34,114). Importantly, we found that although ABB2 (37) and IgFold (55) have been found to be superior to ABB (36,37,117) (which was used in this work for the majority of the results), the correlation of ABB with all other tools (experimental) in terms of structure-based DPs was low and did not differ to the aforementioned tools. Strikingly, we found that structures obtained by computational structure prediction methods belonged to a common underlying ensemble structural distribution as revealed by MD (Supplementary File; Supp.…”

“…MD is important for a fuller understanding of antibody systems as it provides insight into the flexibility and fluctuations of antibody structure and developability parameters ( 29 , 33 , 34 , 117 , 119 , 120 ). Specifically, Park and Izadi found that antibody developability surface descriptor parameters (e.g., positive electrostatic potential on the surface of the CDR region ( 121 )) vary extensively as a function of the structure prediction method used, which is in line with the findings in this manuscript (Supplementary File).…”

Structural modeling of antibody variable regions using deep learning—progress and perspectives on drug discovery

“…However, the predicted structures are often of bad physical quality [e.g., atomic clashes, D-amino acids, etc. (Fernández-Quintero et al, 2023)], requiring refinement.…”

Assessing AF2’s ability to predict structural ensembles of proteins

“…As for nanobodies, antibodies pose challenges in protein structure prediction. 70 While five of their six CDR loops tend to adopt different canonical conformations, CDR-H3 has proven to be difficult to model due to its increased diversity in sequence and length. 71 Here, we examine an antibody (anti-hemagglutinin Fv 17/9 influenza antibody) that exhibits distinct conformations in the CDR-H3 (Figure 9B).…”