Allergen cross-reactivity, in which a secondary allergen binds specific IgE antibodies of an organism sensitized to a primary allergen, represents a clinically important phenomenon, especially in pollen-food allergy syndrome.
Although the World Health Organization (WHO) recommends predicting such reactivity based on local alignment of primary sequences, the low sequence identity of some clinically confirmed cross-reactive allergens (e.g. Act d 11, Vir r 6) has indicated the importance of tertiary structure.
We aimed to investigate whether the comparison of 3D structures would reveal a higher degree of structural similarity. We also aimed to assess the quality of computationally predicted structures, as the number of known protein sequences is significantly higher than that of three-dimensional structures, which are experimentally determined.
In this study we, using three isoforms of the major birch pollen allergen (Bet v 1.0101, 1.0106, 1.0107) as references, analyzed 81 cross-reactive food allergens. We used the COMPASS tool to perform sequence alignment of whole primary sequence, followed by DALI comparison of their experimentally determined (PDBe) and predicted (AlphaFold) tertiary structures.
The results showed:
a. In all analyzed allergens 3D structural similarity was higher than sequence identity. The average difference was 30.0 percentage points for predicted and 22.6 for experimentally determined structures.
b. Significantly higher 3D structural similarity was observed even in allergens with very low sequence identity. Allergen Act d 11 (22.4% sequence identity) exhibited 63.9% similarity for the predicted and 59.7% for the experimental structure.
c. We also found that AlphaFold predicted 3D structures with varying accuracy (62.5 % of allergens had Q>0.8, however comparison with experimental structures revealed discrepancies (e.g. Act d 8.0101: Q=0.57)).
Based on these results, we conclude that incorporating 3D structure-based bioinformatic analysis into WHO guidelines could enhance the reliability of predicting allergen cross-reactivity, despite the limitations of current prediction tools.
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