In silico B-cell epitope prediction and molecular docking of Aspergillus allergens targeting improved ABPA diagnosis

The objective of this study is to in silico predict Aspergillus fumigatus specific B-cell epitopes with a focus on enhancing Allergic Bronchopulmonary Aspergillosis (ABPA) diagnostic precision by using and to validate using molecular docking of Aspergillus fumigatus specific B-cell epitopes, aiming to overcome current serological and clinical method limitations and to support specific therapies and preventive strategies for better ABPA management.

The sequences of Asp f1, Asp f2, Asp f3, and Asp f4 from NCBI were analyzed using IEDB-AR for B-cell epitope prediction. Structural modeling and molecular docking analysis were conducted using MODELER and HADDOCK, respectively, with visualization via PyMOL and PDBe PISA.

For Asp f1, two IgE-specific (40-47) and four IgG-specific (33-76, 125-148) B-cell epitopes were predicted. Asp f3 had one IgG-specific epitope (47-73), and Asp f4 had two IgG-specific epitopes (52-133) with no IgE epitopes. Asp f2 had eight IgE-specific epitopes (56-63, 93-99, 136-146, 153-160, 185-194, 200-206, 229-239) with IgPred scores above 0.931 and no IgG-specific epitopes. Molecular docking with HADDOCK Z-scores showed strong interactions between IgE and Asp f1 and Asp f2 epitopes. PyMOL and PISA-EBI identified key residues: LYS43 in Asp f1 forms a salt bridge with the IgE heavy chain. In Asp f2, out of nineteen identified residues, six residues (LYS 94, ARG 153, ASP 200, ASP 204, ASP 207 and GLU 233) were confirmed as part of the predicted IgE epitopes, exhibiting significant interactions with IgE, in agreement with both PyMOL and PISA analysis.

This study aimed to enhance ABPA diagnostics by identifying key B-cell epitopes of Aspergillus fumigatus through in silico prediction and molecular docking, a way to support personalized therapies and preventive strategies in future.