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Findings underscore affinity maturation as a powerful strategy for engineering high-affinity antibody fragments by integrating in silico maturation, molecular dynamics simulations, and experimental validation.
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Abstract Background In silico approaches are becoming increasingly important for enhancing the affinity of antibodies and antibody fragments, including single-chain variable fragments (scFvs). In this study, we present an in silico affinity-maturation workflow designed to improve the affinity of an scFv directed against Neisseria meningitidis factor H-binding protein (fHbp) by integrating in silico maturation, molecular dynamics (MD) simulations, and experimental validation. Approximately 300 single-point substitutions were designed at scFv residues predicted to interact with fHbp, and the resulting V3 (R99D) scFv–fHbp complexes were analyzed through all-atom MD simulations. Guided by bioinformatic analyses, the top V3 (R99D) variant was cloned and expressed, and its binding activity was validated by ELISA. Results MD analyses identified five variants, Y35H, L31R, R99D, R99H, and A100K, that exhibited markedly enhanced complex stability. In all variants, the scFv secondary structure remained intact, whereas fHbp showed shrinkage relative to the scFv in the V3 (R99D)–fHbp complexes during the simulations. Hydrogen-bonding patterns and inter-residue distances further supported the improved affinity of the variants relative to the native form. The R99D variant showed the highest binding affinity, with an experimentally measured value of 67.93 × 10⁹ M⁻¹. Conclusion Collectively, these findings underscore affinity maturation as a powerful strategy for engineering high-affinity antibody fragments.
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@article{Ghazanfari2026Affinity,
title = {Affinity improvement of specific scFvs interacting with the Neisseria meningitidis factor H-binding protein: a molecular dynamics simulation approach and experimental study},
author = {Bijan Ghazanfari and Zahra Ebrahimi and Fatemeh Yarian and Arezu Lari and Ali Ghanbariasad and Bahram Kazemi},
journal = {BMC Biotechnology},
year = {2026},
doi = {10.1186/s12896-026-01204-3},
url = {https://doi.org/10.1186/s12896-026-01204-3}
}
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