Exploring the ability of the MD+FoldX method to predict SARS-CoV-2 antibody escape mutations using large-scale data.

Autor:	Chi LA; Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID 83843, USA., Barnes JE; Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID 83843, USA., Suresh Patel J; Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID 83843, USA.; Department of Chemical and Biological Engineering, University of Idaho, Moscow, ID 83843, USA., Ytreberg FM; Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID 83843, USA.; Department of Physics, University of Idaho, Moscow, ID 83843, USA.
Jazyk:	angličtina
Zdroj:	BioRxiv : the preprint server for biology [bioRxiv] 2024 May 22. Date of Electronic Publication: 2024 May 22.
DOI:	10.1101/2024.05.22.595230
Abstrakt:	Antibody escape mutations pose a significant challenge to the effectiveness of vaccines and antibody-based therapies. The ability to predict these escape mutations with computer simulations would allow us to detect threats early and develop effective countermeasures, but a lack of large-scale experimental data has hampered the validation of these calculations. In this study, we evaluate the ability of the MD+FoldX molecular modeling method to predict escape mutations by leveraging a large deep mutational scanning dataset, focusing on the SARS-CoV-2 receptor binding domain. Our results show a positive correlation between predicted and experimental data, indicating that mutations with reduced predicted binding affinity correlate moderately with higher experimental escape fractions. We also demonstrate that better performance can be achieved using affinity cutoffs tailored to distinct antibody-antigen interactions rather than a one-size-fits-all approach. We find that 70% of the systems surpass the 50% precision mark, and demonstrate success in identifying mutations present in significant variants of concern and variants of interest. Despite promising results for some systems, our study highlights the challenges in comparing predicted and experimental values. It also emphasizes the need for new binding affinity methods with improved accuracy that are fast enough to estimate hundreds to thousands of antibody-antigen binding affinities. Competing Interests: Financial interests The authors have no competing interests to declare that are relevant to the content of this article.
Databáze:	MEDLINE
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