Enhanced Sampling Reveals Metastable Conformations Driving K417N-Mediated Class I Antibody Escape
The continuous emergence of SARS-CoV-2 variants compromises the effectiveness of neutralizing antibodies (nAbs), contributing to immune evasion. Several single-point mutations in the receptor-binding domain (RBD) have been shown to drastically affect the recognition of Class I antibodies. Understanding the molecular mechanisms behind such single-point mutations remains challenging, especially as weak binding affinity prevents the characterization of complex structures through conventional experimental methods. Here, we employ enhanced sampling molecular dynamics (MD) simulations, the generalized replica exchange with solute tempering (gREST), to investigate the structural and dynamic impacts of the K417N mutation on NT-193 (Class I) binding to RBD. Although stable conformations of wild-type and mutants show similar binding interactions, the mutation profoundly reshapes the transient metastable states. At the key interface between the heavy and light chains, the substitution of lysine with asparagine disrupts key interactions with the heavy chain in metastable states, facilitating antibody dissociation. Consistent with simulation results, surface plasmon resonance (SPR) experiments confirm that K417N significantly increases the dissociation rate, reducing the overall binding affinity to RBD. These findings illustrate the importance of capturing metastable conformations to fully understand mutation-driven immune evasion mechanisms. Moreover, this study demonstrates the power of applying gREST to unravel essential insights into transient and weakly interacting antibody-antigen complexes, facilitating the rational design and optimization of next-generation antibody therapeutics capable of overcoming viral immune escape.
Authors
- Taishi Onodera (ORCID: https://orcid.org/0000-0002-7079-3339)
- Yoshimasa Takahashi (ORCID: https://orcid.org/0000-0001-6342-4087)
- Hisham Dokainish (ORCID: https://orcid.org/0000-0002-4387-4790)
- Katsumi Maenaka (ORCID: https://orcid.org/0000-0002-5459-521X)
- Takashi Tadokoro (ORCID: https://orcid.org/0000-0002-3390-4861)
- Shunsuke Kita (ORCID: https://orcid.org/0000-0003-3969-302X)
- Xu Pan
Institutions
- Hokkaido University (JP)
- Infectious Diseases Institute (UG)
- Discovery Laboratories (United States) (US)
- Drug Discovery Laboratory (Norway) (NO)
- Pharmaceutical Biotechnology (Czechia) (CZ)
- Sanyo-Onoda City University (JP)
Publication Details
- Journal
- Journal of Chemical Information and Modeling
- Published
- 2025-10-21
- DOI
- https://doi.org/10.1021/acs.jcim.5c01396
- Primary Topic
- Monoclonal and Polyclonal Antibodies Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00