bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2025.02.11.637756

Probing the Role of Membrane in Neutralizing Activity of Antibodies Against Influenza Virus

Ozgulbas, D. G.·Tan, T. J. C.·Wen, P.-C.·Teo, Q. W.·Lv, H.·Ghaemi, Z.·Frank, M.·Wu, N. C.·Tajkhorshid, E.
View original source ↗Export metadata ↓

Abstract

Influenza poses a major health issue globally. Neutralizing antibodies targeting the highly conserved stem region of hemagglutinin (HA) of the influenza virus provide considerable protection against the infection. Using an array of advanced simulation technologies, we developed a high-resolution structural model of full-length, Fab-bound HA in a native viral membrane to characterize direct membrane interactions that govern the efficacy of the antibody. We reveal functionally important residues beyond the antibodys complementary-determining regions that contribute to its membrane binding. Mutagenesis experiments and infectivity assays confirm that deactivating the membrane-binding residues of the antibody decreases its neutralization activity. Therefore, we propose that the association with the viral membrane plays a key role in the neutralization activity of these antibodies. Given the rapid evolution of the influenza virus, the developed model provides a structural framework for the rational design and development of more effective therapeutic antibodies.

Source connections

Explore related subjects

biophysicsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ozgulbas, D. G., Tan, T. J. C., Wen, P.-C., Teo, Q. W., Lv, H., Ghaemi, Z., Frank, M., Wu, N. C., Tajkhorshid, E.. 2025-02-14. Probing the Role of Membrane in Neutralizing Activity of Antibodies Against Influenza Virus. https://doi.org/10.1101/2025.02.11.637756

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Mechanism of molecular recognition revealed through dynamic drug binding pathways to SARS-CoV-2 main protease

Characterization of drug-binding pathways remains experimentally limited by transient intermediates and computationally challenging due to long timescales intractable for conventional molecular dynamics. To address these challenges, we combined solution NMR titrations with weighted ensemble (WE) enhanced sampling simulations to resolve atomistic pathways of nirmatrelvir binding to the SARS-CoV-2 main protease. NMR titration revealed residue-dependent heterogeneity spanning fast, intermediate, and slow exchange regimes. WE simulations complement the NMR by providing insights into unassigned residues and adding time-resolved and three-dimensional structural context. We map key interactions along two distinct binding pathways, provide dynamic explanations for residues involved in resistance, and capture unique backbone conformations compared to those sampled in unbound or bound states. Our comprehensive binding model is consistent with a combined conformational selection and induced fit mechanism in which early transient contacts are made with residues E47 and L50 and allosteric motions are centered around residue V204 of the distal domain. This synergistic application of WE and titration NMR enables a more comprehensive characterization of drug binding than either method alone, providing an integrated framework that may have broader applicability to defining structure-kinetic relationships and guiding design of next-generation inhibitors.

biophysics↗
bioRxiv2026

Discriminating betacoronavirus receptor usage across subgenera using protein structure prediction and molecular dynamics

A critical step in the emergence of a virus is the ability of the viral protein to bind a host receptor and mediate cell entry. For many coronaviruses, this interaction occurs between the Spike S1 subunit and the human ACE2 receptor. Whether this binding interface can be computationally distinguished across unstudied viruses without experimentally resolved protein structures remains an open question. We predicted how 28 emerging coronaviruses may bind to human ACE2 using structural predictions, static interaction prediction programs, and molecular dynamics simulations. To screen the emerging coronaviruses, we predicted a library of S1 structures using AlphaFold. These predicted structures were then used to model the S1-ACE2 interaction with AlphaFold, ClusPro, and HADDOCK. We used known ACE2-binding sarbecoviruses as positive controls and coronaviruses that bind other receptors as negative controls to threshold predicted binding. Contact analysis quantified the predicted binding and revealed that these static interaction prediction methods varied in discriminative power. Less restrained static predictions separated binders from non-binders, whereas heavily restrained docking did not, potentially forcing an interaction where none should exist. This analysis highlighted an emerging coronavirus, Zhejiang2013, as a potential ACE2 binder. We used molecular dynamics simulations to further assess the static predictions and model the interaction over time. Overall, our results indicate that Zhejiang2013 exhibits dynamic interaction patterns consistent with ACE2 binding. Given that two ACE2-binding coronaviruses have caused global pandemics within the past two decades, identifying potential ACE2 binders is critical for early warning and pandemic preparedness.

biophysics↗
bioRxiv2026

De novo design of flexible protein interactions with GuideFlip

De novo design of protein binders requires a target structure. However, for flexible targets, such as intrinsically disordered proteins, this structure does not exist until the binder has stabilized the interaction. Such targets are therefore difficult for methods that separate structure generation from sequence design. We introduce GuideFlip, which co-designs structure and sequence through guided discrete flow matching: binder residues are assigned progressively while the complex is re-predicted at each step, allowing the evolving interface to affect the design process. GuideFlip reduces the hydrophobic bias of direct AlphaFold optimization and improves in silico success rates over existing approaches. We release a database of binder candidates for 177 human disordered proteins. Experimentally, we obtain de novo binders to the C-terminus of -synuclein and the disordered amino terminus of RBX1 with hit rates of 13.5% and 41.7%, respectively, and we confirm the epitopes of selected binders by NMR and mutagenesis. Applying GuideFlip to flexibility on the binder side, we design a nanobody that binds the agonist-bound {beta}1-adrenergic receptor in the active state, but not the receptor in its inactive state, with a 75% hit rate and cryo-EM structure confirming the design. GuideFlip enables protein design where bound structures emerge only upon binding.

biophysics↗