bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2024.07.14.603367

Large-Scale Computational Modeling of H5 Influenza Variants Against HA1-Neutralizing Antibodies

Ford, C. T.·Yasa, S.·Obeid, K.·Guirales-Medrano, S.·White, R. A.·Janies, D.
View original source ↗Export metadata ↓

Abstract

The United States Department of Agriculture has recently released reports that show samples from 2022-2024 of highly pathogenic avian influenza (H5N1) have been detected in mammals and birds (1). To date, the United States Centers for Disease Control reports that there have been 27 humans infected with H5N1 in 2024 (2). The broader potential impact on human health remains unclear. In this study, we computationally model 1,804 protein complexes consisting of various H5 isolates from 1959 to 2024 against 11 hemagglutinin domain 1 (HA1)-neutralizing antibodies. This study shows a trend of weakening binding affinity of existing antibodies against H5 isolates over time, indicating that the H5N1 virus is evolving immune escape of our medical defenses. We also found that based on the wide variety of host species and geographic locations in which H5N1 was observed to have been transmitted from birds to mammals, there is not a single central reservoir host species or location associated with H5N1s spread. These results indicate that the virus has potential to move from epidemic to pandemic status in the near future. This study illustrates the value of high-performance computing to rapidly model protein-protein interactions and viral genomic sequence data at-scale for functional insights into medical preparedness. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSPrevious studies have shown cases of avian influenza transmissions to mammals that are increasing in frequency, which is of concern to human health. Since 1997, nearly a thousand H5N1 cases have been reported in humans with a 52% fatality rate. Previous analyses have indicated specific mutations on the hemagglutinin protein that allow for this "host jumping" between birds and mammals (3). There are also existing evidence of recent viral strains with reduced neutralization to sera (4). Added value of this studyThis study provides a comprehensive look at the mutational space of hemagglutinin of H5N1 influenza and presents computational predictions of the binding between various HA1-neutralizing antibodies derived from infected vaccinated patients and humanized mice and 1,804 representative H5 HA1 proteins. These analyses show a weakening trend of existing antibodies. We also confirm that the mutations found in other studies that enable zoonosis also affect binding affinities of the antibodies tested. Furthermore, through phylogenetic analyses, we quantify the avian-to-mammalian transmissions from 1959 to 2024 and show a persistent circulation of isolates between North America and Europe. Taken together, the continuous transmission of H5N1 from birds to mammals and the increase in immuno-evasive HA strains in mammals sampled over time suggest that antigenic drift is a source of spillover risk. Implications of all the available evidenceOur findings indicate that the worsening in antibody binding, along with the increase in of avian-to-mammalian H5N1 influenza transmissions are risks to public health. Through the findings of previous studies along with the predictions reported in this study, we can now monitor specific mutations of interest, quantified by their potential impact on antibody evasion, and inform public health monitoring of circulating isolates in 2024 and beyond. In addition, these findings may help to guide future vaccine and therapeutic development in the fight against H5N1 influenza infections in humans.

Source connections

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ford, C. T., Yasa, S., Obeid, K., Guirales-Medrano, S., White, R. A., Janies, D.. 2024-07-17. Large-Scale Computational Modeling of H5 Influenza Variants Against HA1-Neutralizing Antibodies. https://doi.org/10.1101/2024.07.14.603367

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

KEEP EXPLORING

Related preprints

bioRxiv2026

Targeted finetuning enables co-folding models to learn ligand-induced protein conformational states

Advances in protein structure prediction have enabled all-atom protein-ligand co-folding models that predict bound conformations directly from sequence and small-molecule structure. However, these models often fail to generalize to novel binding sites or alternative protein conformational states, limiting their utility for chemical biology and drug discovery. Here we show this limitation reflects training data bias rather than architectural constraints and can be overcome through targeted finetuning. Using ten previously unseen X-ray structures of Werner (WRN) helicase from a drug discovery program, we finetune Boltz-1 to learn both an allosteric binding site and a large conformational change locking the enzyme in an inactive state, while preserving accuracy on the ATP-bound state. The finetuned model generalizes to different chemical series and transfers the conformational logic across RecQ-family helicases in a binding-site sequence-dependent manner. This approach provides a blueprint for adapting foundation models as new structural and mechanistic data emerge, enabling co-folding networks to capture ligand-induced conformational switches and binding poses absent from their training data but central to biological regulation and therapeutic intervention.

bioinformatics↗
bioRxiv2026

Benchmarking single-cell foundation models for aging biology

Single cell foundation models (scFMs) provide representations of cellular states, but their utility across biological questions in aging research remains unclear. We established a benchmark of cellular representations for aging research, evaluating ten general-purpose scFMs, three aging-specific models and conventional methods across five biological questions using more than 2.5 million single cell transcriptomes. Using frozen pretrained representations, Geneformer performed best among scFMs for chronological age prediction and age pseudotime concordance, although 2,000 highly variable genes achieved higher mean performance. Several scFMs captured positive molecular age shifts across three disease contexts, consistent with reported aging-associated changes. SCimilarity performed well for rare cellular state identification across out-of-distribution datasets, exceeding aging specific models and conventional baselines. At the gene level, scGPT showed the highest recovery of reference TF target interactions, including aging-related regulatory hubs. Overall, scFMs supported diverse aging analyses, but performance depended on the biological question, highlighting their utility for rare cellular state identification and regulatory analysis.

bioinformatics↗
bioRxiv2026

CryoMV: Structure-Prior-Guided Modeling and Real-Particle Validation of Continuous Conformational Transitions in Cryo-EM

Continuous protein conformations are essential for understanding fundamental biological processes and supporting drug discovery. Although cryo-EM can resolve individual states at high resolution, recovering continuous heterogeneity from 2D particle images remains challenging. High noise, motion blur, and limited structural priors make it difficult to accurately generate and validate high-resolution continuous conformations using raw particle data. Here, we introduce cryoMV, a framework that integrates structure-prior-guided modeling with real-particle validation for continuous conformational transitions. CryoMV uses reference density maps to establish structural anchors and motion priors, models candidate transition paths between selected conformations, and transfers the learned representation to raw 2D cryo-EM particle images. Each candidate conformation is subsequently evaluated using the estimated particle poses and contrast transfer functions. Supported conformations are reconstructed through raw particle back-projection and assessed using canonical half-maps and Fourier shell correlation. On EMPIAR-10516 and EMPIAR-10345, cryoMV achieves excellent performance in terms of robustness, verifiability, and reconstruction resolution. By incorporating structure-prior modeling and evidence from the raw particles, cryoMV offers an explicit mechanism for assessing whether generated conformations are supported by experimental data and provides a practical approach to reducing model-induced artifacts in continuous cryo-EM heterogeneity analysis.

bioinformatics↗