Search Results (85)

The rapid evolution of SARS-CoV-2 has underscored the need for a detailed understanding of antibody binding mechanisms to combat immune evasion by emerging variants. In this study, we investigated the interactions between Class I neutralizing antibodies—BD55-1205, BD-604, OMI-42, P5S-1H1, and P5S-2B10—and the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein using multiscale modeling, which combined molecular simulations with the ensemble-based mutational scanning of the binding interfaces and binding free energy computations. A central theme emerging from this work is that the unique binding strength and resilience to immune escape of the BD55-1205 antibody are determined by leveraging a broad epitope footprint and distributed hotspot architecture, additionally supported by backbone-mediated specific interactions, which are less sensitive to amino acid substitutions and together enable exceptional tolerance to mutational escape. In contrast, BD-604 and OMI-42 exhibit localized binding modes with strong dependence on side-chain interactions, rendering them particularly vulnerable to escape mutations at K417N, L455M, F456L and A475V. Similarly, P5S-1H1 and P5S-2B10 display intermediate behavior—effective in some contexts but increasingly susceptible to antigenic drift due to narrower epitope coverage and concentrated hotspots. Our computational predictions show strong agreement with experimental deep mutational scanning data, validating the accuracy of the models and reinforcing the value of binding hotspot mapping in predicting antibody vulnerability. This work highlights that neutralization breadth and durability are not solely dictated by epitope location, but also by how binding energy is distributed across the interface. The results provide atomistic insight into mechanisms driving resilience to immune escape for broadly neutralizing antibodies targeting the ACE2 binding interface—which stems from cumulative effects of structural diversity in binding contacts, redundancy in interaction patterns and reduced vulnerability to mutation-prone positions. Full article

Background/Objectives: For viral entry into host cells, the spike (S) protein of coronavirus (CoV) uses its S1 domain to bind to the host receptor and S2 domain to mediate the fusion between virion and cellular membranes. The S1 domain acquired multiple mutations as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) evolved to give rise to Variant of Concerns (VOCs) but the S2 domain has limited changes. In particular, the stem helix in S2 did not change significantly and it is fairly well-conserved across multiple beta-CoVs. In this study, we generated a murine mAb 7B2 binding to the stem helix of SARS-CoV-2. Methods: MAb 7B2 was isolated from immunized mouse and its neutralization activity was evaluated using microneutralization, plaque reduction and cell–cell fusion assays. Bio-layer interferometry was used to measure binding affinity and AlphaFold3 was used to model the antibody–antigen interface. Results: MAb 7B2 has lower virus neutralizing and membrane block activities when compared to a previously reported stem helix-binding human mAb S2P6. Alanine scanning and AlphaFold3 modeling reveals that residues K1149 and D1153 in S form a network of polar interactions with the heavy chain of 7B2. Conversely, S2P6 binding to S is not affected by alanine substitution at K1149 and D1153 as indicated by the high ipTM scores in the predicted S2P6-stem helix structure. Conclusions: Our detailed characterization of the mechanism of inhibition of 7B2 reveals its distinctive binding model from S2P6 and yields insights on multiple neutralizing and highly conserved epitopes in the S2 domain which could be key components for pan-CoV vaccine development. Full article

Antibody lead discovery, crucial for immunotherapy development, requires identifying candidates with potent binding affinities to target antigens. Recent advances in protein language models have opened promising avenues to tackle this challenge by predicting antibody–antigen interactions (AAIs). Despite their appeals, precisely detecting binding sites (i.e., paratopes and epitopes) within the complex landscape of long-sequence biomolecules remains challenging. Herein, we propose MambaAAI, a bio-inspired model built upon the Mamba architecture, designed to predict AAIs and identify binding sites through selective attention mechanisms. Technically, we employ ESM-2, a pre-trained protein language model to extract evolutionarily enriched representations from input antigen and antibody sequences, which are modeled as residue-level interaction matrixes. Subsequently, a dual-view Mamba encoder is devised to capture important binding patterns, by dynamically learning embeddings of interaction matrixes from both antibody and antigen perspectives. Finally, the learned embeddings are decoded using a multilayer perceptron to output interaction probabilities. MambaAAI provides a unique advantage, relative to prior techniques, in dynamically selecting bio-enhancing residue sites that contribute to AAI prediction. We evaluate MambaAAI on two large-scale antibody–antigen neutralization datasets, and in silico results demonstrate that our method marginally outperforms the state-of-the-art baselines in terms of prediction accuracy, while maintaining robust generalization to unseen antibodies and antigens. In further analysis of the selective attention mechanism, we found that MambaAAI successfully uncovers critical epitope and paratope regions in the SARS-CoV-2 antibody examples. It is believed that MambaAAI holds great potential to discover lead candidates targeting specific antigens at a lower burden. Full article

In this study, we conducted a comprehensive analysis of the interactions between the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein and four neutralizing antibodies—S309, S304, CYFN1006, and VIR-7229. Using integrative computational modeling that combined all-atom molecular dynamics (MD) simulations, mutational scanning, and MM-GBSA binding free energy calculations, we elucidated the structural, energetic, and dynamic determinants of antibody binding. Our findings reveal distinct dynamic binding mechanisms and evolutionary adaptation driving the broad neutralization effect of these antibodies. We show that S309 targets conserved residues near the ACE2 interface, leveraging synergistic van der Waals and electrostatic interactions, while S304 focuses on fewer but sensitive residues, making it more susceptible to escape mutations. The analysis of CYFN-1006.1 and CYFN-1006.2 antibody binding highlights broad epitope coverage with critical anchors at T345, K440, and T346, enhancing its efficacy against variants carrying the K356T mutation, which caused escape from S309 binding. Our analysis of broadly potent VIR-7229 antibody binding to XBB.1.5 and EG.5 Omicron variants emphasized a large and structurally complex epitope, demonstrating certain adaptability and compensatory effects to F456L and L455S mutations. Mutational profiling identified key residues crucial for antibody binding, including T345, P337, and R346 for S309 as well as T385 and K386 for S304, underscoring their roles as evolutionary “weak spots” that balance viral fitness and immune evasion. The results of the energetic analysis demonstrate a good agreement between the predicted binding hotspots, reveal distinct energetic mechanisms of binding, and highlight the importance of targeting conserved residues and diverse epitopes to counteract viral resistance. Full article

(1) Background: Human Leukocyte Antigen (HLA) genetics substantially affect viral infection outcomes. SARS-CoV-2 continues to evolve, potentially escaping HLA presentation and hindering immune control. However, studies on HLA alleles in diverse non-Western populations remain limited. Therefore, we aimed to investigate whether mutations in successive SARS-CoV-2 variants have led to viral escape from common HLA class I alleles in the Saudi Arabian population. (2) Methods: The binding affinities of spike protein epitopes for common Saudi HLA alleles (HLA-A02:01, HLA-C06:02, and HLA-B51:01) were predicted across major SARS-CoV-2 strains using NetMHCpan. One-way ANOVA, one-sample t-tests, and pairwise chi-square analyses were performed to assess the differences in binding affinities and epitope binding categories among strains. (3) Results: One-way ANOVA revealed significant differences in binding affinities among SARS-CoV-2 strains for HLA-A02:01 and HLA-C06:02, but not for HLA-B51:01. One-sample t-tests revealed significant differences in mean binding affinity scores compared to a theoretical mean of 0 for all strain–HLA allele combinations, except for HLA-B51:01. Pairwise chi-square analyses identified significant differences in the epitope binding category distribution between Alpha and Epsilon strains, as well as between Epsilon and Gamma strains for HLA-B51:01. (4) Conclusions: The evolution of SARS-CoV-2 has enabled its escape from common HLA alleles in Saudis. Tracking population-specific HLA binding profiles is crucial for the elucidation of associated evasion mechanisms and guiding the design of future vaccines against COVID-19. Full article

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants poses an ongoing threat to the efficacy of vaccines and therapeutic antibodies. Mutations predominantly affect the receptor-binding domain (RBD) of the spike protein, which mediates viral entry. The RBD is also a major target of monoclonal antibodies that were authorised for use during the pandemic. In this study, an in silico approach was used to investigate the mutational landscape of SARS-CoV-2 RBD variants, including currently circulating Omicron subvariants. A total of 40 single-point mutations were assessed for their potential effect on protein stability and dynamics. Destabilising effects were predicted for mutations such as L455S and F456L, while stabilising effects were predicted for mutations such as R346T. Conformational B-cell epitope predictions were subsequently performed for wild-type (WT) and variant RBDs. Mutations from SARS-CoV-2 variants were located within the predicted epitope residues and the epitope regions were found to correspond to the sites targeted by therapeutic antibodies. Furthermore, homology models of the RBD of SARS-CoV-2 variants were generated and were utilised for protein–antibody docking. The binding characteristics of 10 monoclonal antibodies against WT and 14 SARS-CoV-2 variants were evaluated. Through evaluating the binding affinities, interactions, and energy contributions of RBD residues, mutations that were contributing to viral evasion were identified. The findings from this study provide insight into the structural and molecular mechanisms underlying neutralising antibody evasion. Future antibody development could focus on broadly neutralising antibodies, engineering antibodies with enhanced binding affinity, and targeting spike protein regions beyond the RBD. Full article

The rapid evolution of SARS-CoV-2 has led to the emergence of variants with increased immune evasion capabilities, posing significant challenges to antibody-based therapeutics and vaccines. In this study, we conducted a comprehensive structural and energetic analysis of SARS-CoV-2 spike receptor-binding domain (RBD) complexes with neutralizing antibodies from four distinct groups (A–D), including group A LY-CoV016, group B AZD8895 and REGN10933, group C LY-CoV555, and group D antibodies AZD1061, REGN10987, and LY-CoV1404. Using coarse-grained simplified simulation models, rapid energy-based mutational scanning, and rigorous MM-GBSA binding free energy calculations, we elucidated the molecular mechanisms of antibody binding and escape mechanisms, identified key binding hotspots, and explored the evolutionary strategies employed by the virus to evade neutralization. The residue-based decomposition analysis revealed energetic mechanisms and thermodynamic factors underlying the effect of mutations on antibody binding. The results demonstrate excellent qualitative agreement between the predicted binding hotspots and the latest experiments on antibody escape. These findings provide valuable insights into the molecular determinants of antibody binding and viral escape, highlighting the importance of targeting conserved epitopes and leveraging combination therapies to mitigate the risk of immune evasion. Full article

A growing body of experimental and computational studies suggests that the cross-neutralization antibody activity against Omicron variants may be driven by the balance and tradeoff between multiple energetic factors and interaction contributions of the evolving escape hotspots involved in antigenic drift and convergent evolution. However, the dynamic and energetic details quantifying the balance and contribution of these factors, particularly the balancing nature of specific interactions formed by antibodies with epitope residues, remain largely uncharacterized. In this study, we performed molecular dynamics simulations, an ensemble-based deep mutational scanning of SARS-CoV-2 spike residues, and binding free energy computations for two distinct groups of broadly neutralizing antibodies: the E1 group (BD55-3152, BD55-3546, and BD5-5840) and the F3 group (BD55-3372, BD55-4637, and BD55-5514). Using these approaches, we examined the energetic determinants by which broadly potent antibodies can largely evade immune resistance. Our analysis revealed the emergence of a small number of immune escape positions for E1 group antibodies that correspond to the R346 and K444 positions in which the strong van der Waals and interactions act synchronously, leading to the large binding contribution. According to our results, the E1 and F3 groups of Abs effectively exploit binding hotspot clusters of hydrophobic sites that are critical for spike functions along with the selective complementary targeting of positively charged sites that are important for ACE2 binding. Together with targeting conserved epitopes, these groups of antibodies can lead expand the breadth and resilience of neutralization to the antigenic shifts associated with viral evolution. The results of this study and the energetic analysis demonstrate excellent qualitative agreement between the predicted binding hotspots and critical mutations with respect to the latest experiments on average antibody escape scores. We argue that the E1 and F3 groups of antibodies targeting binding epitopes may leverage strong hydrophobic interactions with the binding epitope hotspots that are critical for the spike stability and ACE2 binding, while escape mutations tend to emerge in sites associated with synergistically strong hydrophobic and electrostatic interactions. Full article

Background: The rapid advancement of the pandemic caused by SARS-CoV-2 and its variants reinforced the importance of developing easy-to-edit vaccines with fast production, such as multi-epitope DNA vaccines. The present study aimed to construct a synthetic antigen multi-epitope SARS-CoV-2 to produce a DNA vaccine. Methods: A database of previously predicted Spike and Nucleocapsid protein epitopes was created, and these epitopes were analyzed for immunogenicity, conservation, population coverage, and molecular docking. Results: A synthetic antigen with 15 epitopes considered immunogenic, conserved even in the face of variants and that were able to anchor themselves in the appropriate HLA site, together had more than 90% worldwide coverage. A multi-epitope construct was developed with the sequences of these peptides separated from each other by linkers, cloned into the pVAX1 vector. This construct was evaluated in vivo as a DNA vaccine and elicited T CD4+ and T CD8+ cell expansion in the blood and spleen. In hematological analyses, there was an increase in lymphocytes, monocytes, and neutrophils between the two doses. Furthermore, based on histopathological analysis, the vaccines did not cause any damage to the organs analyzed. Conclusions: The present study generated a multi-epitope synthetic vaccine antigen capable of generating antibody-mediated and cellular immune responses. Full article

The elicitation of broadly neutralizing antibodies (bnAbs) is a major goal of vaccine design for highly mutable pathogens, such as influenza, HIV, and coronavirus. Although many rational vaccine design strategies for eliciting bnAbs have been devised, their efficacies need to be evaluated in preclinical animal models and in clinical trials. To improve outcomes for such vaccines, it would be useful to develop methods that can predict vaccine efficacies against arbitrary pathogen variants. As a step in this direction, here, we describe a simple biologically motivated model of antibody reactivity elicited by nanoparticle-based vaccines using only antigen amino acid sequences, parametrized with a small sample of experimental antibody binding data from influenza or SARS-CoV-2 nanoparticle vaccinations. Results: The model is able to recapitulate the experimental data to within experimental uncertainty, is relatively insensitive to the choice of the parametrization/training set, and provides qualitative predictions about the antigenic epitopes exploited by the vaccine, which are testable by experiment. For the mosaic nanoparticle vaccines considered here, model results suggest indirectly that the sera obtained from vaccinated mice contain bnAbs, rather than simply different strain-specific Abs. Although the present model was motivated by nanoparticle vaccines, we also apply it to a mutlivalent mRNA flu vaccination study, and demonstrate good recapitulation of experimental results. This suggests that the model formalism is, in principle, sufficiently flexible to accommodate different vaccination strategies. Finally, we show how the model could be used to rank the efficacies of vaccines with different antigen compositions. Conclusions: Overall, this study suggests that simple models of vaccine efficacy parametrized with modest amounts of experimental data could be used to compare the effectiveness of designed vaccines. Full article

Objectives: SARS-CoV-2 caused the COVID-19 pandemic, which overwhelmed global healthcare systems. Over 776 million COVID-19 cases and more than 7 million deaths were reported by WHO in September 2024. COVID-19 vaccination is crucial for preventing infection and controlling the pandemic. Here, we describe the design and development of a next-generation multi-epitope vaccine for SARS-CoV-2, consisting of T cell epitopes. Methods: Immunoinformatic methods were used to derive models for the selection of MHC binders specific for the mouse strain used in this study among a set of human SARS-CoV-2 T cell epitopes identified in convalescent patients with COVID-19. The immunogenicity of the vaccine prototype was tested on humanized-ACE2 transgenic B6.Cg-Tg(K18-ACE2)2Prlmn/J mice by in vitro, in vivo, and ex vivo immunoassays. Results: Eleven binders (two from the Envelope (E) protein; two from the Membrane (M) protein; three from the Spike (S) protein; and four from the Nucleocapsid (N) protein) were synthesized and included in a multi-epitope vaccine prototype. The animals were immunized with a mix of predicted MHC-I, MHC-II, or MHC-I/MHC-II peptide epitopes in Complete Freund’s Adjuvant, and boosted with peptides in Incomplete Freund’s Adjuvant. Immunization with SARS-CoV-2 epitopes remodeled the lymphocyte profile. A weak humoral response and the significant production of IL-4 and IFN-γ from T cells were found after the vaccination of the animals. Conclusions: The multi-epitope vaccine prototype presented in this study demonstrates immunogenicity in mice and shows potential for human vaccine construction. Full article

After disruption in the influenza circulation due to the emergence of SARS-CoV-2, the intensity of seasonal outbreaks has returned to the pre-pandemic levels. This study aimed to evaluate the evolution and variability of whole-genome sequences of A(H1N1)pdm09, the predominant influenza virus in Sicily (Italy) during the season 2023–2024. The potential vaccine efficacy was calculated using the p_epitope model based on amino acid changes in the dominant epitope of hemagglutinin. The HA gene sequences showed several amino acid substitutions, some of which were within the major antigenic sites. The phylogenetic analysis showed that Sicilian strains grouped into two main genetic clades (6B.1A.5a.2a.1 and 6B.1A.5a.2a) and several subclades. Notably, about 40% of sequences partially drifted from the WHO-recommended vaccine strain A/Victoria/4897/2022 for the Northern Hemisphere. These sequences mostly belonged to the subclades C.1.8 and C.1.9 and harboured the amino acid mutations responsible for the modest predicted vaccine efficacy (E = 38.12% of 53%, p_epitope = 0) against these viruses. Amino acid substitutions in other gene segments were also found. Since influenza viruses are constantly evolving, genomic surveillance is crucial in monitoring their molecular evolution and the occurrence of genetic and antigenic changes, and, thus, their potential impact on vaccine efficacy. Full article

The human leukocyte antigen (HLA) system plays a pivotal role in the immune response to viral infections, mediating the presentation of viral peptides to T cells and influencing both the strength and specificity of the host immune response. Variations in HLA genotypes across individuals lead to differences in susceptibility to viral infection and severity of illness. This study uses observations from the early phase of the COVID-19 pandemic to explore how specific HLA class I molecules affect clinical responses to SARS-CoV-2 infection. By analyzing paired high-resolution HLA types and viral genomic sequences from 60 patients, we assess the relationship between predicted HLA class I peptide binding repertoires and infection severity as measured by the sequential organ failure assessment score. This approach leverages functional convergence across HLA-C alleles to identify relationships that may otherwise be inaccessible due to allelic diversity and limitations in sample size. Surprisingly, our findings show that severely symptomatic infection in this cohort is associated with disproportionately abundant binding of SARS-CoV-2 structural and non-structural protein epitopes by patient HLA-C molecules. In addition, the extent of overlap between a given patient’s predicted HLA-C and HLA-A peptide binding repertoires correlates with worse prognoses in this cohort. The findings highlight immunologic mechanisms linking HLA-C molecules with the human response to viral pathogens that warrant further investigation. Full article

The most recent wave of SARS-CoV-2 Omicron variants descending from BA.2 and BA.2.86 exhibited improved viral growth and fitness due to convergent evolution of functional hotspots. These hotspots operate in tandem to optimize both receptor binding for effective infection and immune evasion efficiency, thereby maintaining overall viral fitness. The lack of molecular details on structure, dynamics and binding energetics of the latest FLiRT and FLuQE variants with the ACE2 receptor and antibodies provides a considerable challenge that is explored in this study. We combined AlphaFold2-based atomistic predictions of structures and conformational ensembles of the SARS-CoV-2 spike complexes with the host receptor ACE2 for the most dominant Omicron variants JN.1, KP.1, KP.2 and KP.3 to examine the mechanisms underlying the role of convergent evolution hotspots in balancing ACE2 binding and antibody evasion. Using the ensemble-based mutational scanning of the spike protein residues and computations of binding affinities, we identified binding energy hotspots and characterized the molecular basis underlying epistatic couplings between convergent mutational hotspots. The results suggested the existence of epistatic interactions between convergent mutational sites at L455, F456, Q493 positions that protect and restore ACE2-binding affinity while conferring beneficial immune escape. To examine immune escape mechanisms, we performed structure-based mutational profiling of the spike protein binding with several classes of antibodies that displayed impaired neutralization against BA.2.86, JN.1, KP.2 and KP.3. The results confirmed the experimental data that JN.1, KP.2 and KP.3 harboring the L455S and F456L mutations can significantly impair the neutralizing activity of class 1 monoclonal antibodies, while the epistatic effects mediated by F456L can facilitate the subsequent convergence of Q493E changes to rescue ACE2 binding. Structural and energetic analysis provided a rationale to the experimental results showing that BD55-5840 and BD55-5514 antibodies that bind to different binding epitopes can retain neutralizing efficacy against all examined variants BA.2.86, JN.1, KP.2 and KP.3. The results support the notion that evolution of Omicron variants may favor emergence of lineages with beneficial combinations of mutations involving mediators of epistatic couplings that control balance of high ACE2 affinity and immune evasion. Full article

The SARS-CoV-2 pandemic resulted in a scale-up of viral genomic surveillance globally. However, the wet lab constraints (economic, infrastructural, and personnel) of translating novel virus variant sequence information to meaningful immunological and structural insights that are valuable for the development of broadly acting countermeasures (especially for emerging and re-emerging viruses) remain a challenge in many resource-limited settings. Here, we describe a workflow that couples wastewater surveillance, high-throughput sequencing, phylogenetics, immuno-informatics, and virus capsid structure modeling for the genotype-to-serotype characterization of uncultivated picornavirus sequences identified in wastewater. Specifically, we analyzed canine picornaviruses (CanPVs), which are uncultivated and yet-to-be-assigned members of the family Picornaviridae that cause systemic infections in canines. We analyzed 118 archived (stored at −20 °C) wastewater (WW) samples representing a population of ~700,000 persons in southwest USA between October 2019 to March 2020 and October 2020 to March 2021. Samples were pooled into 12 two-liter volumes by month, partitioned (into filter-trapped solids [FTSs] and filtrates) using 450 nm membrane filters, and subsequently concentrated to 2 mL (1000×) using 10,000 Da MW cutoff centrifugal filters. The 24 concentrates were subjected to RNA extraction, CanPV complete capsid single-contig RT-PCR, Illumina sequencing, phylogenetics, immuno-informatics, and structure prediction. We detected CanPVs in 58.3% (14/24) of the samples generated 13,824,046 trimmed Illumina reads and 27 CanPV contigs. Phylogenetic and pairwise identity analyses showed eight CanPV genotypes (intragenotype divergence <14%) belonging to four clusters, with intracluster divergence of <20%. Similarity analysis, immuno-informatics, and virus protomer and capsid structure prediction suggested that the four clusters were likely distinct serological types, with predicted cluster-distinguishing B-cell epitopes clustered in the northern and southern rims of the canyon surrounding the 5-fold axis of symmetry. Our approach allows forgenotype-to-serotype characterization of uncultivated picornavirus sequences by coupling phylogenetics, immuno-informatics, and virus capsid structure prediction. This consequently bypasses a major wet lab-associated bottleneck, thereby allowing resource-limited settings to leapfrog from wastewater-sourced genomic data to valuable immunological insights necessary for the development of prophylaxis and other mitigation measures. Full article