Networks link antigenic and receptor-binding sites of influenza hemagglutinin: mechanistic insight into fitter strain propagation

Enhancing antibody affinity through experimental sampling of non-deleterious CDR mutations predicted by machine learning

The application of machine learning (ML) models to optimize antibody affinity to an antigen is gaining prominence. Unfortunately, the small and biased nature of the publicly available antibody-antigen interaction datasets makes it challenging to build an ML model that can accurately predict binding affinity changes due to mutations (ΔΔG). Recognizing these inherent limitations, we reformulated the problem to ask whether an ML model capable of classifying deleterious vs non-deleterious mutations can guide antibody affinity maturation in a practical setting. To test this hypothesis, we developed a Random Forest classifier (Antibody Random Forest Classifier or AbRFC) with expert-guided features and integrated it into a computational-experimental workflow. AbRFC effectively predicted non-deleterious mutations on an in-house validation dataset that is free of biases seen in the publicly available training datasets. Furthermore, experimental screening of a limited number of predictions from the model (<10^2 designs) identified affinity-enhancing mutations in two unrelated SARS-CoV-2 antibodies, resulting in constructs with up to 1000-fold increased binding to the SARS-COV-2 RBD. Our findings indicate that accurate prediction and screening of non-deleterious mutations using machine learning offers a powerful approach to improving antibody affinity.

Comparison of genome replication fidelity between SARS-CoV-2 and influenza A virus in cell culture

Since the emergence of COVID-19, several SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) variants have emerged and spread widely. These variants are produced through replication errors of the viral genome by viral RNA-dependent RNA polymerase (RdRp). Seasonal epidemics of influenza are also known to occur because of new variants of influenza A virus (IAV), which are generated by the introduction of mutations by viral RdRp with low fidelity. Variants with different antigenicities appear because of mutations in envelope glycoproteins. In this study, we calculated and compared the mutation rates in genome replication of IAV and SARS-CoV-2. Average mutation rates per passage were 9.01 × 10-5 and 3.76 × 10-6 substitutions/site for IAV and SARS-CoV-2, respectively. The mutation rate of SARS-CoV-2 was 23.9-fold lower than that of IAV because of the proofreading activity of the SARS-CoV-2 RdRp complex. Our data could be useful in establishing effective countermeasures against COVID-19.

Generation of bispecific antibodies by structure-guided redesign of IgG constant regions

Bispecific antibodies (BsAbs) form an exciting class of bio-therapeutics owing to their multispecificity. Although numerous formats have been developed, generation of hetero-tetrameric IgG1-like BsAbs having acceptable safety and pharmacokinetics profiles from a single cell culture system remains challenging due to the heterogeneous pairing between the four chains. Herein, we employed a structure-guided approach to engineer mutations in the constant domain interfaces (C_H1-C_L and C_H3-C_H3) of heavy and κ light chains to prevent heavy-light mispairing in the antigen binding fragment (Fab) region and heavy-heavy homodimerization in the Fc region. Transient co-transfection of mammalian cells with heavy and light chains of pre-existing antibodies carrying the engineered constant domains generates BsAbs with percentage purity ranging from 78% to 85%. The engineered BsAbs demonstrate simultaneous binding of both antigens, while retaining the thermal stability, Fc-mediated effector properties and FcRn binding properties of the parental antibodies. Importantly, since the variable domains were not modified, the mutations may enable BsAb formation from antibodies belonging to different germline origins and isotypes. The rationally designed mutations reported in this work could serve as a starting point for generating optimized solutions required for large scale production.

Learned features of antibody-antigen binding affinity

Defining predictors of antigen-binding affinity of antibodies is valuable for engineering therapeutic antibodies with high binding affinity to their targets. However, this task is challenging owing to the huge diversity in the conformations of the complementarity determining regions of antibodies and the mode of engagement between antibody and antigen. In this study, we used the structural antibody database (SAbDab) to identify features that can discriminate high- and low-binding affinity across a 5-log scale. First, we abstracted features based on previously learned representations of protein-protein interactions to derive 'complex' feature sets, which include energetic, statistical, network-based, and machine-learned features. Second, we contrasted these complex feature sets with additional 'simple' feature sets based on counts of contacts between antibody and antigen. By investigating the predictive potential of 700 features contained in the eight complex and simple feature sets, we observed that simple feature sets perform comparably to complex feature sets in classification of binding affinity. Moreover, combining features from all eight feature-sets provided the best classification performance (median cross-validation AUROC and F1-score of 0.72). Of note, classification performance is substantially improved when several sources of data leakage (e.g., homologous antibodies) are not removed from the dataset, emphasizing a potential pitfall in this task. We additionally observe a classification performance plateau across diverse featurization approaches, highlighting the need for additional affinity-labeled antibody-antigen structural data. The findings from our present study set the stage for future studies aimed at multiple-log enhancement of antibody affinity through feature-guided engineering.

Optimizing Antibody Affinity and Developability Using a Framework-CDR Shuffling Approach-Application to an Anti-SARS-CoV-2 Antibody

The computational methods used for engineering antibodies for clinical development have undergone a transformation from three-dimensional structure-guided approaches to artificial-intelligence- and machine-learning-based approaches that leverage the large sequence data space of hundreds of millions of antibodies generated by next-generation sequencing (NGS) studies. Building on the wealth of available sequence data, we implemented a computational shuffling approach to antibody components, using the complementarity-determining region (CDR) and the framework region (FWR) to optimize an antibody for improved affinity and developability. This approach uses a set of rules to suitably combine the CDRs and FWRs derived from naturally occurring antibody sequences to engineer an antibody with high affinity and specificity. To illustrate this approach, we selected a representative SARS-CoV-2-neutralizing antibody, H4, which was identified and isolated previously based on the predominant germlines that were employed in a human host to target the SARS-CoV-2-human ACE2 receptor interaction. Compared to screening vast CDR libraries for affinity enhancements, our approach identified fewer than 100 antibody framework-CDR combinations, from which we screened and selected an antibody (CB79) that showed a reduced dissociation rate and improved affinity against the SARS-CoV-2 spike protein (7-fold) when compared to H4. The improved affinity also translated into improved neutralization (>75-fold improvement) of SARS-CoV-2. Our rapid and robust approach for optimizing antibodies from parts without the need for tedious structure-guided CDR optimization will have broad utility for biotechnological applications.

Antibacterial and Antiviral Properties of Tetrahydrocurcumin-Based Formulations: An Overview of Their Metabolism in Different Microbiotic Compartments

In this review, the basic metabolic characteristics of the curcuminoid tetrahydrocurcumin (THC) at the level of the intestinal microbiota were addressed. Special attention was given to the bactericidal effects of one of the THC-phospholipid formulations, which has shown greater bioavailability and activity than pure THC. Similarly, quinoline derivatives and amino acid conjugates of THC have also shown antibacterial effects in the gut. The microbial effect of pure THC is particularly pronounced in pathophysiological conditions related to the function of the intestinal microbiota, such as type II diabetes. Furthermore, the antiviral characteristics of Cur compared to those of THC are more pronounced in preventing the influenza virus. In the case of HIV infections, the new microemulsion gel formulations of THC possess high retention during preventive application in the vagina and, at the same time, do not disturb the vaginal microbiota, which is critical in maintaining low vaginal pH. Based on the reviewed literature, finding new formulations of THC which can increase its bioavailability and activity and emphasize its antibacterial and antiviral characteristics could be very important. Applying such THC formulations in preventing and treating ailments related to the microbiotic compartments in the body would be beneficial from a medical point of view.

Complexity of Viral Epitope Surfaces as Evasive Targets for Vaccines and Therapeutic Antibodies

The dynamic interplay between virus and host plays out across many interacting surfaces as virus and host evolve continually in response to one another. In particular, epitope-paratope interactions (EPIs) between viral antigen and host antibodies drive much of this evolutionary race. In this review, we describe a series of recent studies examining aspects of epitope complexity that go beyond two interacting protein surfaces as EPIs are typically understood. To structure our discussion, we present a framework for understanding epitope complexity as a spectrum along a series of axes, focusing primarily on 1) epitope biochemical complexity (e.g., epitopes involving N-glycans) and 2) antigen conformational/dynamic complexity (e.g., epitopes with differential properties depending on antigen state or fold-axis). We highlight additional epitope complexity factors including epitope tertiary/quaternary structure, which contribute to epistatic relationships between epitope residues within- or adjacent-to a given epitope, as well as epitope overlap resulting from polyclonal antibody responses, which is relevant when assessing antigenic pressure against a given epitope. Finally, we discuss how these different forms of epitope complexity can limit EPI analyses and therapeutic antibody development, as well as recent efforts to overcome these limitations.

Insights on the mutational landscape of the SARS-CoV-2 Omicron variant receptor-binding domain

The Omicron variant features enhanced transmissibility and antibody escape. Here, we describe the Omicron receptor-binding domain (RBD) mutational landscape using amino acid interaction (AAI) networks, which are well suited for interrogating constellations of mutations that function in an epistatic manner. Using AAI, we map Omicron mutations directly and indirectly driving increased escape breadth and depth in class 1-4 antibody epitopes. Further, we present epitope networks for authorized therapeutic antibodies and assess perturbations to each antibody's epitope. Since our initial modeling following the identification of Omicron, these predictions have been realized by experimental findings of Omicron neutralization escape from therapeutic antibodies ADG20, AZD8895, and AZD1061. Importantly, the AAI predicted escape resulting from indirect epitope perturbations was not captured by previous sequence or point mutation analyses. Finally, for several Omicron RBD mutations, we find evidence for a plausible role in enhanced transmissibility via disruption of RBD-down conformational stability at the RBDdown-RBDdown interface.

Insights on the mutational landscape of the SARS-CoV-2 Omicron variant

The SARS-COV2 Omicron variant has sparked global concern due to the possibility of enhanced transmissibility and escape from vaccines and therapeutics. In this study, we describe the mutational landscape of the Omicron variant using amino acid interaction (AAI) networks. AAI network analysis is particularly well suited for interrogating the impact of constellations of mutations as occur on Omicron that may function in an epistatic manner. Our analyses suggest that as compared to previous variants of concern, the Omicron variant has increased antibody escape breadth due to mutations in class 3 and 4 antibody epitopes as well as increased escape depth due to accumulated mutations in class 1 antibody epitopes. We note certain RBD mutations that might further enhance Omicron's escape, and in particular advise careful surveillance of two subclades bearing R346S/K mutations with relevance for certain therapeutic antibodies. Further, AAI network analysis suggests that the function of certain therapeutic monoclonal antibodies may be disrupted by Omicron mutations as a result of the cumulative indirect perturbations to the epitope surface properties, despite point-mutation analyses suggesting these antibodies are tolerant of the set of Omicron mutations in isolation. Finally, for several Omicron mutations that do not appear to contribute meaningfully to antibody escape, we find evidence for a plausible role in enhanced transmissibility via disruption of RBD-down conformational stability at the RBD-RBD interface.

An Antigenic Space Framework for Understanding Antibody Escape of SARS-CoV-2 Variants

The evolution of mutations in SARS-CoV-2 at antigenic sites that impact neutralizing antibody responses in humans poses a risk to immunity developed through vaccination and natural infection. The highly successful RNA-based vaccines have enabled rapid vaccine updates that incorporate mutations from current variants of concern (VOCs). It is therefore important to anticipate future antigenic mutations as the virus navigates the heterogeneous global landscape of host immunity. Toward this goal, we survey epitope-paratope interfaces of anti-SARS-CoV-2 antibodies to map an antigenic space that captures the role of each spike protein residue within the polyclonal antibody response directed against the ACE2-receptor binding domain (RBD) or the N-terminal domain (NTD). In particular, the antigenic space map builds on recently published epitope definitions by annotating epitope overlap and orthogonality at the residue level. We employ the antigenic space map as a framework to understand how mutations on nine major variants contribute to each variant's evasion of neutralizing antibodies. Further, we identify constellations of mutations that span the orthogonal epitope regions of the RBD and NTD on the variants with the greatest antibody escape. Finally, we apply the antigenic space map to predict which regions of antigenic space-should they mutate-may be most likely to complementarily augment antibody evasion for the most evasive and transmissible VOCs.

An antibody engineering platform using amino acid networks: A case study in development of antiviral therapeutics

We present here a case study of an antibody-engineering platform that selects, modifies, and assembles antibody parts to construct novel antibodies. A salient feature of this platform includes the role of amino acid networks in optimizing framework regions (FRs) and complementarity determining regions (CDRs) to engineer new antibodies with desired structure-function relationships. The details of this approach are described in the context of its utility in engineering ZAb_FLEP, a potent anti-Zika virus antibody. ZAb_FLEP comprises of distinct parts, including heavy chain and light chain FRs and CDRs, with engineered features such as loop lengths and optimal epitope-paratope contacts. We demonstrate, with different test antibodies derived from different FR-CDR combinations, that despite these test antibodies sharing high overall sequence similarity, they yield diverse functional readouts. Furthermore, we show that strategies relying on one dimensional sequence similarity-based analyses of antibodies miss important structural nuances of the FR-CDR relationship, which is effectively addressed by the amino acid networks approach of this platform.

Mapping Potential Antigenic Drift Sites (PADS) on SARS-CoV-2 Spike in Continuous Epitope-Paratope Space

SARS-CoV-2 mutations with antigenic effects pose a risk to immunity developed through vaccination and natural infection. While vaccine updates for current variants of concern (VOCs) are underway, it is likewise important to prepare for further antigenic mutations as the virus navigates the heterogeneous global landscape of host immunity. Toward this end, a wealth of data and tools exist that can augment existing genetic surveillance of VOC evolution. In this study, we integrate published datasets describing genetic, structural, and functional constraints on mutation along with computational analyses of antibody-spike co-crystal structures to identify a set of potential antigenic drift sites (PADS) within the receptor binding domain (RBD) and N-terminal domain (NTD) of SARS-CoV-2 spike protein. Further, we project the PADS set into a continuous epitope-paratope space to facilitate interpretation of the degree to which newly observed mutations might be antigenically synergistic with existing VOC mutations, and this representation suggests that functionally convergent and synergistic antigenic mutations are accruing across VOC NTDs. The PADS set and synergy visualization serve as a reference as new mutations are detected on VOCs, enable proactive investigation of potentially synergistic mutations, and offer guidance to antibody and vaccine design efforts.

Antivirals Targeting the Surface Glycoproteins of Influenza Virus: Mechanisms of Action and Resistance

Hemagglutinin and neuraminidase, which constitute the glycoprotein spikes expressed on the surface of influenza A and B viruses, are the most exposed parts of the virus and play critical roles in the viral lifecycle. As such, they make prominent targets for the immune response and antiviral drugs. Neuraminidase inhibitors, particularly oseltamivir, constitute the most commonly used antivirals against influenza viruses, and they have proved their clinical utility against seasonal and emerging influenza viruses. However, the emergence of resistant strains remains a constant threat and consideration. Antivirals targeting the hemagglutinin protein are relatively new and have yet to gain global use but are proving to be effective additions to the antiviral repertoire, with a relatively high threshold for the emergence of resistance. Here we review antiviral drugs, both approved for clinical use and under investigation, that target the influenza virus hemagglutinin and neuraminidase proteins, focusing on their mechanisms of action and the emergence of resistance to them.

Emerging and state of the art hemagglutinin-targeted influenza virus inhibitors

Introduction: Seasonal influenza vaccination, together with FDA-approved neuraminidase (NA) and polymerase acidic (PA) inhibitors, is the most effective way for prophylaxis and treatment of influenza infections. However, the low efficacy of prevailing vaccines to newly emerging influenza strains and increasing resistance to available drugs drives intense research to explore more effective inhibitors. Hemagglutinin (HA), one of the major surface proteins of influenza strains, represents an attractive therapeutic target to develop such new inhibitors.Areas covered: This review summarizes the current progress of HA-based influenza virus inhibitors and their mechanisms of action, which may facilitate further research in developing novel antiviral inhibitors for controlling influenza infections.Expert opinion: HA-mediated entry of influenza virus is an essential step for successful infection of the host, which makes HA a promising target for the development of antiviral drugs. Recent progress in delineating the crystal structures of HA, especially HA-inhibitors complexes, has revealed a number of key residues and conserved binding pockets within HA. This has opened up important insights for developing HA-based antiviral inhibitors that have a high resistance barrier and broad-spectrum activities.

Prediction of the binding interface between monoclonal antibody m102.4 and Nipah attachment glycoprotein using structure-guided alanine scanning and computational docking

Nipah Virus (NiV) has been designated as a priority disease with an urgent need for therapeutic development by World Health Organization. The monoclonal antibody m102.4 binds to the immunodominant NiV receptor-binding glycoprotein (GP), and potently neutralizes NiV, indicating its potential as a therapeutic agent. Although the co-crystal structure of m102.3, an m102.4 derivative, in complex with the GP of the related Hendra Virus (HeV) has been solved, the structural interaction between m102.4 and NiV is uncharacterized. Herein, we used structure-guided alanine-scanning mutagenesis to map the functional epitope and paratope residues that govern the antigen-antibody interaction. Our results revealed that the binding of m102.4 is mediated predominantly by two residues in the HCDR3 region, which is unusually small for an antibody-antigen interaction. We performed computational docking to generate a structural model of m102.4-NiV interaction. Our model indicates that m102.4 targets the common hydrophobic central cavity and a hydrophilic rim on the GP, as observed for the m102.3-HeV co-crystal, albeit with Fv orientation differences. In summary, our study provides insight into the m102.4-NiV interaction, demonstrating that structure-guided alanine-scanning and computational modeling can serve as the starting point for additional antibody reengineering (e.g. affinity maturation) to generate potential therapeutic candidates.

Clinical and virological responses to a broad-spectrum human monoclonal antibody in an influenza virus challenge study

Influenza A infections cause significant seasonal morbidity and mortality as well as periodic pandemic infections. Currently, no approved therapies exist for patients hospitalized with influenza. The efficacy of VIS410, a broadly neutralizing human immunoglobulin IgG1 monoclonal antibody engineered to bind to the stem region of group 1 and 2 influenza A hemagglutinins, was explored in experimental human influenza infection. Healthy volunteers were inoculated with influenza A/California/07/2009 (H1N1) and received a single dose of VIS410 or placebo 24 h later. Subjects were monitored for symptoms, viral shedding, and safety, including cytokine measurements. The primary efficacy endpoint was the area under the curve (AUC) of viral load (VL) in the VIS410 group versus placebo. VIS410 treatment was associated with a 76% reduction in median VL AUC as measured by qRT-PCR (p = 0.024). Similar VIS410 antiviral activity was observed by virus culture, with a 91% reduction in median VL AUC by TCID₅₀ (p = 0.019) compared to placebo-treated volunteers. Influenza symptoms were generally mild or moderate, with a trend toward faster resolution in VIS410-treated subjects. Treatment with VIS410 was generally safe, with an increase in gastrointestinal events that were largely mitigated by pre-treatment with oral diphenhydramine (50 mg) in combination with 600 mg of ibuprofen. Transient elevation of specific cytokines (IL-8 and TNFα) were associated with gastrointestinal adverse events. Treatment with VIS410 did not interfere with the endogenous immune response to influenza A. These data indicate that VIS410 may provide therapeutic benefit in influenza A infection. TRIAL REGISTRATION: ClinicaTtrials.gov Identification NCT02468115; https://clinicaltrials.gov/ct2/show/NCT02468115?term=NCT02468115&rank=1).

Allele-specific nonstationarity in evolution of influenza A virus surface proteins

Influenza A virus (IAV) is a major public health problem and a pandemic threat. Its evolution is largely driven by diversifying positive selection so that relative fitness of different amino acid variants changes with time due to changes in herd immunity or genomic context, and novel amino acid variants attain fitness advantage. Here, we hypothesize that diversifying selection also has another manifestation: the fitness associated with a particular amino acid variant should decline with time since its origin, as the herd immunity adapts to it. By tracing the evolution of antigenic sites at IAV surface proteins, we show that an amino acid variant becomes progressively more likely to become replaced by another variant with time since its origin-a phenomenon we call "senescence." Senescence is particularly pronounced at experimentally validated antigenic sites, implying that it is largely driven by host immunity. By contrast, at internal sites, existing variants become more favorable with time, probably due to arising contingent mutations at other epistatically interacting sites. Our findings reveal a previously undescribed facet of adaptive evolution and suggest approaches for prediction of evolutionary dynamics of pathogens.

Molecular basis for dengue virus broad cross-neutralization by humanized monoclonal antibody 513

Dengue is a widespread viral disease with 3.6 billion people at risk worldwide. Humanized monoclonal antibody (mAb) 513, currently undergoing clinical trials in Singapore, targets an epitope on the envelope protein domain III exposed at the surface of the viral particle. This antibody potently neutralizes all four dengue virus serotypes in a humanized mouse model that recapitulates human dengue infection, without signs of antibody-mediated enhancement of the disease. The crystal structure of single-chain variable fragment (scFv) 513 bound to the envelope protein domain III from dengue virus serotype 4 was used as a template to explore the molecular origins of the broader cross-reactivity and increased in vivo potency of mAb 513, compared to the parent murine mAb 4E11, using molecular dynamics simulations and network analyses. These two methods are a powerful complement to existing structural and binding data and detail specific interactions that underpin the differential binding of the two antibodies. We found that a Glu at position H55 (Glu^H55) from the second Complementarity Determining Region of the Heavy chain (CDR-H2) which corresponds to Ala in 4E11, is a major contributor to the enhancement in the interactions of mAb 513 compared to 4E11. Importantly, we also validate the importance of Glu^H55 using site-directed mutagenesis followed by isothermal titration calorimetry measurements.

Generation and testing anti-influenza human monoclonal antibodies in a new humanized mouse model (DRAGA: HLA-A2. HLA-DR4. Rag1 KO. IL-2Rγc KO. NOD)

Pandemic outbreaks of influenza type A viruses have resulted in numerous fatalities around the globe. Since the conventional influenza vaccines (CIV) provide less than 20% protection for individuals with weak immune system, it has been considered that broadly cross-neutralizing antibodies may provide a better protection. Herein, we showed that a recently generated humanized mouse (DRAGA mouse; HLA-A2. HLA-DR4. Rag1KO. IL-2Rgc KO. NOD) that lacks the murine immune system and expresses a functional human immune system can be used to generate cross-reactive, human anti-influenza monoclonal antibodies (hu-mAb). DRAGA mouse was also found to be suitable for influenza virus infection, as it can clear a sub-lethal infection and sustain a lethal infection with PR8/A/34 influenza virus. The hu-mAbs were designed for targeting a human B-cell epitope (¹⁸⁰WGIHHPPNSKEQ QNLY¹⁹⁵) of hemagglutinin (HA) envelope protein of PR8/A/34 (H1N1) virus with high homology among seven influenza type A viruses. A single administration of HA_180-195 specific hu-mAb in PR8-infected DRAGA mice significantly delayed the lethality by reducing the lung damage. The results demonstrated that DRAGA mouse is a suitable tool to (i) generate heterotype cross-reactive, anti-influenza human monoclonal antibodies, (ii) serve as a humanized mouse model for influenza infection, and (iii) assess the efficacy of anti-influenza antibody-based therapeutics for human use.

Structural comparison among the 2013-2017 avian influenza A H5N6 hemagglutinin proteins: A computational study with epidemiological implications

Avian influenza viruses easily spread allowing viral re-assortment to simply occur which in-turn increases the potential for a pandemic. A novel 2013 H5N6 influenza strain was detected among the avian population and was reported to continuously evolve, however, this was never structurally demonstrated. Here, we elucidated the putative structural evolution of the novel H5N6 influenza strain. Throughout this study, we analyzed 2013-2017 H5N6 HA protein models. Model quality was first verified before further analyses and structural comparison was made using superimposition. We found that Leu was inserted at position 129₁ among the 2013-2015 models while Leu was not inserted among the 2016-2017 models. Moreover, presence of Leu at position 129₁ shifts residue E126₁ by 159.6° affecting nearby residues which may explain the difference between the 2013-2015 and 2016-2017 HA structural groups. Similarly, we believe that our results would support the hypothesis that the current H5N6 strain is still continuously evolving.

An inter-residue network model to identify mutational-constrained regions on the Ebola coat glycoprotein

Recently, progress has been made in the development of vaccines and monoclonal antibody cocktails that target the Ebola coat glycoprotein (GP). Based on the mutation rates for Ebola virus given its natural sequence evolution, these treatment strategies are likely to impose additional selection pressure to drive acquisition of mutations in GP that escape neutralization. Given the high degree of sequence conservation among GP of Ebola viruses, it would be challenging to determine the propensity of acquiring mutations in response to vaccine or treatment with one or a cocktail of monoclonal antibodies. In this study, we analyzed the mutability of each residue using an approach that captures the structural constraints on mutability based on the extent of its inter-residue interaction network within the three-dimensional structure of the trimeric GP. This analysis showed two distinct clusters of highly networked residues along the GP₁-GP₂ interface, part of which overlapped with epitope surfaces of known neutralizing antibodies. This network approach also permitted us to identify additional residues in the network of the known hotspot residues of different anti-Ebola antibodies that would impact antibody-epitope interactions.

Viral factors in influenza pandemic risk assessment

The threat of an influenza A virus pandemic stems from continual virus spillovers from reservoir species, a tiny fraction of which spark sustained transmission in humans. To date, no pandemic emergence of a new influenza strain has been preceded by detection of a closely related precursor in an animal or human. Nonetheless, influenza surveillance efforts are expanding, prompting a need for tools to assess the pandemic risk posed by a detected virus. The goal would be to use genetic sequence and/or biological assays of viral traits to identify those non-human influenza viruses with the greatest risk of evolving into pandemic threats, and/or to understand drivers of such evolution, to prioritize pandemic prevention or response measures. We describe such efforts, identify progress and ongoing challenges, and discuss three specific traits of influenza viruses (hemagglutinin receptor binding specificity, hemagglutinin pH of activation, and polymerase complex efficiency) that contribute to pandemic risk.

Glycan-protein interactions in viral pathogenesis

The surfaces of host cells and viruses are decorated by complex glycans, which play multifaceted roles in the dynamic interplay between the virus and the host including viral entry into host cell, modulation of proteolytic cleavage of viral proteins, recognition and neutralization of virus by host immune system. These roles are mediated by specific multivalent interactions of glycans with their cognate proteins (generally termed as glycan-binding proteins or GBPs or lectins). The advances in tools and technologies to chemically synthesize and structurally characterize glycans and glycan-GBP interactions have offered several insights into the role of glycan-GBP interactions in viral pathogenesis and have presented opportunities to target these interactions for novel antiviral therapeutic or vaccine strategies. This review covers aspects of role of host cell surface glycan receptors and viral surface glycans in viral pathogenesis and offers perspectives on how to employ various analytical tools to target glycan-GBP interactions.

Virological surveillance of influenza and other respiratory viruses during six consecutive seasons from 2006 to 2012 in Catalonia, Spain

Most attention is given to seasonal influenza and respiratory syncytial virus outbreaks, but the cumulative burden caused by other respiratory viruses (RV) is not widely considered. The aim of the present study is to describe the circulation of RV in the general population during six consecutive seasons from 2006 to 2012 in Catalonia, Spain. Cell culture, immunofluorescence and PCR-based assays were used for the RV laboratory-confirmation and influenza subtyping. Phylogenetic and molecular characterizations of viral haemagglutinin, partial neuraminidase and matrix 2 proteins were performed from a representative sampling of influenza viruses. A total of 6315 nasopharyngeal samples were collected, of which 64% were laboratory-confirmed, mainly as influenza A viruses and rhinoviruses. Results show the significant burden of viral aetiological agents in acute respiratory infection, particularly in the youngest cases. The study of influenza strains reveals their continuous evolution through either progressive mutations or by segment reassortments. Moreover, the predominant influenza B lineage was different from that included in the recommended vaccine in half of the studied seasons, supporting the formulation and use of a quadrivalent influenza vaccine. Regarding neuraminidase inhibitors resistance, with the exception of the 2007/08 H275Y seasonal A(H1N1) strains, no other circulating influenza strains carrying known resistance genetic markers were found. Moreover, all circulating A(H1N1)pdm09 and A(H3N2) strains finally became genetically resistant to adamantanes. A wide knowledge of the seasonality patterns of the RV in the general population is well-appreciated, but it is a challenge due to the unpredictable circulation of RV, highlighting the value of local and global RV surveillance.

A broadly neutralizing human monoclonal antibody is effective against H7N9

Emerging strains of influenza represent a significant public health threat with potential pandemic consequences. Of particular concern are the recently emerged H7N9 strains which cause pneumonia with acute respiratory distress syndrome. Estimates are that nearly 80% of hospitalized patients with H7N9 have received intensive care unit support. VIS410, a human antibody, targets a unique conserved epitope on influenza A. We evaluated the efficacy of VIS410 for neutralization of group 2 influenza strains, including H3N2 and H7N9 strains in vitro and in vivo. VIS410, administered at 50 mg/kg, protected DBA mice infected with A/Anhui/2013 (H7N9), resulting in significant survival benefit upon single-dose (-24 h) or double-dose (-12 h, +48 h) administration (P < 0.001). A single dose of VIS410 at 50 mg/kg (-12 h) combined with oseltamivir at 50 mg/kg (-12 h, twice daily for 7 d) in C57BL/6 mice infected with A/Shanghai 2/2013 (H7N9) resulted in significant decreased lung viral load (P = 0.002) and decreased lung cytokine responses for nine of the 11 cytokines measured. Based on these results, we find that VIS410 may be effective either as monotherapy or combined with antivirals in treating H7N9 disease, as well as disease from other influenza strains.

Structure-Guided Design of an Anti-dengue Antibody Directed to a Non-immunodominant Epitope

Dengue is the most common vector-borne viral disease, causing nearly 400 million infections yearly. Currently there are no approved therapies. Antibody epitopes that elicit weak humoral responses may not be accessible by conventional B cell panning methods. To demonstrate an alternative strategy to generating a therapeutic antibody, we employed a non-immunodominant, but functionally relevant, epitope in domain III of the E protein, and engineered by structure-guided methods an antibody directed to it. The resulting antibody, Ab513, exhibits high-affinity binding to, and broadly neutralizes, multiple genotypes within all four serotypes. To assess therapeutic relevance of Ab513, activity against important human clinical features of dengue was investigated. Ab513 mitigates thrombocytopenia in a humanized mouse model, resolves vascular leakage, reduces viremia to nearly undetectable levels, and protects mice in a maternal transfer model of lethal antibody-mediated enhancement. The results demonstrate that Ab513 may reduce the public health burden from dengue.

Antibody-Based Strategies to Prevent and Treat Influenza

Passive immunization using antibodies is a promising alternative to other antiviral treatment options. The potential for seasonal protection arising from a single injection of antibodies is appealing and has been pursued for a number of infectious agents. However, until recently, antibody-based strategies to combat infectious agents have been hampered due to the fact that most antibodies have been found to be strain specific, with the virus evolving resistance in many cases. The discovery of broadly neutralizing antibodies (bNAbs) in influenza, dengue virus, and HIV, which bind to multiple, structurally diverse strains, has provided renewed interest in this area. This review will focus on new technologies that enable the discovery of bNAbs, the challenges and opportunities of immunotherapies as an important addition to existing antiviral therapy, and the role of antibody discovery in informing rational vaccine discovery - with agents targeting influenza specifically addressed. Multiple candidates have entered the clinic and raise the possibility that a single antibody or small combination of antibodies can effectively neutralize a wide variety of strains. However, challenges remain - including combating escape variants, pharmacodynamics of antibody distribution, and development of efficacy biomarkers beyond virologic endpoints.

Selecting vaccine strains for H3N2 human influenza A virus

H3N2 human influenza A virus causes epidemics of influenza mainly in the winter season in temperate regions. Since the antigenicity of this virus evolves rapidly, several attempts have been made to predict the major amino acid sequence of hemagglutinin 1 (HA1) in the target season of vaccination. However, the usefulness of predicted sequence was unclear because its relationship to the antigenicity was unknown. Here the antigenic model for estimating the degree of antigenic difference (antigenic distance) between amino acid sequences of HA1 was integrated into the process of selecting vaccine strains for H3N2 human influenza A virus. When the effectiveness of a potential vaccine strain for a target season was evaluated retrospectively using the average antigenic distance between the strain and the epidemic viruses sampled in the target season, the most effective vaccine strain was identified mostly in the season one year before the target season (pre-target season). Effectiveness of actual vaccines appeared to be lower than that of the strains randomly chosen in the pre-target season on average. It was recommended to replace the vaccine strain for every target season with the strain having the smallest average antigenic distance to the others in the pre-target season. The procedure of selecting vaccine strains for future epidemic seasons described in the present study was implemented in the influenza virus forecasting system (INFLUCAST) (http://www.nsc.nagoya-cu.ac.jp/~yossuzuk/influcast.html).

Computational Identification of Antigenicity-Associated Sites in the Hemagglutinin Protein of A/H1N1 Seasonal Influenza Virus

The antigenic variability of influenza viruses has always made influenza vaccine development challenging. The punctuated nature of antigenic drift of influenza virus suggests that a relatively small number of genetic changes or combinations of genetic changes may drive changes in antigenic phenotype. The present study aimed to identify antigenicity-associated sites in the hemagglutinin protein of A/H1N1 seasonal influenza virus using computational approaches. Random Forest Regression (RFR) and Support Vector Regression based on Recursive Feature Elimination (SVR-RFE) were applied to H1N1 seasonal influenza viruses and used to analyze the associations between amino acid changes in the HA1 polypeptide and antigenic variation based on hemagglutination-inhibition (HI) assay data. Twenty-three and twenty antigenicity-associated sites were identified by RFR and SVR-RFE, respectively, by considering the joint effects of amino acid residues on antigenic drift. Our proposed approaches were further validated with the H3N2 dataset. The prediction models developed in this study can quantitatively predict antigenic differences with high prediction accuracy based only on HA1 sequences. Application of the study results can increase understanding of H1N1 seasonal influenza virus antigenic evolution and accelerate the selection of vaccine strains.

Global connectivity of hub residues in Oncoprotein structures encodes genetic factors dictating personalized drug response to targeted Cancer therapy

The efficacy and mechanisms of therapeutic action are largely described by atomic bonds and interactions local to drug binding sites. Here we introduce global connectivity analysis as a high-throughput computational assay of therapeutic action--inspired by the Google page rank algorithm that unearths most "globally connected" websites from the information-dense world wide web (WWW). We execute short timescale (30 ps) molecular dynamics simulations with high sampling frequency (0.01 ps), to identify amino acid residue hubs whose global connectivity dynamics are characteristic of the ligand or mutation associated with the target protein. We find that unexpected allosteric hubs--up to 20 Å from the ATP binding site, but within 5 Å of the phosphorylation site--encode the Gibbs free energy of inhibition (ΔG(inhibition)) for select protein kinase-targeted cancer therapeutics. We further find that clinically relevant somatic cancer mutations implicated in both drug resistance and personalized drug sensitivity can be predicted in a high-throughput fashion. Our results establish global connectivity analysis as a potent assay of protein functional modulation. This sets the stage for unearthing disease-causal exome mutations and motivates forecast of clinical drug response on a patient-by-patient basis. We suggest incorporation of structure-guided genetic inference assays into pharmaceutical and healthcare Oncology workflows.

Predictability of antigenic evolution for H3N2 human influenza A virus

Influenza A virus continues to pose a threat to public health. Since this virus can evolve escape mutants rapidly, it is desirable to predict the antigenic evolution for developing effective vaccines. Although empirical methods have been proposed and reported to predict the antigenic evolution more or less accurately, they did not provide much insight into the effects of unobserved mutations and the mechanisms of antigenic evolution. Here a theoretical method was introduced to predict the antigenic evolution of H3N2 human influenza A virus by evaluating de novo mutations through estimating the antigenic distance. The antigenic distance defined with the hemagglutination inhibition (HI) titer was estimated with antigenic models taking into account the volume, isoelectric point, relative solvent accessibility, and distances from receptor-binding sites (RBS) and N-linked glycosylation sites (NGS) for amino acids in hemagglutinin 1 (HA1). When the best model with the optimized parameter values was used to predict the antigenic evolution for the dominant strains, the prediction accuracy was relatively low. However, there appeared to be an overall tendency that the amino acid sites with larger potential net effect on antigenicity were more likely to evolve and the amino acid changes with larger potential effect were more likely to take place, suggesting that natural selection may operate to enhance the antigenic evolution of H3N2 human influenza A virus.

Broadly neutralizing influenza hemagglutinin stem-specific antibody CR8020 targets residues that are prone to escape due to host selection pressure

Broadly neutralizing antibodies (bNAb) that target a conserved region of a viral antigen hold significant therapeutic promise. CR8020 is a bNAb that targets the stem region of influenza A virus (IAV) hemagglutinin (HA). CR8020 is currently being evaluated for prophylactic use against group 2 IAVs in phase II studies. Structural and computational analyses reported here indicate that CR8020 targets HA residues that are prone to antigenic drift and host selection pressure. Critically, CR8020 escape mutation is seen in certain H7N9 viruses from recent outbreaks. Furthermore, the ability of the bNAb Fc region to effectively engage activating Fcγ receptors (FCγR) is essential for antibody efficacy. In this regard, our data indicate that the membrane could sterically hinder the formation of HA-CR8020-FcγRIIa/HA-IgG-FcγRIIIa ternary complexes. Altogether, our analyses suggest that epitope mutability and accessibility to immune complex assembly are important attributes to consider when evaluating bNAb candidates for clinical development.

Interim estimates of 2013/14 vaccine effectiveness against influenza A(H1N1)pdm09 from Canada s sentinel surveillance network, January 2014

The 2013/14 influenza season to date in Canada has been characterised by predominant (90%) A(H1N1)pdm09 activity. Vaccine effectiveness (VE) was assessed in January 2014 by Canada's sentinel surveillance network using a test-negative case-control design. Interim adjusted-VE against medically-attended laboratory-confirmed influenza A(H1N1)pdm09 infection was 74% (95% CI: 58-83). Relative to vaccine, A(H1N1)pdm09 viruses were antigenically similar and genetically well conserved, with most showing just three mutations across the 50 amino acids comprising antigenic sites of the haemagglutinin protein.

Structural differences between the avian and human H7N9 hemagglutinin proteins are attributable to modifications in salt bridge formation: a computational study with implications in viral evolution

Influenza A hemagglutinin (HA) is a homotrimeric glycoprotein composed of a fibrous globular stem supporting a globular head containing three sialic acid binding sites responsible for infection. The H7N9 strain has consistently infected an avian host, however, the novel 2013 strain is now capable of infecting a human host which would imply that the HA in both strains structurally differ. A better understanding of the structural differences between the avian and human H7N9 strains may shed light into viral evolution and transmissibility. In this study, we elucidated the structural differences between the avian and human H7N9 strains. Throughout the study, we generated HA homology models, verified the quality of each model, superimposed HA homology models to determine structural differences, and, likewise, elucidated the probable cause for these structural differences. We detected two different types of structural differences between the novel H7N9 human and representative avian strains, wherein, one type (Pattern-1) showed three non-overlapping regions while the other type (Pattern-2) showed only one non-overlapping region. In addition, we found that superimposed HA homology models exhibiting Pattern-1 contain three non-overlapping regions designated as: Region-1 (S157₁-A160₁); Region-3 (R262₁-S265₁); and Region-4 (S270₁-D281₁), whereas, superimposed HA homology models showing Pattern-2 only contain one non-overlapping region designated as Region-2 (S137₁-S145₁). We attributed the two patterns we observed to either the presence of salt bridges involving the E114₁ residue or absence of the R141₁:D77₁ salt bridge. Interestingly, comparison between the human H7N7 and H7N9 HA homology models showed high structural similarity. We propose that the putative absence of the R141₁:D77₁ salt bridge coupled with the putative presence of the E114₁:R262₁ and E114₁:K264₁ salt bridges found in the 2013 H7N9 HA homology model is associated to human-type receptor binding. This highlights the possible significance of HA salt bridge formation modifications in viral infectivity, immune escape, transmissibility and evolution.

Structure-activity relationship analysis of curcumin analogues on anti-influenza virus activity

Curcumin (Cur) is a commonly used colouring agent and spice in food. Previously, we reported that Cur inhibits type A influenza virus (IAV) infection by interfering with viral haemagglutination (HA) activity. To search for a stable Cur analogue with potent anti-IAV activity and to investigate the structure contributing to its anti-IAV activity, a comparative analysis of structural and functional analogues of Cur, such as tetrahydrocurcumin (THC) and petasiphenol (Pet), was performed. The result of time-of-drug addition tests indicated that these curcuminoids were able to inhibit IAV production in cell cultures. Noticeably, Pet and THC inhibit IAV to a lesser extent than Cur, which is in line with their effect on reducing plaque formation when IAV was treated with Cur analogues before infection. Unexpectedly, both THC and Pet did not harbour any HA inhibitory effect. It should be noted that the structure of Pet and THC differs from Cur with respect to the number of double bonds present in the central seven-carbon chain, and structure modelling of Cur analogues indicates that the conformations of THC and Pet are distinct from that of Cur. Moreover, simulation docking of Cur with the HA structure revealed that Cur binds to the region constituting sialic acid anchoring residues, supporting the results obtained by the inhibition of HA activity assay. Collectively, structure-activity relationship analyses indicate that the presence of the double bonds in the central seven-carbon chain enhanced the Cur -dependent anti-IAV activity and also that Cur might interfere with IAV entry by its interaction with the receptor binding region of viral HA protein.

Glycan receptor binding of the influenza A virus H7N9 hemagglutinin

The advent of H7N9 in early 2013 is of concern for a number of reasons, including its capability to infect humans, the lack of clarity in the etiology of infection, and because the human population does not have pre-existing immunity to the H7 subtype. Earlier sequence analyses of H7N9 hemagglutinin (HA) point to amino acid changes that predicted human receptor binding and impinge on the antigenic characteristics of the HA. Here, we report that the H7N9 HA shows limited binding to human receptors; however, should a single amino acid mutation occur, this would result in structural changes within the receptor binding site that allow for extensive binding to human receptors present in the upper respiratory tract. Furthermore, a subset of the H7N9 HA sequences demarcating coevolving amino acids appears to be in the antigenic regions of H7, which, in turn, could impact effectiveness of the current WHO-recommended prepandemic H7 vaccines.

Homology modeling study toward identifying structural properties in the HA2 B-loop that would influence the HA1 receptor-binding site

Influenza hemagglutinin (HA) consists of a fibrous globular stem (HA2) inserted into the viral membrane supporting a globular head (HA1). HA1 receptor-binding has been hypothesized to be structurally correlated to the HA2 B-loop, however, this was never fully understood. Here, we elucidated the structural relationship between the HA2 B-loop and the HA1 receptor-binding site (RBS). Throughout this study, we analyzed 2486 H1N1 HA homology models obtained from human, swine and avian strains during 1976-2012. Quality of all homology models were verified before further analyses. We established that amino acid residue 882 is putatively strain-conserved and differs in the human (K882), swine (H882) and avian (N882) strains. Moreover, we observed that the amino acid at residue 882 and, similarly, its orientation has the potential to influence the HA1 RBS diameter measurements which we hypothesize may consequentially affect influenza H1N1 viral infectivity, immune escape, transmissibility, and evolution.

Structural determinants for naturally evolving H5N1 hemagglutinin to switch its receptor specificity

Of the factors governing human-to-human transmission of the highly pathogenic avian-adapted H5N1 virus, the most critical is the acquisition of mutations on the viral hemagglutinin (HA) to "quantitatively switch" its binding from avian to human glycan receptors. Here, we describe a structural framework that outlines a necessary set of H5 HA receptor-binding site (RBS) features required for the H5 HA to quantitatively switch its preference to human receptors. We show here that the same RBS HA mutations that lead to aerosol transmission of A/Vietnam/1203/04 and A/Indonesia/5/05 viruses, when introduced in currently circulating H5N1, do not lead to a quantitative switch in receptor preference. We demonstrate that HAs from circulating clades require as few as a single base pair mutation to quantitatively switch their binding to human receptors. The mutations identified by this study can be used to monitor the emergence of strains having human-to-human transmission potential.

The antigenic evolution of influenza: drift or thrift?

It is commonly assumed that antibody responses against the influenza virus are polarized in the following manner: strong antibody responses are directed at highly variable antigenic epitopes, which consequently undergo 'antigenic drift', while weak antibody responses develop against conserved epitopes. As the highly variable epitopes are in a constant state of flux, current antibody-based vaccine strategies are focused on the conserved epitopes in the expectation that they will provide some level of clinical protection after appropriate boosting. Here, we use a theoretical model to suggest the existence of epitopes of low variability, which elicit a high degree of both clinical and transmission-blocking immunity. We show that several epidemiological features of influenza and its serological and molecular profiles are consistent with this model of 'antigenic thrift', and that identifying the protective epitopes of low variability predicted by this model could offer a more viable alternative to regularly update the influenza vaccine than exploiting responses to weakly immunogenic conserved regions.

Electrochemical lectin based biosensors as a label-free tool in glycomics

Glycans and other saccharide moieties attached to proteins and lipids, or present on the surface of a cell, are actively involved in numerous physiological or pathological processes. Their structural flexibility (that is based on the formation of various kinds of linkages between saccharides) is making glycans superb "identity cards". In fact, glycans can form more "words" or "codes" (i.e., unique sequences) from the same number of "letters" (building blocks) than DNA or proteins. Glycans are physicochemically similar and it is not a trivial task to identify their sequence, or - even more challenging - to link a given glycan to a particular physiological or pathological process. Lectins can recognise differences in glycan compositions even in their bound state and therefore are most useful tools in the task to decipher the "glycocode". Thus, lectin-based biosensors working in a label-free mode can effectively complement the current weaponry of analytical tools in glycomics. This review gives an introduction into the area of glycomics and then focuses on the design, analytical performance, and practical utility of lectin-based electrochemical label-free biosensors for the detection of isolated glycoproteins or intact cells.

Glycosylation at Asn91 of H1N1 haemagglutinin affects binding to glycan receptors

The glycoprotein HA (haemagglutinin) on the surface of influenza A virus plays a central role in recognition and binding to specific host cell-surface glycan receptors and in fusion of viral membrane to the host nuclear membrane during viral replication. Given the abundance of HA on the viral surface, this protein is also the primary target for host innate and adaptive immune responses. Although addition of glycosylation sites on HA are a part of viral evolution to evade the host immune responses, there are specific glycosylation sites that are conserved during most of the evolution of the virus. In the present study, it was demonstrated that one such conserved glycosylation site at Asn⁹¹ in H1N1 HA critically governs the glycan receptor-binding specificity and hence would potentially impinge on the host adaptation of the virus.