Influenza Virus with Increased pH of Hemagglutinin Activation Has Improved Replication in Cell Culture but at the Cost of Infectivity in Human Airway Epithelium.

Pandemic H1N1 (pH1N1) influenza virus emerged from swine in 2009 with an adequate capability to infect and transmit between people. In subsequent years, it has circulated as a seasonal virus and evolved further human-adapting mutations. Mutations in the hemagglutinin (HA) stalk that increase pH stability have been associated with human adaptation and airborne transmission of pH1N1 virus. Yet, our understanding of how pH stability impacts virus-host interactions is incomplete. Here, using recombinant viruses with point mutations that alter the pH stability of pH1N1 HA, we found distinct effects on virus phenotypes in different experimental models. Increased pH sensitivity enabled viruses to uncoat in endosomes more efficiently, manifesting as increased replication rate in typical continuous cell cultures under single-cycle conditions. A more acid-labile HA also conferred a small reduction in sensitivity to antiviral therapeutics that act at the pH-sensitive HA fusion step. Conversely, in primary human airway epithelium cultured at the air-liquid interface, increased pH sensitivity attenuated multicycle viral replication by compromising virus survival in the extracellular microenvironment. In a mouse model of influenza pathogenicity, there was an optimum HA activation pH, and viruses with either more- or less-pH-stable HA were less virulent. Opposing pressures inside and outside the host cell that determine pH stability may influence zoonotic potential. The distinct effects that changes in pH stability exert on viral phenotypes underscore the importance of using the most appropriate systems for assessing virus titer and fitness, which has implications for vaccine manufacture, antiviral drug development, and pandemic risk assessment.IMPORTANCE The pH stability of the hemagglutinin surface protein varies between different influenza strains and subtypes and can affect the virus' ability to replicate and transmit. Here, we demonstrate a delicate balance that the virus strikes within and without the target cell. We show that a pH-stable hemagglutinin enables a human influenza virus to replicate more effectively in human airway cells and mouse lungs by facilitating virus survival in the extracellular environment of the upper respiratory tract. Conversely, after entering target cells, being more pH stable confers a relative disadvantage, resulting in less efficient delivery of the viral genome to the host cell nucleus. Since the balance we describe will be affected differently in different host environments, it may restrict a virus' ability to cross species. In addition, our findings imply that different influenza viruses may show variation in how well they are controlled by antiviral strategies targeting pH-dependent steps in the virus replication cycle.

Critical assessment of influenza VLP production in Sf9 and HEK293 expression systems.

BACKGROUND: Each year, influenza is responsible for hundreds of thousand cases of illness and deaths worldwide. Due to the virus' fast mutation rate, the World Health Organization (WHO) is constantly on alert to rapidly respond to emerging pandemic strains. Although anti-viral therapies exist, the most proficient way to stop the spread of disease is through vaccination. The majority of influenza vaccines on the market are produced in embryonic hen's eggs and are composed of purified viral antigens from inactivated whole virus. This manufacturing system, however, is limited in its production capacity. Cell culture produced vaccines have been proposed for their potential to overcome the problems associated with egg-based production. Virus-like particles (VLPs) of influenza virus are promising candidate vaccines under consideration by both academic and industry researchers. METHODS: In this study, VLPs were produced in HEK293 suspension cells using the Bacmam transduction system and Sf9 cells using the baculovirus infection system. The proposed systems were assessed for their ability to produce influenza VLPs composed of Hemagglutinin (HA), Neuraminidase (NA) and Matrix Protein (M1) and compared through the lens of bioprocessing by highlighting baseline production yields and bioactivity. VLPs from both systems were characterized using available influenza quantification techniques, such as single radial immunodiffusion assay (SRID), HA assay, western blot and negative staining transmission electron microscopy (NSTEM) to quantify total particles. RESULTS: For the HEK293 production system, VLPs were found to be associated with the cell pellet in addition to those released in the supernatant. Sf9 cells produced 35 times more VLPs than HEK293 cells. Sf9-VLPs had higher total HA activity and were generally more homogeneous in morphology and size. However, Sf9 VLP samples contained 20 times more baculovirus than VLPs, whereas 293 VLPs were produced along with vesicles. CONCLUSIONS: This study highlights key production hurdles that must be overcome in both expression platforms, namely the presence of contaminants and the ensuing quantification challenges, and brings up the question of what truly constitutes an influenza VLP candidate vaccine.

Combined quantum mechanics/molecular mechanics (QM/MM) simulations for protein-ligand complexes: free energies of binding of water molecules in influenza neuraminidase.

The applicability of combined quantum mechanics/molecular mechanics (QM/MM) methods for the calculation of absolute binding free energies of conserved water molecules in protein/ligand complexes is demonstrated. Here, we apply QM/MM Monte Carlo simulations to investigate binding of water molecules to influenza neuraminidase. We investigate five different complexes, including those with the drugs oseltamivir and peramivir. We investigate water molecules in two different environments, one more hydrophobic and one hydrophilic. We calculate the free-energy change for perturbation of a QM to MM representation of the bound water molecule. The calculations are performed at the BLYP/aVDZ (QM) and TIP4P (MM) levels of theory, which we have previously demonstrated to be consistent with one another for QM/MM modeling. The results show that the QM to MM perturbation is significant in both environments (greater than 1 kcal mol(-1)) and larger in the more hydrophilic site. Comparison with the same perturbation in bulk water shows that this makes a contribution to binding. The results quantify how electronic polarization differences in different environments affect binding affinity and also demonstrate that extensive, converged QM/MM free-energy simulations, with good levels of QM theory, are now practical for protein/ligand complexes.

Strategies to calculate water binding free energies in protein-ligand complexes.

Water molecules are commonplace in protein binding pockets, where they can typically form a complex between the protein and a ligand or become displaced upon ligand binding. As a result, it is often of great interest to establish both the binding free energy and location of such molecules. Several approaches to predicting the location and affinity of water molecules to proteins have been proposed and utilized in the literature, although it is often unclear which method should be used under what circumstances. We report here a comparison between three such methodologies, Just Add Water Molecules (JAWS), Grand Canonical Monte Carlo (GCMC), and double-decoupling, in the hope of understanding the advantages and limitations of each method when applied to enclosed binding sites. As a result, we have adapted the JAWS scoring procedure, allowing the binding free energies of strongly bound water molecules to be calculated to a high degree of accuracy, requiring significantly less computational effort than more rigorous approaches. The combination of JAWS and GCMC offers a route to a rapid scheme capable of both locating and scoring water molecules for rational drug design.

Cell culture-selected substitutions in influenza A(H3N2) neuraminidase affect drug susceptibility assessment.

Assessment of drug susceptibility has become an integral part of influenza virus surveillance. In this study, we describe the drug resistance profile of influenza A(H3N2) virus, A/Mississippi/05/2011, collected from a patient treated with oseltamivir and detected via surveillance. An MDCK cell-grown isolate of this virus exhibited highly reduced inhibition by the neuraminidase (NA) inhibitors (NAIs) oseltamivir (8,005-fold), zanamivir (813-fold), peramivir (116-fold), and laninamivir (257-fold) in the NA inhibition assay. Sequence analysis of its NA gene revealed a known oseltamivir-resistance marker, the glutamic acid-to-valine substitution at position 119 (E119V), and an additional change, threonine to isoleucine at position 148 (T148I). Unlike E119V, T148I was not detected in the clinical sample but acquired during viral propagation in MDCK cells. Using recombinant proteins, T148I by itself was shown to cause only a 6-fold increase in the zanamivir 50% inhibitory concentration (IC50) and had no effect on inhibition by other drugs. The T148I substitution reduced NA activity by 50%, most likely by affecting the positioning of the 150 loop at the NA catalytic site. Using pyrosequencing, changes at T148 were detected in 35 (23%) of 150 MDCK cell-grown A(H3N2) viruses tested, which was lower than the frequency of changes at D151 (85%), an NA residue previously implicated in cell selection. We demonstrate that culturing of the A(H3N2) viruses (n = 11) at a low multiplicity of infection delayed the emergence of the NA variants with changes at position 148 and/or 151, especially when conducted in MDCK-SIAT1 cells. Our findings highlight the current challenges in monitoring susceptibility of influenza A(H3N2) viruses to the NAI class of antiviral drugs.

Molecular dynamics-based virtual screening: accelerating the drug discovery process by high-performance computing.

High-performance computing (HPC) has become a state strategic technology in a number of countries. One hypothesis is that HPC can accelerate biopharmaceutical innovation. Our experimental data demonstrate that HPC can significantly accelerate biopharmaceutical innovation by employing molecular dynamics-based virtual screening (MDVS). Without using HPC, MDVS for a 10K compound library with tens of nanoseconds of MD simulations requires years of computer time. In contrast, a state of the art HPC can be 600 times faster than an eight-core PC server is in screening a typical drug target (which contains about 40K atoms). Also, careful design of the GPU/CPU architecture can reduce the HPC costs. However, the communication cost of parallel computing is a bottleneck that acts as the main limit of further virtual screening improvements for drug innovations.

Bayesian coalescent analysis of pandemic H1N1 influenza A virus circulating in the South American region.

The first influenza pandemic of this century was declared in April of 2009, with the emergence of a novel H1N1 influenza A virus strain (H1N1pdm). Understanding the evolution of H1N1pdm populations within the South American region is essential for studying global diversification, emergence, resistance and vaccine efficacy. In order to gain insight into these matters, we have performed a Bayesian coalescent Markov Chain Monte Carlo analysis of hemagglutinin (HA) and neuraminidase (NA) gene sequences of all available and comparable HA and NA sequences obtained from H1N1pdm IAV circulating in the South American region. High evolutionary rates and fast population growths characterize the population dynamics of H1N1pdm strains in this region of the world. A significant contribution of first codon position to the mean evolutionary rate was found for both genes studied, revealing a high contribution of non-synonymous substitutions to the mean substitution rate. In the 178days period covered by these studies, substitutions in all HA epitope regions can be observed. HA substitutions D239G/N and Q310H have been observed only in Brazilian patients. While substitution D239G/N is not particularly associated to a specific genetic lineage, all strains bearing substitution Q310H were assigned to clade 6, suggesting a founder effect. None of the substitutions found in the NA proteins of H1N1pdm strains isolated in South America appears sufficiently close to affect the drug binding pocket for the three NA inhibitor antivirals tested. A more detailed analysis of NA proteins revealed epitope differences among 2010 vaccine and H1N1pdm IAV strains circulating in the South American region.

Protein-ligand binding affinity predictions by implicit solvent simulations: a tool for lead optimization?

Continuum electrostatics is combined with rigorous free-energy calculations in an effort to deliver a reliable and efficient method for in silico lead optimization. The methodology is tested by calculation of the relative binding free energies of a set of inhibitors of neuraminidase, cyclooxygenase2, and cyclin-dependent kinase 2. The calculated free energies are compared to the results obtained with explicit solvent simulations and empirical scoring functions. For cyclooxygenase2, deficiencies in the continuum electrostatics theory are identified and corrected with a modified simulation protocol. For neuraminidase, it is shown that a continuum representation of the solvent leads to markedly different protein-ligand interactions compared to the explicit solvent simulations, and a reconciliation of the two protocols is problematic. Cyclin-dependent kinase 2 proves more challenging, and none of the methods employed in this study yield high quality predictions. Despite the differences observed, for these systems, the use of an implicit solvent framework to predict the ranking of congeneric inhibitors to a protein is shown to be faster, as accurate or more accurate than the explicit solvent protocol, and superior to empirical scoring schemes.

A Monte Carlo sampling method of amino acid sequences adaptable to given main-chain atoms in the proteins.

We have developed a computational method of protein design to detect amino acid sequences that are adaptable to given main-chain coordinates of a protein. In this method, the selection of amino acid types employs a Metropolis Monte Carlo method with a scoring function in conjunction with the approximation of free energies computed from 3D structures. To compute the scoring function, a side-chain prediction using another Metropolis Monte Carlo method was performed to select structurally suitable side-chain conformations from a side-chain library. In total, two layers of Monte Carlo procedures were performed, first to select amino acid types (1st layer Monte Carlo) and then to predict side-chain conformations (2nd layers Monte Carlo). We applied this method to sequence design for the entire sequence on the SH3 domain, Protein G, and BPTI. The predicted sequences were similar to those of the wild-type proteins. We compared the results of the predictions with and without the 2nd layer Monte Carlo method. The results revealed that the two-layer Monte Carlo method produced better sequence similarity to the wild-type proteins than the one-layer method. Finally, we applied this method to neuraminidase of influenza virus. The results were consistent with the sequences identified from the isolated viruses.

A Monte Carlo method for finding important ligand fragments from receptor data.

A simulated annealing method for finding important ligand fragments is described. At a given temperature, ligand fragments are randomly selected and randomly placed within the given receptor cavity, often replacing or forming bonds with existing ligand fragments. For each new ligand fragment combination, the bonded, nonbonded, polarization and solvation energies of the new ligand-receptor system are compared to the previous configuration. Acceptance or rejection of the new system is decided using the Boltzmann distribution e-E/kT, where E is the energy difference between the old and new systems, k is the Boltzmann constant and T is the temperature. Thus, energetically unfavorable fragment switches are sometimes accepted, sacrificing immediate energy gains in the interest of findings a system with minimum energy. By lowering the temperature, the rate of unfavorable switches decreases and energetically favorable combinations become more difficult to change. The process is terminated when the frequency of switches becomes too small. As a test, the method predicted positions and types of important ligand fragments for neuraminidase that were in accord with the known ligand, sialic acid.