Dynamics of Glycoprotein Charge in the Evolutionary History of Human Influenza

Influenza viruses show a significant capacity to evade host immunity; this is manifest both as large occasional jumps in the antigenic phenotype of viral surface molecules and in gradual antigenic changes leading to annual influenza epidemics in humans. Recent mouse studies show that avidity for host cells can play an important role in polyclonal antibody escape, and further that electrostatic charge of the hemagglutinin glycoprotein can contribute to such avidity.We test the role of glycoprotein charge on sequence data from the three major subtypes of influenza A in humans, using a simple method of calculating net glycoprotein charge. Of all subtypes, H3N2 in humans shows a striking pattern of increasing positive charge since its introduction in 1968. Notably, this trend applies to both hemagglutinin and neuraminidase glycoproteins. In the late 1980s hemagglutinin charge reached a plateau, while neuraminidase charge started to decline. We identify key groups of amino acid sites involved in this charge trend.To our knowledge these are the first indications that, for human H3N2, net glycoprotein charge covaries strongly with antigenic drift on a global scale. Further work is needed to elucidate how such charge interacts with other immune escape mechanisms, such as glycosylation, and we discuss important questions arising for future study

Quantifying Water-Mediated Protein–Ligand Interactions in a Glutamate Receptor: A DFT Study

It is becoming increasingly clear that careful treatment of water molecules in ligand–protein interactions is required in many cases if the correct binding pose is to be identified in molecular docking. Water can form complex bridging networks and can play a critical role in dictating the binding mode of ligands. A particularly striking example of this can be found in the ionotropic glutamate receptors. Despite possessing similar chemical moieties, crystal structures of glutamate and α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid (AMPA) in complex with the ligand-binding core of the GluA2 ionotropic glutamate receptor revealed, contrary to all expectation, two distinct modes of binding. The difference appears to be related to the position of water molecules within the binding pocket. However, it is unclear exactly what governs the preference for water molecules to occupy a particular site in any one binding mode. In this work we use density functional theory (DFT) calculations to investigate the interaction energies and polarization effects of the various components of the binding pocket. Our results show (i) the energetics of a key water molecule are more favorable for the site found in the glutamate-bound mode compared to the alternative site observed in the AMPA-bound mode, (ii) polarization effects are important for glutamate but less so for AMPA, (iii) ligand–system interaction energies alone can predict the correct binding mode for glutamate, but for AMPA alternative modes of binding have similar interaction energies, and (iv) the internal energy is a significant factor for AMPA but not for glutamate. We discuss the results within the broader context of rational drug-design

Switching Transport through Nanopores with pH-Responsive Polymer Brushes for Controlled Ion Permeability

Several nanoporous platforms were functionalized with pH-responsive poly(methacrylic acid) (PMAA) brushes using surface-initiated atom transfer radical polymerization (SI-ATRP). The growth of the PMAA brush and its pH-responsive behavior from the nanoporous platforms were confirmed by scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, and atomic force microscopy (AFM). The swelling behavior of the pH-responsive PMAA brushes grafted only from the nanopore walls was investigated by AFM in aqueous liquid environment with pH values of 4 and 8. AFM images displayed open nanopores at pH 4 and closed ones at pH 8, which rationalizes their use as gating platforms. Ion conductivity across the nanopores was investigated with current–voltage measurements at various pH values. Enhanced higher resistance across the nanopores was observed in a neutral polymer brush state (lower pH values) and lower resistance when the brush was charged (higher pH values). By adding a fluorescent dye in an environment of pH 4 or pH 8 at one side of the PMAA-brush functionalized nanopore array chips, diffusion across the nanopores was followed. These experiments displayed faster diffusion rates of the fluorescent molecules at pH 4 (PMAA neutral state, open pores) and slower diffusion at pH 8 (PMAA charged state, closed pores) showing the potential of this technology toward nanoscale valve applications

The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale

<p>Abstract</p> <p>Background</p> <p>Computational models play an increasingly important role in the assessment and control of public health crises, as demonstrated during the 2009 H1N1 influenza pandemic. Much research has been done in recent years in the development of sophisticated data-driven models for realistic computer-based simulations of infectious disease spreading. However, only a few computational tools are presently available for assessing scenarios, predicting epidemic evolutions, and managing health emergencies that can benefit a broad audience of users including policy makers and health institutions.</p> <p>Results</p> <p>We present "GLEaMviz", a publicly available software system that simulates the spread of emerging human-to-human infectious diseases across the world. The GLEaMviz tool comprises three components: the client application, the proxy middleware, and the simulation engine. The latter two components constitute the GLEaMviz server. The simulation engine leverages on the Global Epidemic and Mobility (GLEaM) framework, a stochastic computational scheme that integrates worldwide high-resolution demographic and mobility data to simulate disease spread on the global scale. The GLEaMviz design aims at maximizing flexibility in defining the disease compartmental model and configuring the simulation scenario; it allows the user to set a variety of parameters including: compartment-specific features, transition values, and environmental effects. The output is a dynamic map and a corresponding set of charts that quantitatively describe the geo-temporal evolution of the disease. The software is designed as a client-server system. The multi-platform client, which can be installed on the user's local machine, is used to set up simulations that will be executed on the server, thus avoiding specific requirements for large computational capabilities on the user side.</p> <p>Conclusions</p> <p>The user-friendly graphical interface of the GLEaMviz tool, along with its high level of detail and the realism of its embedded modeling approach, opens up the platform to simulate realistic epidemic scenarios. These features make the GLEaMviz computational tool a convenient teaching/training tool as well as a first step toward the development of a computational tool aimed at facilitating the use and exploitation of computational models for the policy making and scenario analysis of infectious disease outbreaks.</p

A method for detergent-free isolation of membrane proteins in their local lipid environment.

Despite the great importance of membrane proteins, structural and functional studies of these proteins present major challenges. A significant hurdle is the extraction of the functional protein from its natural lipid membrane. Traditionally achieved with detergents, purification procedures can be costly and time consuming. A critical flaw with detergent approaches is the removal of the protein from the native lipid environment required to maintain functionally stable protein. This protocol describes the preparation of styrene maleic acid (SMA) co-polymer to extract membrane proteins from prokaryotic and eukaryotic expression systems. Successful isolation of membrane proteins into SMA lipid particles (SMALPs) allows the proteins to remain with native lipid, surrounded by SMA. We detail procedures for obtaining 25 g of SMA (4 d); explain the preparation of protein-containing SMALPs using membranes isolated from Escherichia coli (2 d) and control protein-free SMALPS using E. coli polar lipid extract (1-2 h); investigate SMALP protein purity by SDS-PAGE analysis and estimate protein concentration (4 h); and detail biophysical methods such as circular dichroism (CD) spectroscopy and sedimentation velocity analytical ultracentrifugation (svAUC) to undertake initial structural studies to characterize SMALPs (∼2 d). Together, these methods provide a practical tool kit for those wanting to use SMALPs to study membrane proteins

Accessing a Hidden Conformation of the Maltose Binding Protein Using Accelerated Molecular Dynamics

Periplasmic binding proteins (PBPs) are a large family of molecular transporters that play a key role in nutrient uptake and chemotaxis in Gram-negative bacteria. All PBPs have characteristic two-domain architecture with a central interdomain ligand-binding cleft. Upon binding to their respective ligands, PBPs undergo a large conformational change that effectively closes the binding cleft. This conformational change is traditionally viewed as a ligand induced-fit process; however, the intrinsic dynamics of the protein may also be crucial for ligand recognition. Recent NMR paramagnetic relaxation enhancement (PRE) experiments have shown that the maltose binding protein (MBP) - a prototypical member of the PBP superfamily - exists in a rapidly exchanging (ns to µs regime) mixture comprising an open state (approx 95%), and a minor partially closed state (approx 5%). Here we describe accelerated MD simulations that provide a detailed picture of the transition between the open and partially closed states, and confirm the existence of a dynamical equilibrium between these two states in apo MBP. We find that a flexible part of the protein called the balancing interface motif (residues 175–184) is displaced during the transformation. Continuum electrostatic calculations indicate that the repacking of non-polar residues near the hinge region plays an important role in driving the conformational change. Oscillations between open and partially closed states create variations in the shape and size of the binding site. The study provides a detailed description of the conformational space available to ligand-free MBP, and has implications for understanding ligand recognition and allostery in related proteins

Real-time numerical forecast of global epidemic spreading: Case study of 2009 A/H1N1pdm

Background Mathematical and computational models for infectious diseases are increasingly used to support public-health decisions; however, their reliability is currently under debate. Real-time forecasts of epidemic spread using data-driven models have been hindered by the technical challenges posed by parameter estimation and validation. Data gathered for the 2009 H1N1 influenza crisis represent an unprecedented opportunity to validate real-time model predictions and define the main success criteria for different approaches. Methods We used the Global Epidemic and Mobility Model to generate stochastic simulations of epidemic spread worldwide, yielding (among other measures) the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. Using a Monte Carlo Maximum Likelihood analysis, the model provided an estimate of the seasonal transmission potential during the early phase of the H1N1 pandemic and generated ensemble forecasts for the activity peaks in the northern hemisphere in the fall/winter wave. These results were validated against the real-life surveillance data collected in 48 countries, and their robustness assessed by focusing on 1) the peak timing of the pandemic; 2) the level of spatial resolution allowed by the model; and 3) the clinical attack rate and the effectiveness of the vaccine. In addition, we studied the effect of data incompleteness on the prediction reliability. Results Real-time predictions of the peak timing are found to be in good agreement with the empirical data, showing strong robustness to data that may not be accessible in real time (such as pre-exposure immunity and adherence to vaccination campaigns), but that affect the predictions for the attack rates. The timing and spatial unfolding of the pandemic are critically sensitive to the level of mobility data integrated into the model. Conclusions Our results show that large-scale models can be used to provide valuable real-time forecasts of influenza spreading, but they require high-performance computing. The quality of the forecast depends on the level of data integration, thus stressing the need for high-quality data in population-based models, and of progressive updates of validated available empirical knowledge to inform these models