Conformational ensemble of the poliovirus 3CD precursor observed by MD simulations and confirmed by SAXS: A strategy to expand the viral proteome?

The genomes of RNA viruses are relatively small. To overcome the small-size limitation, RNA viruses assign distinct functions to the processed viral proteins and their precursors. This is exemplified by poliovirus 3CD protein. 3C protein is a protease and RNA-binding protein. 3D protein is an RNA-dependent RNA polymerase (RdRp). 3CD exhibits unique protease and RNA-binding activities relative to 3C and is devoid of RdRp activity. The origin of these differences is unclear, since crystal structure of 3CD revealed “beads-on-a-string” structure with no significant structural differences compared to the fully processed proteins. We performed molecular dynamics (MD) simulations on 3CD to investigate its conformational dynamics. A compact conformation of 3CD was observed that was substantially different from that shown crystallographically. This new conformation explained the unique properties of 3CD relative to the individual proteins. Interestingly, simulations of mutant 3CD showed altered interface. Additionally, accelerated MD simulations uncovered a conformational ensemble of 3CD. When we elucidated the 3CD conformations in solution using small-angle X-ray scattering (SAXS) experiments a range of conformations from extended to compact was revealed, validating the MD simulations. The existence of conformational ensemble of 3CD could be viewed as a way to expand the poliovirus proteome, an observation that may extend to other viruses

Common and Unique Network Dynamics in Football Games

The sport of football is played between two teams of eleven players each using a spherical ball. Each team strives to score by driving the ball into the opposing goal as the result of skillful interactions among players. Football can be regarded from the network perspective as a competitive relationship between two cooperative networks with a dynamic network topology and dynamic network node. Many complex large-scale networks have been shown to have topological properties in common, based on a small-world network and scale-free network models. However, the human dynamic movement pattern of this network has never been investigated in a real-world setting. Here, we show that the power law in degree distribution emerged in the passing behavior in the 2006 FIFA World Cup Final and an international “A” match in Japan, by describing players as vertices connected by links representing passes. The exponent values are similar to the typical values that occur in many real-world networks, which are in the range of , and are larger than that of a gene transcription network, . Furthermore, we reveal the stochastically switched dynamics of the hub player throughout the game as a unique feature in football games. It suggests that this feature could result not only in securing vulnerability against intentional attack, but also in a power law for self-organization. Our results suggest common and unique network dynamics of two competitive networks, compared with the large-scale networks that have previously been investigated in numerous works. Our findings may lead to improved resilience and survivability not only in biological networks, but also in communication networks

Different Domains of the RNA Polymerase of Infectious Bursal Disease Virus Contribute to Virulence

BACKGROUND: Infectious bursal disease virus (IBDV) is a pathogen of worldwide significance to the poultry industry. IBDV has a bi-segmented double-stranded RNA genome. Segments A and B encode the capsid, ribonucleoprotein and non-structural proteins, or the virus polymerase (RdRp), respectively. Since the late eighties, very virulent (vv) IBDV strains have emerged in Europe inducing up to 60% mortality. Although some progress has been made in understanding the molecular biology of IBDV, the molecular basis for the pathogenicity of vvIBDV is still not fully understood. METHODOLOGY, PRINCIPAL FINDINGS: Strain 88180 belongs to a lineage of pathogenic IBDV phylogenetically related to vvIBDV. By reverse genetics, we rescued a molecular clone (mc88180), as pathogenic as its parent strain. To study the molecular basis for 88180 pathogenicity, we constructed and characterized in vivo reassortant or mosaic recombinant viruses derived from the 88180 and the attenuated Cu-1 IBDV strains. The reassortant virus rescued from segments A of 88180 (A88) and B of Cu-1 (BCU1) was milder than mc88180 showing that segment B is involved in 88180 pathogenicity. Next, the exchange of different regions of BCU1 with their counterparts in B88 in association with A88 did not fully restore a virulence equivalent to mc88180. This demonstrated that several regions if not the whole B88 are essential for the in vivo pathogenicity of 88180. CONCLUSION, SIGNIFICANCE: The present results show that different domains of the RdRp, are essential for the in vivo pathogenicity of IBDV, independently of the replication efficiency of the mosaic viruses

APBSmem: A Graphical Interface for Electrostatic Calculations at the Membrane

Electrostatic forces are one of the primary determinants of molecular interactions. They help guide the folding of proteins, increase the binding of one protein to another and facilitate protein-DNA and protein-ligand binding. A popular method for computing the electrostatic properties of biological systems is to numerically solve the Poisson-Boltzmann (PB) equation, and there are several easy-to-use software packages available that solve the PB equation for soluble proteins. Here we present a freely available program, called APBSmem, for carrying out these calculations in the presence of a membrane. The Adaptive Poisson-Boltzmann Solver (APBS) is used as a back-end for solving the PB equation, and a Java-based graphical user interface (GUI) coordinates a set of routines that introduce the influence of the membrane, determine its placement relative to the protein, and set the membrane potential. The software Jmol is embedded in the GUI to visualize the protein inserted in the membrane before the calculation and the electrostatic potential after completing the computation. We expect that the ease with which the GUI allows one to carry out these calculations will make this software a useful resource for experimenters and computational researchers alike. Three examples of membrane protein electrostatic calculations are carried out to illustrate how to use APBSmem and to highlight the different quantities of interest that can be calculated