Haemagglutinin mutations and glycosylation changes shaped the 2012/13 influenza A(H3N2) epidemic, Houston, Texas.

While the early start and higher intensity of the 2012/13 influenza A virus (IAV) epidemic was not unprecedented, it was the first IAV epidemic season since the 2009 H1N1 influenza pandemic where the H3N2 subtype predominated. We directly sequenced the genomes of 154 H3N2 clinical specimens collected throughout the epidemic to better understand the evolution of H3N2 strains and to inform the H3N2 vaccine selection process. Phylogenetic analyses indicated that multiple co-circulating clades and continual antigenic drift in the haemagglutinin (HA) of clades 5, 3A, and 3C, with the evolution of a new 3C subgroup (3C-2012/13), were the driving causes of the epidemic. Drift variants contained HA substitutions and alterations in the potential N-linked glycosylation sites of HA. Antigenic analysis demonstrated that viruses in the emerging subclade 3C.3 and subgroup 3C-2012/13 were not well inhibited by antisera generated against the 3C.1 vaccine strains used for the 2012/13 (A/Victoria/361/2011) or 2013/14 (A/Texas/50/2012) seasons. Our data support updating the H3N2 vaccine strain to a clade 3C.2 or 3C.3-like strain or a subclade that has drifted further. They also underscore the challenges in vaccine strain selection, particularly regarding HA and neuraminidase substitutions derived during laboratory passage that may alter antigenic testing accuracy.

How common is the funnel-like energy landscape in protein-protein interactions?

The goal of this study is to verify the concept of the funnel-like intermolecular energy landscape in protein-protein interactions by use of a series of computational experiments. Our preliminary analysis revealed the existence of the funnel in many protein-protein interactions. However, because of the uncertainties in the modeling of these interactions and the ambiguity of the analysis procedures, the detection of the funnels requires detailed quantitative approaches to the energy landscape analysis. A number of such approaches are presented in this study. We show that the funnel detection problem is equivalent to a problem of distinguishing between distributions of low-energy intermolecular matches in the funnel and in the low-frequency landscape fluctuations. If the fluctuations are random, the decision about whether the minimum is the funnel is equivalent to determining whether this minimum is significantly different from a would-be random one. A database of 475 nonredundant cocrystallized protein-protein complexes was used to re-dock the proteins by use of smoothed potentials. To detect the funnel, we developed a set of sophisticated models of random matches. The funnel was considered detected if the binding area was more populated by the low-energy docking predictions than by the matches generated in the random models. The number of funnels detected by use of different random models varied significantly. However, the results confirmed that the funnel may be the general feature in protein-protein association.