Haemagglutinin (HA) is the influenza surface glycoprotein that interacts with infectivity-neutralizing antibodies. As a consequence of this immune pressure, it is the variable virus component, which is important in antigenic drift, that results in recurrent epidemics of influenza. We have determined the crystallographic structure of a complex formed between the antigen-binding fragment (Fab) of a neutralizing antibody and the membrane-distal domain ('HA top') of a HA subunit prepared from HA in its membrane-fusion-active conformation. A dramatic change is seen in the structure of the Fab-combining site on complex formation. Our results indicate that neutralization of infectivity by this antibody involves the inhibition of receptor binding, and demonstrate how influenza virus can maintain its conserved receptor-binding site despite the immune selective pressure for change in this region of the molecule; they also contribute to a complete description of the endosomal pH-induced fusion-active HA structure.

BackgroundIn drug design, one may be confronted to the problem of finding hits for targets for which no small inhibiting molecules are known and only low-throughput experiments are available (like ITC or NMR studies), two common difficulties encountered in a typical academic setting. Using a virtual screening strategy like docking can alleviate some of the problems and save a considerable amount of time by selecting only top-ranking molecules, but only if the method is very efficient, i.e. when a good proportion of actives are found in the 1–10 % best ranked molecules.ResultsThe use of several programs (in our study, Gold, Surflex, FlexX and Glide were considered) shows a divergence of the results, which presents a difficulty in guiding the experiments. To overcome this divergence and increase the yield of the virtual screening, we created the standard deviation consensus (SDC) and variable SDC (vSDC) methods, consisting of the intersection of molecule sets from several virtual screening programs, based on the standard deviations of their ranking distributions.ConclusionsSDC allowed us to find hits for two new protein targets by testing only 9 and 11 small molecules from a chemical library of circa 15,000 compounds. Furthermore, vSDC, when applied to the 102 proteins of the DUD-E benchmarking database, succeeded in finding more hits than any of the four isolated programs for 13–60 % of the targets. In addition, when only 10 molecules of each of the 102 chemical libraries were considered, vSDC performed better in the number of hits found, with an improvement of 6–24 % over the 10 best-ranked molecules given by the individual docking programs.Graphical abstractIn drug design, for a given target and a given chemical library, the results obtained with different virtual screening programs are divergent. So how to rationally guide the experimental tests, especially when only a few number of experiments can be made? The variable Standard Deviation Consensus (vSDC) method was developed to answer this issue. Left panel the vSDC principle consists of intersecting molecule sets, chosen on the basis of the standard deviations of their ranking distributions, obtained from various virtual screening programs. In this study Glide, Gold, FlexX and Surflex were used and tested on the 102 targets of the DUD-E database. Right panel Comparison of the average percentage of hits found with vSDC and each of the four programs, when only 10 molecules from each of the 102 chemical libraries of the DUD-E database were considered. On average, vSDC was capable of finding 38 % of the findable hits, against 34 % for Glide, 32 % for Gold, 16 % for FlexX and 14 % for Surflex, showing that with vSDC, it was possible to overcome the unpredictability of the virtual screening results and to improve themElectronic supplementary materialThe online version of this article (doi:10.1186/s13321-016-0112-z) contains supplementary material, which is available to authorized users.