Combining Molecular Dynamics with Bayesian Analysis To Predict and Evaluate Ligand-Binding Mutations in Influenza Hemagglutinin.

P. M. Kasson, D. L. Ensign, and V. S. Pande.
Journal of the American Chemical Society (2009). Published online 2009 July 28.

The influenza virus infects people and animals by binding to complex sugar molecules on the surface of the respiratory tract. Bird viruses bind most strongly to bird cell-surface sugars and human viruses bind most strongly to human cell-surface sugars. As the recent swine-origin influenza virus has demonstrated, there is considerable overlap between the binding ability of human and pig viruses to cells of the other host. Changes to this binding affinity are one key component for viruses to make a jump between species, and it is difficult to predict the necessary mutations ahead of time. We would like to predict high-risk mutations to enable better surveillance and early control of potential inter-species transmission events. This work represents a first step in that direction, as we examine mutations to H5N1 avian influenza that alter ligand binding. We use Folding@home as a powerful computational screen to evaluate mutations that will eventually require experimental testing to verify.

Influenza virus attaches to and infects target cells via binding of cell-surface glycans by the viral hemagglutinin. This binding specificity is considered a major reason why avian influenza is typically poorly transmitted between humans, while swine influenza is better transmitted due to glycan similarity between the human and swine upper respiratory tract. Predicting mutations that control glycan binding is thus important to continued surveillance against new pandemic influenza strains. We have designed a molecular-dynamics approach for scoring potential mutants with predictive power for both receptor-binding-domain and allosteric mutations similar to those identified from clinical isolates of avian influenza. We have performed thousands of simulations of 17 different hemagglutinin mutants totaling >1 ms in length and employ a Bayesian model to rank mutations that disrupt the stability of the hemagglutinin−ligand complex. Based on our simulations, we predict a significantly increased koff for seven of these mutants. This means of using molecular dynamics analysis to make experimentally verifiable predictions offers a potentially general method to identify ligand-binding mutants, particularly allosteric ones. Our analysis of ligand dissociation provides a means to evaluate mutants prior to experimental mutagenesis and testing and constitutes an important step toward understanding the determinants of ligand binding by H5N1 influenza.