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

Influenza attaches to host cells by binding cell-surface glycans via the viral hemagglutinin protein. Influenza hemagglutinin binds glycans in a species-specific manner: avian strains of influenza selectively bind glycans found in the avian upper respiratory tract, while human strains selectively bind human upper respiratory tract glycans.1 Changes to this specificity are considered among the key factors required for efficient transmission of avian influenza between humans. In contrast, recent transmission between swine and humans is eased by the marked similarity between swine and human upper respiratory tract glycans2-5. Structural studies of H1N1 influenza from 1918 implicate specificity changes in the influenza pandemic of 1918-1919,6,7 and retrospective characterizations of H5N1 avian influenza isolates from humans find mutations that shift H5N1 to an intermediate specificity between avian-type and human-type glycans 8-10. Despite the success of these retrospective analyses, prospective prediction of H5N1 mutations remains a much more difficult task. Influenza hemagglutinin is heavily glycosylated 11, and the viral glycans can affect both affinity and specificity for host glycans 12-14. Even using simplified ligands, large-scale expression and experimental screening of hemagglutinin glycoprotein mutants for specificity changes remain challenging due to biosafety issues and the difficulty of doing large-scale mutagenesis in cell-culture systems that will produce the relevant hemagglutinin glycosylation patterns. We have therefore designed an approach to large-scale computational screening of hemagglutinin mutants that will allow more directed experimental validation. A number of experimental and computational methods have been developed to examine receptor-binding-domain mutants, but ligand-binding mutants from clinical isolates of influenza virus encompass both receptor-binding-domain and allosteric sites. Experimental data for allosteric sites are particularly sparse due to the challenges of high-throughput mutagenesis and screening of influenza hemagglutinin. We have designed a molecular-dynamics approach to score potential mutants with robust predictive power for both receptor-binding-domain and allosteric mutations. We perform thousands of simulations of 17 hemagglutinin mutants totaling >1 millisecond in length and employ a Bayesian model to rank mutations that disrupt hemagglutinin-ligand complex stability. Based on our analysis, we predict a significantly increased koff for 7 of these mutants. This means of analyzing molecular-dynamics data to make experimentally verifiable predictions offers a potentially general method to identify ligand-binding mutants, particularly allosteric ones. Our analysis provides a robust means to evaluate mutants prior to experimental mutagenesis and testing; these results also constitute an important step towards understanding the determinants of ligand binding by H5N1 influenza. Dissociation rates were chosen as a means to evaluate predicted ligand-binding mutants because the association and dissociation rates (kon and koff) of ligands from wild-type hemagglutinins is relatively slow—data on monovalent kon and koff are not available, but X-31 hemagglutinin rosettes bind fetuin with multivalent rates of kon = 2× 103 M−1 s−1 and koff = 2× 10−4 s−1.15 Experimental dissociation rates reported for hemagglutinin vary by up to 10,000-fold, however, based in part on the surface conjugation15,16. Depending on whether a ligand-binding mutation alters the transition-state free energy or only the free energy of the bound state, it would alter both kon and koff or kon alone. We can sample and estimate fast processes more accurately via molecular dynamics than we can slow ones, so acceleration of koff is a more accessible parameter than deceleration of kon. Computational methods to predict free energies of binding under active development,17 but predicting binding of charged, flexible ligands by a flexible protein is extremely challenging for current methods. Methods based on molecular mechanics-Generalized Born calculations have recently been applied to predict hemagglutinin glycan binding.18 These show promise for predicting receptor-binding-domain mutations, while our molecular-dynamics-based calculations are designed also to detect allosteric mutants in a robust fashion.