Characterizing Receptor Flexibility to Predict Mutations That Lead to Human Adaptation of Influenza Hemagglutinin.

A key step in the emergence of human pandemic influenza strains has been a switch in binding preference of the viral glycoprotein hemagglutinin (HA) from avian to human sialic acid (SA) receptors. The conformation of the bound SA varies substantially with HA sequence, and crystallographic evidence suggests that the bound SA is flexible, making it difficult to predict which mutations are responsible for changing HA-binding preference. We performed molecular dynamics (MD) simulations of SA analogues binding to various HAs and observed a dynamic equilibrium among structurally diverse receptor conformations, including conformations that have not been experimentally observed. Using one such novel conformation, we predicted─and experimentally confirmed─a set of mutations that substantially increased an HA's affinity for a human SA analogue. This prediction could not have been inferred from the existing crystal structures, suggesting that MD-generated HA-SA conformational ensembles could help researchers predict human-adaptive mutations, aiding surveillance of emerging pandemic threats.

Quantitative Characterization of the Binding and Unbinding of Millimolar Drug Fragments with Molecular Dynamics Simulations.

A quantitative characterization of the binding properties of drug fragments to a target protein is an important component of a fragment-based drug discovery program. Fragments typically have a weak binding affinity, however, making it challenging to experimentally characterize key binding properties, including binding sites, poses, and affinities. Direct simulation of the binding equilibrium by molecular dynamics (MD) simulations can provide a computational route to characterize fragment binding, but this approach is so computationally intensive that it has thus far remained relatively unexplored. Here, we perform MD simulations of sufficient length to observe several different fragments spontaneously and repeatedly bind to and unbind from the protein FKBP, allowing the binding affinities, on- and off-rates, and relative occupancies of alternative binding sites and alternative poses within each binding site to be estimated, thereby illustrating the potential of long time scale MD as a quantitative tool for fragment-based drug discovery. The data from the long time scale fragment binding simulations reported here also provide a useful benchmark for testing alternative computational methods aimed at characterizing fragment binding properties. As an example, we calculated binding affinities for the same fragments using a standard free energy perturbation approach and found that the values agreed with those obtained from the fragment binding simulations within statistical error.