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Outbreaks of influenza cause considerable public health concerns and pose a potential threat of a global pandemic. In this paper, we describe some of our recent work on modeling of influenza virus with an IBM Blue Gene® supercomputer. The goal is to predict which mutations [on the viral glycoprotein hemagglutinin (HA)] are likely to occur in the next flu season, which mutations might escape antibody (Ab) neutralization, and which mutations might cause its receptor binding specificity to switch (e.g., from avian to human). We have analyzed more than 4,000 influenza A/H3N2 HA sequences from 1968 to 2010 to model the evolutionary path using a weighted mutual information method, which allows us to build a site transition network to predict antigenic drifts. We then used large-scale free energy perturbation calculations to study the mutation-induced effects on the antigen–Ab and antigen–receptor bindings. For example, we found that a single mutation T131I on H3N2 HA can decrease the HA–Ab binding affinity by 5.2
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