Stochastic simulations reveal influenza A uses binding and catalysis for directional movement, indicating navigational strategies through spatial patterns.
Influenza A viruses (IAVs) traverse sialylated host barriers like airway mucus layers through coordinated action of its surface proteins: hemagglutinin (HA) binds sialic acid (SA) receptors abundant in mucus, and neuraminidase (NA) cleaves those same receptors. This coupling of multivalent adhesion and localized catalysis generates directed motion that goes beyond simple diffusion. Through stochastic simulations and mean-field modeling, we find that HA binding and NA catalysis operate in a biophysical “Goldilocks regime.” HA-SA binding is strong enough to extract information from receptor landscapes yet weak enough to permit release. This balance suggests evolutionary fine-tuning of HA/NA activity to host barriers. On uniform SA-coated surfaces, IAVs exhibit superdiffusive motion as NA activity creates receptor-depleted wakes that suppress backtracking. Each step thus becomes biased by the virions history of movement. Beyond uniform surfaces, we find that IAVs also respond to spatial patterns in receptors. When SA density varies across the surface, our experiments show that IAVs exhibit directional bias toward higher-receptor regions. Stochastic simulations recapitulate these behaviors and further reveal the role of surface protein organization: in filamentous, wild-type virions with segregated HA and NA domains, front-loaded HA reads the forward receptor field through cycles of multivalent binding and unbinding while the trailing NA removes receptors in its wake. This polarity enhances both gradient sensing and motion persistence, enabling chemotaxis-like behavior without conventional cellular machinery. Together, these results show how local molecular interactions can generate emergent navigation strategies. By coordinating binding, catalysis, and spatial organization, IAVs convert receptor landscapes into guidance cues, offering design principles that could be used in synthetic systems for autonomous, environment-responsive transport.
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Agarwal et al. (2026) studied this question.
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