Machupo virus (MACV) is a New World arenavirus that is currently emerging from a rodent reservoir into the human population. For New World arenaviruses, it is known that transferrin receptor binding is a major determinate of host range variation. To better understand the relationship between the virus and its human host, we used a structure-based computational model to interrogate the effects of mutations in the interfacebetween the MACV spike glycoprotein ( MACVGP) and the Human transferrin receptor (hTfR1) . Existing computational methods to probe protein-protein interactions perform poorly with non-alanine mutations and on the flexible loops encountered in many host-virus interactions. We applied steered molecular dynamics (SMD) simulations to induce protein dissociation; using descriptive statistics, we were able to differentiate mutant complexes and biochemically rationalize available infectivity data. The previously published co-crystal structure implies two important hydrogen bonding networks in the MACVGP /hTfR1 interface ; our simulations confirm one of them is critical for viral binding . Also, since mutations in polar residues in another putative network do not change viral binding, we can conclude the second network is likely an artifact of crystallization. Finally, we found a viral site known to be critical for infection , may mark an important evolutionary suppressor site for infection-diminishing hTfR1 mutants. Taken together, our computational data rationalize many of the available experimental data, further refine our biochemical understanding of this system, and point to the most likely next evolutionary step for MACV .
Existing computational methods to predict protein--protein interaction affinity often perform poorly in important test cases. In particular, the effects of multiple mutations, non-alanine substitutions, and flexible loops are difficult to predict with available tools and protocols. We present here a new method to interrogate affinity differences resulting from mutations in a host-virus protein--protein interface. Our method is based on extensive non-equilibrium all-atom simulations: We computationally pull the machupo virus (MACV) spike glycoprotein ( GP1) away from the human transferrin receptor (hTfR1) and estimate affinity using the maximum applied force during a pulling simulation and the area under the force-versus-distance curve. We find that these quantities provide novel biophysical insight into the GP1/hTfR1 interaction. First, with no prior knowledge of the system we can differentiate among wild-type and mutant complexes. Second, although the static co-crystal structure shows two large hydrogen-bonding networks in the GP1 /hTfR1 interface , our simulations indicate that only one of them is critical for the binding interaction. Third, one viral site known to be critical for infection may mark an important evolutionary suppressor site for infection-resistant hTfR1 mutants. Finally, our method provides an elegant framework to compare the effects of multiple mutations, individually and jointly, on protein--protein interactions .
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Machupo virus (MACV) is a New World arenavirus that is currently emerging from a rodent reservoir into the human population. For New World arenaviruses, it is known that transferrin receptor binding is a major determinate of host range variation. To better understand the relationship between the virus and its human host, we used a structure-based computational model to interrogate the effects of mutations in the interfacebetween the MACV
Existing computational methods to predict protein--protein interaction affinity often perform poorly in important test cases. In particular, the effects of multiple mutations, non-alanine substitutions, and flexible loops are difficult to predict with available tools and protocols. We present here a new method to interrogate affinity differences resulting from mutations in a host-virus protein--protein interface. Our method is based on extensive non-equilibrium all-atom simulations: We computationally pull the machupo virus (MACV)
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. Existing computational methods to probe protein-protein interactions perform poorly with non-alanine mutations and on the flexible loops encountered in many host-virus interactions. We applied steered molecular dynamics (SMD) simulations to induce protein dissociation; using descriptive statistics, we were able to differentiate mutant complexes and biochemically rationalize available infectivity data. The previously published
and estimate affinity using the maximum applied force during a pulling simulation and the area under the force-versus-distance curve. We find that these quantities provide novel biophysical insight into the GP1/hTfR1 interaction. First, with no prior knowledge of the system we can differentiate among wild-type and mutant complexes. Second, although the static
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Taken together, our computational data rationalize many of the available experimental data, further refine our biochemical understanding of this system, and point to the most likely next evolutionary step for MACV
Finally, our method provides an elegant framework to compare the effects of multiple mutations, individually and jointly, on protein--protein interactions