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 bindinginteraction . 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.
In many biological applications, we would like to be able to computationally predict mutational effects on affinity in protein-protein interactions. However, many commonly used methods to predict these effects 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 an existing method applied in a novel way to a new test case; we interrogate affinity differences resulting from mutations in a host-virus protein-protein interface. We use steered molecular dynamics (SMD) to computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1) . We then approximate affinity using the maximum applied force of separation and the area under the force-versus-distance curve. We find , even without the rigor and planning required for free energy calculations, that these quantities can 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. Moreover, we show that this simple SMD scheme correlates well with relative free energy differences computed via free energy perturbation. Second, although the static co-crystal structure shows two large hydrogen-bonding networks in the GP1/hTfR1 interface, our simulations indicate that one of them may not be important for tight binding . Third, one viral site known to be critical for infection may mark an important evolutionary suppressor site for infection-resistant hTfR1 mutants. Finally, our approach provides a framework to compare the effects of multiple mutations, individually and jointly, on protein-protein interactions.
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Existing computational methods to predict protein--protein interaction affinity often
In many biological applications, we would like to be able to computationally predict mutational effects on affinity in protein-protein interactions. However, many commonly used methods to predict these effects
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a new method to
an existing method applied in a novel way to a new test case; we
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protein--protein interface. Our method is based on extensive non-equilibrium all-atom simulations: We
protein-protein interface. We use steered molecular dynamics (SMD) to
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and estimate
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during a pulling simulation
of separation
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, even without the rigor and planning required for free energy calculations,
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can
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wild-type
wild type
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Moreover, we show that this simple SMD scheme correlates well with relative free energy differences computed via free energy perturbation.
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only
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is critical for the bindinginteraction
may not be important for tight binding
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method provides an elegant
approach provides a
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protein--protein
protein-protein