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PeerJ. 2014 Feb 27;2:e266. doi: 10.7717/peerj.266. eCollection 2014.

Analyzing machupo virus-receptor binding by molecular dynamics simulations.

Author information

1
Department of Integrative Biology, Institute for Cellular and Molecular Biology, and Center for Computational Biology and Bioinformatics, The University of Texas at Austin , Austin, TX , USA ; Department of Molecular Biosciences, Institute for Cellular and Molecular Biology, The University of Texas at Austin , Austin, TX , USA ; School of Medicine, Texas Tech University Health Sciences Center , Lubbock, TX , USA.
2
Department of Molecular Biosciences, Institute for Cellular and Molecular Biology, The University of Texas at Austin , Austin, TX , USA.
3
Department of Integrative Biology, Institute for Cellular and Molecular Biology, and Center for Computational Biology and Bioinformatics, The University of Texas at Austin , Austin, TX , USA.

Abstract

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.

KEYWORDS:

Arenavirus; Computational mutagenesis; Free energy perturbation; Machupo; Molecular dynamics; Protein–protein interaction

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