Predicting virus-receptor mutant binding by molecular dynamics simulation

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1 Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, United States
2 Department of Integrative Biology, University of Texas at Austin, Austin, TX, United States
3 School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, United States
DOI
10.7287/peerj.preprints.138v1
Published
Accepted
Subject Areas
Biochemistry, Bioinformatics, Computational Biology, Virology, Infectious Diseases
Keywords
Arenavirus, Machupo, molecular dynamics, protein-protein interaction, computational mutagenesis
Copyright
© 2013 Meyer et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Cite this article
Meyer AG, Sawyer SL, Ellington AD, Wilke CO. 2013. Predicting virus-receptor mutant binding by molecular dynamics simulation. PeerJ PrePrints 1:e138v1

Abstract

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 max imum applied force during a pulling simulation and the area under the force-versus-distance curve. We find 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. 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 method provides an elegant framework to compare the effects of multi ple mutations, individually and jointly, on protein–protein interactions.

Author Comment

This manuscript was submitted for review with PeerJ.

Supplemental Information

Example of the weak binding Y211A mutant being separated from the reduced transferrin receptor.

DOI: 10.7287/peerj.preprints.138v1/supp-1