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The linear interaction energy method for the prediction of protein stability changes upon mutation

Authors

  • Lauren Wickstrom,

    1. Department of Chemistry and Chemical Biology, BioMaPS Institute for Quantitative Biology, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854
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  • Emilio Gallicchio,

    1. Department of Chemistry and Chemical Biology, BioMaPS Institute for Quantitative Biology, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854
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  • Ronald M. Levy

    Corresponding author
    1. Department of Chemistry and Chemical Biology, BioMaPS Institute for Quantitative Biology, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854
    • BioMaPS Institute for Quantitative Biology, 610 Taylor Road, The State University of New Jersey, Piscataway, NJ 08854
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Abstract

The coupling of protein energetics and sequence changes is a critical aspect of computational protein design, as well as for the understanding of protein evolution, human disease, and drug resistance. To study the molecular basis for this coupling, computational tools must be sufficiently accurate and computationally inexpensive enough to handle large amounts of sequence data. We have developed a computational approach based on the linear interaction energy (LIE) approximation to predict the changes in the free-energy of the native state induced by a single mutation. This approach was applied to a set of 822 mutations in 10 proteins which resulted in an average unsigned error of 0.82 kcal/mol and a correlation coefficient of 0.72 between the calculated and experimental ΔΔG values. The method is able to accurately identify destabilizing hot spot mutations; however, it has difficulty in distinguishing between stabilizing and destabilizing mutations because of the distribution of stability changes for the set of mutations used to parameterize the model. In addition, the model also performs quite well in initial tests on a small set of double mutations. On the basis of these promising results, we can begin to examine the relationship between protein stability and fitness, correlated mutations, and drug resistance. Proteins 2012; © 2011 Wiley Periodicals, Inc.

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