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Inferring Stabilizing Mutations from Protein Phylogenies: Application to Influenza Hemagglutinin

Overview of attention for article published in PLoS Computational Biology, April 2009
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Title
Inferring Stabilizing Mutations from Protein Phylogenies: Application to Influenza Hemagglutinin
Published in
PLoS Computational Biology, April 2009
DOI 10.1371/journal.pcbi.1000349
Pubmed ID
Authors

Jesse D. Bloom, Matthew J. Glassman

Abstract

One selection pressure shaping sequence evolution is the requirement that a protein fold with sufficient stability to perform its biological functions. We present a conceptual framework that explains how this requirement causes the probability that a particular amino acid mutation is fixed during evolution to depend on its effect on protein stability. We mathematically formalize this framework to develop a Bayesian approach for inferring the stability effects of individual mutations from homologous protein sequences of known phylogeny. This approach is able to predict published experimentally measured mutational stability effects (DeltaDeltaG values) with an accuracy that exceeds both a state-of-the-art physicochemical modeling program and the sequence-based consensus approach. As a further test, we use our phylogenetic inference approach to predict stabilizing mutations to influenza hemagglutinin. We introduce these mutations into a temperature-sensitive influenza virus with a defect in its hemagglutinin gene and experimentally demonstrate that some of the mutations allow the virus to grow at higher temperatures. Our work therefore describes a powerful new approach for predicting stabilizing mutations that can be successfully applied even to large, complex proteins such as hemagglutinin. This approach also makes a mathematical link between phylogenetics and experimentally measurable protein properties, potentially paving the way for more accurate analyses of molecular evolution.

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Mendeley readers

The data shown below were compiled from readership statistics for 120 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
Germany 2 2%
Canada 2 2%
United Kingdom 2 2%
Kenya 1 <1%
Finland 1 <1%
Netherlands 1 <1%
Denmark 1 <1%
Czechia 1 <1%
Other 0 0%
Unknown 106 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 38 32%
Researcher 36 30%
Professor 14 12%
Student > Master 9 8%
Professor > Associate Professor 5 4%
Other 12 10%
Unknown 6 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 61 51%
Biochemistry, Genetics and Molecular Biology 25 21%
Physics and Astronomy 7 6%
Chemistry 7 6%
Chemical Engineering 2 2%
Other 7 6%
Unknown 11 9%