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Random Field Model Reveals Structure of the Protein Recombinational Landscape

Overview of attention for article published in PLoS Computational Biology, October 2012
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Title
Random Field Model Reveals Structure of the Protein Recombinational Landscape
Published in
PLoS Computational Biology, October 2012
DOI 10.1371/journal.pcbi.1002713
Pubmed ID
Authors

Philip A. Romero, Frances H. Arnold

Abstract

We are interested in how intragenic recombination contributes to the evolution of proteins and how this mechanism complements and enhances the diversity generated by random mutation. Experiments have revealed that proteins are highly tolerant to recombination with homologous sequences (mutation by recombination is conservative); more surprisingly, they have also shown that homologous sequence fragments make largely additive contributions to biophysical properties such as stability. Here, we develop a random field model to describe the statistical features of the subset of protein space accessible by recombination, which we refer to as the recombinational landscape. This model shows quantitative agreement with experimental results compiled from eight libraries of proteins that were generated by recombining gene fragments from homologous proteins. The model reveals a recombinational landscape that is highly enriched in functional sequences, with properties dominated by a large-scale additive structure. It also quantifies the relative contributions of parent sequence identity, crossover locations, and protein fold to the tolerance of proteins to recombination. Intragenic recombination explores a unique subset of sequence space that promotes rapid molecular diversification and functional adaptation.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 5%
Israel 1 1%
India 1 1%
Canada 1 1%
Unknown 70 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 30%
Researcher 19 25%
Student > Doctoral Student 5 6%
Student > Master 5 6%
Student > Bachelor 4 5%
Other 10 13%
Unknown 11 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 39%
Biochemistry, Genetics and Molecular Biology 14 18%
Chemistry 9 12%
Physics and Astronomy 3 4%
Engineering 3 4%
Other 7 9%
Unknown 11 14%