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A Structural Systems Biology Approach for Quantifying the Systemic Consequences of Missense Mutations in Proteins

Overview of attention for article published in PLoS Computational Biology, October 2012
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Title
A Structural Systems Biology Approach for Quantifying the Systemic Consequences of Missense Mutations in Proteins
Published in
PLoS Computational Biology, October 2012
DOI 10.1371/journal.pcbi.1002738
Pubmed ID
Authors

Tammy M. K. Cheng, Lucas Goehring, Linda Jeffery, Yu-En Lu, Jacqueline Hayles, Béla Novák, Paul A. Bates

Abstract

Gauging the systemic effects of non-synonymous single nucleotide polymorphisms (nsSNPs) is an important topic in the pursuit of personalized medicine. However, it is a non-trivial task to understand how a change at the protein structure level eventually affects a cell's behavior. This is because complex information at both the protein and pathway level has to be integrated. Given that the idea of integrating both protein and pathway dynamics to estimate the systemic impact of missense mutations in proteins remains predominantly unexplored, we investigate the practicality of such an approach by formulating mathematical models and comparing them with experimental data to study missense mutations. We present two case studies: (1) interpreting systemic perturbation for mutations within the cell cycle control mechanisms (G2 to mitosis transition) for yeast; (2) phenotypic classification of neuron-related human diseases associated with mutations within the mitogen-activated protein kinase (MAPK) pathway. We show that the application of simplified mathematical models is feasible for understanding the effects of small sequence changes on cellular behavior. Furthermore, we show that the systemic impact of missense mutations can be effectively quantified as a combination of protein stability change and pathway perturbation.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 3%
Germany 1 1%
Malaysia 1 1%
France 1 1%
Colombia 1 1%
Canada 1 1%
United Kingdom 1 1%
Iran, Islamic Republic of 1 1%
United States 1 1%
Other 0 0%
Unknown 62 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 29%
Student > Ph. D. Student 11 15%
Student > Master 10 14%
Professor 6 8%
Student > Bachelor 5 7%
Other 14 19%
Unknown 5 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 28 39%
Biochemistry, Genetics and Molecular Biology 20 28%
Computer Science 5 7%
Chemistry 4 6%
Medicine and Dentistry 2 3%
Other 6 8%
Unknown 7 10%