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Protein Meta-Functional Signatures from Combining Sequence, Structure, Evolution, and Amino Acid Property Information

Overview of attention for article published in PLoS Computational Biology, September 2008
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Title
Protein Meta-Functional Signatures from Combining Sequence, Structure, Evolution, and Amino Acid Property Information
Published in
PLoS Computational Biology, September 2008
DOI 10.1371/journal.pcbi.1000181
Pubmed ID
Authors

Kai Wang, Jeremy A. Horst, Gong Cheng, David C. Nickle, Ram Samudrala

Abstract

Protein function is mediated by different amino acid residues, both their positions and types, in a protein sequence. Some amino acids are responsible for the stability or overall shape of the protein, playing an indirect role in protein function. Others play a functionally important role as part of active or binding sites of the protein. For a given protein sequence, the residues and their degree of functional importance can be thought of as a signature representing the function of the protein. We have developed a combination of knowledge- and biophysics-based function prediction approaches to elucidate the relationships between the structural and the functional roles of individual residues and positions. Such a meta-functional signature (MFS), which is a collection of continuous values representing the functional significance of each residue in a protein, may be used to study proteins of known function in greater detail and to aid in experimental characterization of proteins of unknown function. We demonstrate the superior performance of MFS in predicting protein functional sites and also present four real-world examples to apply MFS in a wide range of settings to elucidate protein sequence-structure-function relationships. Our results indicate that the MFS approach, which can combine multiple sources of information and also give biological interpretation to each component, greatly facilitates the understanding and characterization of protein function.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 3%
Portugal 1 2%
Germany 1 2%
Australia 1 2%
Switzerland 1 2%
India 1 2%
United States 1 2%
Unknown 51 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 34%
Researcher 14 24%
Professor > Associate Professor 7 12%
Other 5 8%
Professor 3 5%
Other 8 14%
Unknown 2 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 33 56%
Biochemistry, Genetics and Molecular Biology 10 17%
Computer Science 7 12%
Medicine and Dentistry 3 5%
Chemistry 2 3%
Other 2 3%
Unknown 2 3%