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Discrete structural features among interface residue-level classes

Overview of attention for article published in BMC Bioinformatics, December 2015
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Title
Discrete structural features among interface residue-level classes
Published in
BMC Bioinformatics, December 2015
DOI 10.1186/1471-2105-16-s18-s8
Pubmed ID
Authors

Gopichandran Sowmya, Shoba Ranganathan

Abstract

Protein-protein interaction (PPI) is essential for molecular functions in biological cells. Investigation on protein interfaces of known complexes is an important step towards deciphering the driving forces of PPIs. Each PPI complex is specific, sensitive and selective to binding. Therefore, we have estimated the relative difference in percentage of polar residues between surface and the interface for each complex in a non-redundant heterodimer dataset of 278 complexes to understand the predominant forces driving binding. Our analysis showed ~60% of protein complexes with surface polarity greater than interface polarity (designated as class A). However, a considerable number of complexes (~40%) have interface polarity greater than surface polarity, (designated as class B), with a significantly different p-value of 1.66E-45 from class A. Comprehensive analyses of protein complexes show that interface features such as interface area, interface polarity abundance, solvation free energy gain upon interface formation, binding energy and the percentage of interface charged residue abundance distinguish among class A and class B complexes, while electrostatic visualization maps also help differentiate interface classes among complexes. Class A complexes are classical with abundant non-polar interactions at the interface; however class B complexes have abundant polar interactions at the interface, similar to protein surface characteristics. Five physicochemical interface features analyzed from the protein heterodimer dataset are discriminatory among the interface residue-level classes. These novel observations find application in developing residue-level models for protein-protein binding prediction, protein-protein docking studies and interface inhibitor design as drugs.

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Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 7%
Unknown 13 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 21%
Student > Bachelor 2 14%
Student > Master 2 14%
Student > Ph. D. Student 1 7%
Other 1 7%
Other 2 14%
Unknown 3 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 29%
Environmental Science 1 7%
Agricultural and Biological Sciences 1 7%
Computer Science 1 7%
Physics and Astronomy 1 7%
Other 2 14%
Unknown 4 29%