Prediction of protein interaction hot spots using rough set-based multiple criteria linear programming.

Chen, R; Zhang, Z; Wu, D; Zhang, P; Zhang, X; Wang, Y; Shi, Y

Prediction of protein interaction hot spots using rough set-based multiple criteria linear programming.

Chen, R Zhang, Z Wu, D Zhang, P

Zhang, X Wang, Y Shi, Y

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Publication Type:: Journal Article
Citation:: J Theor Biol, 2011, 269 (1), pp. 174 - 180
Issue Date:: 2011-01-21

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Field	Value	Language
dc.contributor.author	Chen, R	en_US
dc.contributor.author	Zhang, Z	en_US
dc.contributor.author	Wu, D	en_US
dc.contributor.author	Zhang, P https://orcid.org/0000-0001-7973-2746	en_US
dc.contributor.author	Zhang, X	en_US
dc.contributor.author	Wang, Y	en_US
dc.contributor.author	Shi, Y	en_US
dc.date.available	2010-10-16	en_US
dc.date.issued	2011-01-21	en_US
dc.identifier.citation	J Theor Biol, 2011, 269 (1), pp. 174 - 180	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28898
dc.description.abstract	Protein-protein interactions are fundamentally important in many biological processes and it is in pressing need to understand the principles of protein-protein interactions. Mutagenesis studies have found that only a small fraction of surface residues, known as hot spots, are responsible for the physical binding in protein complexes. However, revealing hot spots by mutagenesis experiments are usually time consuming and expensive. In order to complement the experimental efforts, we propose a new computational approach in this paper to predict hot spots. Our method, Rough Set-based Multiple Criteria Linear Programming (RS-MCLP), integrates rough sets theory and multiple criteria linear programming to choose dominant features and computationally predict hot spots. Our approach is benchmarked by a dataset of 904 alanine-mutated residues and the results show that our RS-MCLP method performs better than other methods, e.g., MCLP, Decision Tree, Bayes Net, and the existing HotSprint database. In addition, we reveal several biological insights based on our analysis. We find that four features (the change of accessible surface area, percentage of the change of accessible surface area, size of a residue, and atomic contacts) are critical in predicting hot spots. Furthermore, we find that three residues (Tyr, Trp, and Phe) are abundant in hot spots through analyzing the distribution of amino acids.	en_US
dc.language	eng	en_US
dc.relation.ispartof	J Theor Biol	en_US
dc.relation.isbasedon	10.1016/j.jtbi.2010.10.021	en_US
dc.subject.classification	Evolutionary Biology	en_US
dc.subject.mesh	Animals	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Amino Acids	en_US
dc.subject.mesh	Bayes Theorem	en_US
dc.subject.mesh	Protein Interaction Mapping	en_US
dc.subject.mesh	Protein Binding	en_US
dc.subject.mesh	Mutation	en_US
dc.subject.mesh	Decision Trees	en_US
dc.subject.mesh	Databases, Protein	en_US
dc.subject.mesh	Programming, Linear	en_US
dc.title	Prediction of protein interaction hot spots using rough set-based multiple criteria linear programming.	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	269	en_US
utslib.location.activity	England	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	06 Biological Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	269	en_US

Abstract:

Protein-protein interactions are fundamentally important in many biological processes and it is in pressing need to understand the principles of protein-protein interactions. Mutagenesis studies have found that only a small fraction of surface residues, known as hot spots, are responsible for the physical binding in protein complexes. However, revealing hot spots by mutagenesis experiments are usually time consuming and expensive. In order to complement the experimental efforts, we propose a new computational approach in this paper to predict hot spots. Our method, Rough Set-based Multiple Criteria Linear Programming (RS-MCLP), integrates rough sets theory and multiple criteria linear programming to choose dominant features and computationally predict hot spots. Our approach is benchmarked by a dataset of 904 alanine-mutated residues and the results show that our RS-MCLP method performs better than other methods, e.g., MCLP, Decision Tree, Bayes Net, and the existing HotSprint database. In addition, we reveal several biological insights based on our analysis. We find that four features (the change of accessible surface area, percentage of the change of accessible surface area, size of a residue, and atomic contacts) are critical in predicting hot spots. Furthermore, we find that three residues (Tyr, Trp, and Phe) are abundant in hot spots through analyzing the distribution of amino acids.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/28898