NDLI: Analysis of relevant physicochemical properties in obligate and non-obligate protein-protein interactions

Content Provider	IEEE Xplore Digital Library
Author	Maleki, M. Aziz, M.M. Rueda, L.
Copyright Year	2011
Description	Author affiliation: School of Computer Science, University of Windsor, 401 Sunset Ave., Windsor, ON, N9B 3P4, Canada (Maleki, M.; Aziz, M.M.; Rueda, L.)
Abstract	Identification and analysis of types of protein-protein interactions (PPI) is an important problem in molecular biology because of its key role in many biological processes in living cells. In this paper, we focus on obligate and non-obligate complexes, their prediction and analysis. We propose a feature selection scheme called $MRMR^{pro}$ which is based on Minimum Redundancy Maximum Relevance (MRMR) to focus on the most discriminative and relevant properties to distinguish between these two types of complexes. Our prediction approach uses desolvation energies of pairs of atoms or amino acids present in the interfaces of such complexes. Our results on two well-known datasets confirm that $MRMR^{pro}$ leads to significant improvements on performance by finding more relevant features for prediction. Furthermore, the prediction performance of our biologically guided feature selection methods demonstrate that hydrophobic amino acids are more discriminating than hydrophilic and amphipathic amino acids to distinguish between obligate and non-obligate complexes.
Starting Page	345
Ending Page	351
File Size	838888
Page Count	7
File Format	PDF
ISBN	9781457716126
e-ISBN	9781457716133
DOI	10.1109/BIBMW.2011.6112397
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Accuracy Heating Amino acids Vectors protein-protein interaction Transient analysis feature selection complex type prediction
Content Type	Text
Resource Type	Article

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