Classifying RNA-binding proteins based on electrostatic properties.

PLoS Computational Biology
Shula Shazman, Yael Mandel-Gutfreund

Abstract

Protein structure can provide new insight into the biological function of a protein and can enable the design of better experiments to learn its biological roles. Moreover, deciphering the interactions of a protein with other molecules can contribute to the understanding of the protein's function within cellular processes. In this study, we apply a machine learning approach for classifying RNA-binding proteins based on their three-dimensional structures. The method is based on characterizing unique properties of electrostatic patches on the protein surface. Using an ensemble of general protein features and specific properties extracted from the electrostatic patches, we have trained a support vector machine (SVM) to distinguish RNA-binding proteins from other positively charged proteins that do not bind nucleic acids. Specifically, the method was applied on proteins possessing the RNA recognition motif (RRM) and successfully classified RNA-binding proteins from RRM domains involved in protein-protein interactions. Overall the method achieves 88% accuracy in classifying RNA-binding proteins, yet it cannot distinguish RNA from DNA binding proteins. Nevertheless, by applying a multiclass SVM approach we were able to classify the R...Continue Reading

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Methods Mentioned

BETA
X-ray
NMR

Software Mentioned

RNbind
PyMOL viewer
Gist
NAbind
PISCES
Intervor
PatchFinder
linux
PatchFinderPlus
Protein Dipole Moments Server

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