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Predicting protein–protein interactions using graph invariants and a neural network

Paper ID Volume ID Publish Year Pages File Format Full-Text
15235 1394 2011 6 PDF Available
Title
Predicting protein–protein interactions using graph invariants and a neural network
Abstract

The PDZ domain of proteins mediates a protein–protein interaction by recognizing the hydrophobic C-terminal tail of the target protein. One of the challenges put forth by the DREAM (Discussions on Reverse Engineering Assessment and Methods) 2009 Challenge consists of predicting a position weight matrix (PWM) that describes the specificity profile of five PDZ domains to their target peptides. We consider the primary structures of each of the five PDZ domains as a numerical sequence derived from graph-theoretic models of each of the individual amino acids in the protein sequence. Using available PDZ domain databases to obtain known targets, the graph-theoretic based numerical sequences are then used to train a neural network to recognize their targets. Given the challenge sequences, the target probabilities are computed and a corresponding position weight matrix is derived. In this work we present our method. The results of our method placed second in the DREAM 2009 challenge.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slideHighlights► Graph Theoretic models of amino acids. ► PDZ domains characterized by sequences of graphical and molecular descriptors. ► Machine learning to predict PDZ-domain, ligand binding specificity.

Keywords
Machine learning; Protein–protein interactions; Graph-theoretic model
First Page Preview
Predicting protein–protein interactions using graph invariants and a neural network
Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 35, Issue 2, April 2011, Pages 108–113
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering