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Journal of Biomedicine and Biotechnology
Volume 2012, Article ID 498031, 9 pages
http://dx.doi.org/10.1155/2012/498031
Research Article

Artificial Neural Network for the Prediction of Tyrosine-Based Sorting Signal Recognition by Adaptor Complexes

Department of Biological Sciences, Purdue Center for Cancer Research and Center for Science of Information, 201 S University Street, Hansen Life Sciences Building, West Lafayette, IN 47907, USA

Received 12 July 2011; Accepted 3 November 2011

Academic Editor: Alejandro Giorgetti

Copyright © 2012 Debarati Mukherjee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Sorting of transmembrane proteins to various intracellular compartments depends on specific signals present within their cytosolic domains. Among these sorting signals, the tyrosine-based motif (YXXØ) is one of the best characterized and is recognized by 𝜇-subunits of the four clathrin-associated adaptor complexes (AP-1 to AP-4). Despite their overlap in specificity, each 𝜇-subunit has a distinct sequence preference dependent on the nature of the X-residues. Moreover, combinations of these residues exert cooperative or inhibitory effects towards interaction with the various APs. This complexity makes it impossible to predict a priori, the specificity of a given tyrosine-signal for a particular 𝜇-subunit. Here, we describe the results obtained with a computational approach based on the Artificial Neural Network (ANN) paradigm that addresses the issue of tyrosine-signal specificity, enabling the prediction of YXXØ-𝜇 interactions with accuracies over 90%. Therefore, this approach constitutes a powerful tool to help predict mechanisms of intracellular protein sorting.