Artificial Neural Network for the Prediction of Tyrosine-Based Sorting Signal Recognition by Adaptor Complexes.
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 a...
| Published in: | Journal of Biomedicine & Biotechnology Vol. 2012; pp. 1 - 10 |
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| Main Authors: | , , |
| Format: | equations & formulas pictorial research tables/charts Journal Article |
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Wiley-Blackwell
2012
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104298137&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104298137 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11107243 137K jtl: Journal of Biomedicine & Biotechnology issn: 11107243 maglogo: N pubinfo: dt: 2012 vid: 2012 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104298137 104298137 2011907059 NLM22505811 PMC3312419 104298137 ppf: 1 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Artificial Neural Network for the Prediction of Tyrosine-Based Sorting Signal Recognition by Adaptor Complexes. aug: au: Mukherjee, Debarati Hanna, Claudia B. Aguilar, R. Claudio affil: 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 sug: subj: Neural Networks (Computer) Tyrosine Physiology Signal Transduction Physiology Membrane Proteins Metabolism Membrane Proteins Physiology Tyrosine Metabolism Bioinformatics Methods Amino Acids Physiology Cell Physiology Models, Statistical Peptides Physiology Proteins Physiology Genetic Techniques Molecular Probe Techniques Yeasts Physiology Reproducibility of Results Human Funding Source ab: 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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