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Investigation of transmembrane proteins using a computational approach

BACKGROUND: An important subfamily of membrane proteins are the transmembrane α-helical proteins, in which the membrane-spanning regions are made up of α-helices. Given the obvious biological and medical significance of these proteins, it is of tremendous practical importance to identify the locatio...

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Autores principales: Yang, Jack Y, Yang, Mary Qu, Dunker, A Keith, Deng, Youping, Huang, Xudong
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2386072/
https://www.ncbi.nlm.nih.gov/pubmed/18366620
http://dx.doi.org/10.1186/1471-2164-9-S1-S7
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author Yang, Jack Y
Yang, Mary Qu
Dunker, A Keith
Deng, Youping
Huang, Xudong
author_facet Yang, Jack Y
Yang, Mary Qu
Dunker, A Keith
Deng, Youping
Huang, Xudong
author_sort Yang, Jack Y
collection PubMed
description BACKGROUND: An important subfamily of membrane proteins are the transmembrane α-helical proteins, in which the membrane-spanning regions are made up of α-helices. Given the obvious biological and medical significance of these proteins, it is of tremendous practical importance to identify the location of transmembrane segments. The difficulty of inferring the secondary or tertiary structure of transmembrane proteins using experimental techniques has led to a surge of interest in applying techniques from machine learning and bioinformatics to infer secondary structure from primary structure in these proteins. We are therefore interested in determining which physicochemical properties are most useful for discriminating transmembrane segments from non-transmembrane segments in transmembrane proteins, and for discriminating intrinsically unstructured segments from intrinsically structured segments in transmembrane proteins, and in using the results of these investigations to develop classifiers to identify transmembrane segments in transmembrane proteins. RESULTS: We determined that the most useful properties for discriminating transmembrane segments from non-transmembrane segments and for discriminating intrinsically unstructured segments from intrinsically structured segments in transmembrane proteins were hydropathy, polarity, and flexibility, and used the results of this analysis to construct classifiers to discriminate transmembrane segments from non-transmembrane segments using four classification techniques: two variants of the Self-Organizing Global Ranking algorithm, a decision tree algorithm, and a support vector machine algorithm. All four techniques exhibited good performance, with out-of-sample accuracies of approximately 75%. CONCLUSIONS: Several interesting observations emerged from our study: intrinsically unstructured segments and transmembrane segments tend to have opposite properties; transmembrane proteins appear to be much richer in intrinsically unstructured segments than other proteins; and, in approximately 70% of transmembrane proteins that contain intrinsically unstructured segments, the intrinsically unstructured segments are close to transmembrane segments.
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spelling pubmed-23860722008-05-15 Investigation of transmembrane proteins using a computational approach Yang, Jack Y Yang, Mary Qu Dunker, A Keith Deng, Youping Huang, Xudong BMC Genomics Research BACKGROUND: An important subfamily of membrane proteins are the transmembrane α-helical proteins, in which the membrane-spanning regions are made up of α-helices. Given the obvious biological and medical significance of these proteins, it is of tremendous practical importance to identify the location of transmembrane segments. The difficulty of inferring the secondary or tertiary structure of transmembrane proteins using experimental techniques has led to a surge of interest in applying techniques from machine learning and bioinformatics to infer secondary structure from primary structure in these proteins. We are therefore interested in determining which physicochemical properties are most useful for discriminating transmembrane segments from non-transmembrane segments in transmembrane proteins, and for discriminating intrinsically unstructured segments from intrinsically structured segments in transmembrane proteins, and in using the results of these investigations to develop classifiers to identify transmembrane segments in transmembrane proteins. RESULTS: We determined that the most useful properties for discriminating transmembrane segments from non-transmembrane segments and for discriminating intrinsically unstructured segments from intrinsically structured segments in transmembrane proteins were hydropathy, polarity, and flexibility, and used the results of this analysis to construct classifiers to discriminate transmembrane segments from non-transmembrane segments using four classification techniques: two variants of the Self-Organizing Global Ranking algorithm, a decision tree algorithm, and a support vector machine algorithm. All four techniques exhibited good performance, with out-of-sample accuracies of approximately 75%. CONCLUSIONS: Several interesting observations emerged from our study: intrinsically unstructured segments and transmembrane segments tend to have opposite properties; transmembrane proteins appear to be much richer in intrinsically unstructured segments than other proteins; and, in approximately 70% of transmembrane proteins that contain intrinsically unstructured segments, the intrinsically unstructured segments are close to transmembrane segments. BioMed Central 2008-03-20 /pmc/articles/PMC2386072/ /pubmed/18366620 http://dx.doi.org/10.1186/1471-2164-9-S1-S7 Text en Copyright © 2008 Yang et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Yang, Jack Y
Yang, Mary Qu
Dunker, A Keith
Deng, Youping
Huang, Xudong
Investigation of transmembrane proteins using a computational approach
title Investigation of transmembrane proteins using a computational approach
title_full Investigation of transmembrane proteins using a computational approach
title_fullStr Investigation of transmembrane proteins using a computational approach
title_full_unstemmed Investigation of transmembrane proteins using a computational approach
title_short Investigation of transmembrane proteins using a computational approach
title_sort investigation of transmembrane proteins using a computational approach
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2386072/
https://www.ncbi.nlm.nih.gov/pubmed/18366620
http://dx.doi.org/10.1186/1471-2164-9-S1-S7
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