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A Computational Approach for Deciphering the Organization of Glycosaminoglycans
BACKGROUND: Increasing evidence has revealed important roles for complex glycans as mediators of normal and pathological processes. Glycosaminoglycans are a class of glycans that bind and regulate the function of a wide array of proteins at the cell-extracellular matrix interface. The specific seque...
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Formato: | Texto |
Lenguaje: | English |
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Public Library of Science
2010
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2826411/ https://www.ncbi.nlm.nih.gov/pubmed/20186334 http://dx.doi.org/10.1371/journal.pone.0009389 |
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author | Spencer, Jean L. Bernanke, Joel A. Buczek-Thomas, Jo Ann Nugent, Matthew A. |
author_facet | Spencer, Jean L. Bernanke, Joel A. Buczek-Thomas, Jo Ann Nugent, Matthew A. |
author_sort | Spencer, Jean L. |
collection | PubMed |
description | BACKGROUND: Increasing evidence has revealed important roles for complex glycans as mediators of normal and pathological processes. Glycosaminoglycans are a class of glycans that bind and regulate the function of a wide array of proteins at the cell-extracellular matrix interface. The specific sequence and chemical organization of these polymers likely define function; however, identification of the structure-function relationships of glycosaminoglycans has been met with challenges associated with the unique level of complexity and the nontemplate-driven biosynthesis of these biopolymers. METHODOLOGY/PRINCIPAL FINDINGS: To address these challenges, we have devised a computational approach to predict fine structure and patterns of domain organization of the specific glycosaminoglycan, heparan sulfate (HS). Using chemical composition data obtained after complete and partial digestion of mixtures of HS chains with specific degradative enzymes, the computational analysis produces populations of theoretical HS chains with structures that meet both biosynthesis and enzyme degradation rules. The model performs these operations through a modular format consisting of input/output sections and three routines called chainmaker, chainbreaker, and chainsorter. We applied this methodology to analyze HS preparations isolated from pulmonary fibroblasts and epithelial cells. Significant differences in the general organization of these two HS preparations were observed, with HS from epithelial cells having a greater frequency of highly sulfated domains. Epithelial HS also showed a higher density of specific HS domains that have been associated with inhibition of neutrophil elastase. Experimental analysis of elastase inhibition was consistent with the model predictions and demonstrated that HS from epithelial cells had greater inhibitory activity than HS from fibroblasts. CONCLUSIONS/SIGNIFICANCE: This model establishes the conceptual framework for a new class of computational tools to use to assess patterns of domain organization within glycosaminoglycans. These tools will provide a means to consider high-level chain organization in deciphering the structure-function relationships of polysaccharides in biology. |
format | Text |
id | pubmed-2826411 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-28264112010-02-26 A Computational Approach for Deciphering the Organization of Glycosaminoglycans Spencer, Jean L. Bernanke, Joel A. Buczek-Thomas, Jo Ann Nugent, Matthew A. PLoS One Research Article BACKGROUND: Increasing evidence has revealed important roles for complex glycans as mediators of normal and pathological processes. Glycosaminoglycans are a class of glycans that bind and regulate the function of a wide array of proteins at the cell-extracellular matrix interface. The specific sequence and chemical organization of these polymers likely define function; however, identification of the structure-function relationships of glycosaminoglycans has been met with challenges associated with the unique level of complexity and the nontemplate-driven biosynthesis of these biopolymers. METHODOLOGY/PRINCIPAL FINDINGS: To address these challenges, we have devised a computational approach to predict fine structure and patterns of domain organization of the specific glycosaminoglycan, heparan sulfate (HS). Using chemical composition data obtained after complete and partial digestion of mixtures of HS chains with specific degradative enzymes, the computational analysis produces populations of theoretical HS chains with structures that meet both biosynthesis and enzyme degradation rules. The model performs these operations through a modular format consisting of input/output sections and three routines called chainmaker, chainbreaker, and chainsorter. We applied this methodology to analyze HS preparations isolated from pulmonary fibroblasts and epithelial cells. Significant differences in the general organization of these two HS preparations were observed, with HS from epithelial cells having a greater frequency of highly sulfated domains. Epithelial HS also showed a higher density of specific HS domains that have been associated with inhibition of neutrophil elastase. Experimental analysis of elastase inhibition was consistent with the model predictions and demonstrated that HS from epithelial cells had greater inhibitory activity than HS from fibroblasts. CONCLUSIONS/SIGNIFICANCE: This model establishes the conceptual framework for a new class of computational tools to use to assess patterns of domain organization within glycosaminoglycans. These tools will provide a means to consider high-level chain organization in deciphering the structure-function relationships of polysaccharides in biology. Public Library of Science 2010-02-23 /pmc/articles/PMC2826411/ /pubmed/20186334 http://dx.doi.org/10.1371/journal.pone.0009389 Text en Spencer et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Spencer, Jean L. Bernanke, Joel A. Buczek-Thomas, Jo Ann Nugent, Matthew A. A Computational Approach for Deciphering the Organization of Glycosaminoglycans |
title | A Computational Approach for Deciphering the Organization of Glycosaminoglycans |
title_full | A Computational Approach for Deciphering the Organization of Glycosaminoglycans |
title_fullStr | A Computational Approach for Deciphering the Organization of Glycosaminoglycans |
title_full_unstemmed | A Computational Approach for Deciphering the Organization of Glycosaminoglycans |
title_short | A Computational Approach for Deciphering the Organization of Glycosaminoglycans |
title_sort | computational approach for deciphering the organization of glycosaminoglycans |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2826411/ https://www.ncbi.nlm.nih.gov/pubmed/20186334 http://dx.doi.org/10.1371/journal.pone.0009389 |
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