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Title: Defining the Specificity of Carbohydrate–Protein Interactions by Quantifying Functional Group Contributions

Abstract

Protein–carbohydrate interactions are significant in a wide range of biological processes, disruption of which has been implicated in many different diseases. The capability of glycan-binding proteins (GBPs) to specifically bind to the corresponding glycans allows GBPs to be utilized in glycan biomarker detection or conversely to serve as targets for therapeutic intervention. However, understanding the structural origins of GBP specificity has proven to be challenging due to their typically low binding affinities (mM) and their potential to display broad or complex specificities. Here we perform molecular dynamics (MD) simulations and post-MD energy analyses with the Poisson–Boltzmann and generalized Born solvent models (MM-PB/GBSA) of the Erythrina cristagalli lectin (ECL) with its known ligands, and with new cocrystal structures reported herein. While each MM-PB/GBSA parametrization resulted in different estimates of the desolvation free energy, general trends emerged that permit us to define GBP binding preferences in terms of ligand substructure specificity. Additionally, we have further decomposed the theoretical interaction energies into contributions made between chemically relevant functional groups. Based on these contributions, the functional groups in each ligand can be assembled into a pharmacophore comprised of groups that are either critical for binding, or enhance binding, or are noninteracting. It is revealedmore » that the pharmacophore for ECL consists of the galactopyranose (Gal) ring atoms along with C6 and the O3 and O4 hydroxyl groups. This approach provides a convenient method for identifying and quantifying the glycan pharmacophore and provides a novel method for interpreting glycan specificity that is independent of residue-level glycan nomenclature. As a result, a pharmacophore approach to defining specificity is readily transferable to molecular design software and, therefore, may be particularly useful in designing therapeutics (glycomimetics) that target GBPs.« less

Authors:
 [1]; ORCiD logo [2];  [1];  [3]; ORCiD logo [2]; ORCiD logo [1]
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  1. Univ. of Georgia, Athens, GA (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  3. Russian Academy of Sciences, Moscow (Russian Federation)
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1509590
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Chemical Information and Modeling
Additional Journal Information:
Journal Volume: 58; Journal Issue: 9; Journal ID: ISSN 1549-9596
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats

Sood, Amika, Gerlits, Oksana O., Ji, Ye, Bovin, Nicolai V., Coates, Leighton, and Woods, Robert J. Defining the Specificity of Carbohydrate–Protein Interactions by Quantifying Functional Group Contributions. United States: N. p., 2018. Web. doi:10.1021/acs.jcim.8b00120.
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Sood, Amika, Gerlits, Oksana O., Ji, Ye, Bovin, Nicolai V., Coates, Leighton, & Woods, Robert J. Defining the Specificity of Carbohydrate–Protein Interactions by Quantifying Functional Group Contributions. United States. https://doi.org/10.1021/acs.jcim.8b00120
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Sood, Amika, Gerlits, Oksana O., Ji, Ye, Bovin, Nicolai V., Coates, Leighton, and Woods, Robert J. Tue . "Defining the Specificity of Carbohydrate–Protein Interactions by Quantifying Functional Group Contributions". United States. https://doi.org/10.1021/acs.jcim.8b00120. https://www.osti.gov/servlets/purl/1509590.
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@article{osti_1509590,
title = {Defining the Specificity of Carbohydrate–Protein Interactions by Quantifying Functional Group Contributions},
author = {Sood, Amika and Gerlits, Oksana O. and Ji, Ye and Bovin, Nicolai V. and Coates, Leighton and Woods, Robert J.},
abstractNote = {Protein–carbohydrate interactions are significant in a wide range of biological processes, disruption of which has been implicated in many different diseases. The capability of glycan-binding proteins (GBPs) to specifically bind to the corresponding glycans allows GBPs to be utilized in glycan biomarker detection or conversely to serve as targets for therapeutic intervention. However, understanding the structural origins of GBP specificity has proven to be challenging due to their typically low binding affinities (mM) and their potential to display broad or complex specificities. Here we perform molecular dynamics (MD) simulations and post-MD energy analyses with the Poisson–Boltzmann and generalized Born solvent models (MM-PB/GBSA) of the Erythrina cristagalli lectin (ECL) with its known ligands, and with new cocrystal structures reported herein. While each MM-PB/GBSA parametrization resulted in different estimates of the desolvation free energy, general trends emerged that permit us to define GBP binding preferences in terms of ligand substructure specificity. Additionally, we have further decomposed the theoretical interaction energies into contributions made between chemically relevant functional groups. Based on these contributions, the functional groups in each ligand can be assembled into a pharmacophore comprised of groups that are either critical for binding, or enhance binding, or are noninteracting. It is revealed that the pharmacophore for ECL consists of the galactopyranose (Gal) ring atoms along with C6 and the O3 and O4 hydroxyl groups. This approach provides a convenient method for identifying and quantifying the glycan pharmacophore and provides a novel method for interpreting glycan specificity that is independent of residue-level glycan nomenclature. As a result, a pharmacophore approach to defining specificity is readily transferable to molecular design software and, therefore, may be particularly useful in designing therapeutics (glycomimetics) that target GBPs.},
doi = {10.1021/acs.jcim.8b00120},
journal = {Journal of Chemical Information and Modeling},
number = 9,
volume = 58,
place = {United States},
year = {Tue Aug 07 00:00:00 EDT 2018},
month = {Tue Aug 07 00:00:00 EDT 2018}
}
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