Literature DB >> 24859723

Development and application of site mapping methods for the design of glycosaminoglycans.

Mark Agostino1, Neha S Gandhi1, Ricardo L Mancera2.   

Abstract

Glycosaminoglycans (GAGs) are complex polysaccharides involved in a wide range of biological signaling events, as well as being important as biological structural materials. Despite the ubiquity and importance of GAG-protein interactions in biological systems and potentially as therapeutic targets, detailed structures of such interactions are sparse in availability. Computational methods can provide detailed structural knowledge of these interactions; however, they should be evaluated against suitable test systems prior to their widespread use. In this study, we have investigated the application of automated molecular docking and interaction mapping techniques to characterizing GAG-protein interactions. A series of high-resolution X-ray crystal structures of GAGs in complex with proteins was used to evaluate the approaches. Accurately scoring the pose fitting best with the crystal structure was a challenge for all docking programs evaluated. The site mapping technique offered excellent prediction of the key residues involved in ligand recognition, comparable to the best pose and improved over the top-ranked pose. A design protocol incorporating site- and ligand-based mapping techniques was developed and applied to identify GAGs capable of binding to acidic fibroblast growth factor (aFGF). The protocol was able to identify ligands known to bind to aFGF and accurately able to predict the binding modes of those ligands when using a known ligand-binding conformation of the protein. This study demonstrates the value of mapping-based techniques in identifying specific GAG epitopes recognized by proteins and for GAG-based drug design.
© The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  glycosaminoglycans; interaction analysis; molecular docking; site mapping; virtual screening

Mesh:

Substances:

Year:  2014        PMID: 24859723     DOI: 10.1093/glycob/cwu045

Source DB:  PubMed          Journal:  Glycobiology        ISSN: 0959-6658            Impact factor:   4.313


  7 in total

1.  Toward a robust computational screening strategy for identifying glycosaminoglycan sequences that display high specificity for target proteins.

Authors:  Nehru Viji Sankaranarayanan; Umesh R Desai
Journal:  Glycobiology       Date:  2014-07-21       Impact factor: 4.313

Review 2.  So you think computational approaches to understanding glycosaminoglycan-protein interactions are too dry and too rigid? Think again!

Authors:  Nehru Viji Sankaranarayanan; Balaji Nagarajan; Umesh R Desai
Journal:  Curr Opin Struct Biol       Date:  2018-01-09       Impact factor: 6.809

Review 3.  Sulfated Non-Saccharide Glycosaminoglycan Mimetics as Novel Drug Discovery Platform for Various Pathologies.

Authors:  Daniel K Afosah; Rami A Al-Horani
Journal:  Curr Med Chem       Date:  2020       Impact factor: 4.530

4.  CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma.

Authors:  Heather A Carlson; Richard D Smith; Kelly L Damm-Ganamet; Jeanne A Stuckey; Aqeel Ahmed; Maire A Convery; Donald O Somers; Michael Kranz; Patricia A Elkins; Guanglei Cui; Catherine E Peishoff; Millard H Lambert; James B Dunbar
Journal:  J Chem Inf Model       Date:  2016-05-17       Impact factor: 4.956

5.  A Simple Method for Discovering Druggable, Specific Glycosaminoglycan-Protein Systems. Elucidation of Key Principles from Heparin/Heparan Sulfate-Binding Proteins.

Authors:  Aurijit Sarkar; Umesh R Desai
Journal:  PLoS One       Date:  2015-10-21       Impact factor: 3.240

6.  Computational drill down on FGF1-heparin interactions through methodological evaluation.

Authors:  Sándor Babik; Sergey A Samsonov; M Teresa Pisabarro
Journal:  Glycoconj J       Date:  2016-11-17       Impact factor: 2.916

7.  Wnt Binding Affinity Prediction for Putative Frizzled-Type Cysteine-Rich Domains.

Authors:  Mark Agostino; Sebastian Öther-Gee Pohl
Journal:  Int J Mol Sci       Date:  2019-08-26       Impact factor: 5.923

  7 in total

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