Modeling large protein-glycosaminoglycan complexes using a fragment‐based approach

Sergey Samsonov , Martin Zacharias , Isaure Chauvot de Beauchene


Glycosaminoglycans (GAGs), a major constituent of the extracellular matrix, participate in cell-signaling by binding specific proteins. Structural data on protein–GAG interactions are crucial to understand and modulate these signaling processes, with potential applications in regenerative medicine. However, experimental and theoretical approaches used to study GAG–protein systems are challenged by GAGs high flexibility limiting the conformational sampling above a certain size, and by the scarcity of GAG-specific docking tools compared to protein–protein or protein–drug docking approaches. We present for the first time an automated fragment-based method for docking GAGs on a protein binding site. In this approach, trimeric GAG fragments are flexibly docked to the protein, assembled based on their spacial overlap, and refined by molecular dynamics. The method appeared more successful than the classical full-ligand approach for most of 13 tested complexes with known structure. The approach is particularly promising for docking of long GAG chains, which represents a bottleneck for classical docking approaches applied to these systems.
Author Sergey Samsonov (FCh / DTCh / LMM)
Sergey Samsonov,,
- Laboratory of Molecular Modeling
, Martin Zacharias - [Technical University of Munich]
Martin Zacharias,,
, Isaure Chauvot de Beauchene - [Université de Lorraine]
Isaure Chauvot de Beauchene,,
Journal seriesJournal of Computational Chemistry, ISSN 0192-8651, [1096-987X], (N/A 100 pkt)
Issue year2019
Publication size in sheets0.5
ASJC Classification1600 General Chemistry; 2605 Computational Mathematics
Languageen angielski
LicenseRepository; author's final; Uznanie Autorstwa - Na Tych Samych Warunkach (CC-BY-SA); after publication
Samsonov_Sergey_Modeling_large_protein-glycosaminoglycan_complexes_using_a_fragment-based_approach_2019.pdf 143.31 KB
Score (nominal)100
Score sourcejournalList
ScoreMinisterial score = 100.0, 10-01-2020, ArticleFromJournal
Publication indicators WoS Citations = 0; Scopus Citations = 0; Scopus SNIP (Source Normalised Impact per Paper): 2018 = 1.004; WoS Impact Factor: 2018 = 3.194 (2) - 2018=3.636 (5)
Citation count*1 (2020-01-02)
Additional fields
LicencjaUtwór jest udostępniany na licencji Creative Commons Uznanie autorstwa-Na tych samych warunkach 4.0 Międzynarodowe (CC BY-SA 4.0)
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