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

Sergey Samsonov , Martin Zacharias , Isaure Chauvot de Beauchene

Abstract

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
Martin Zacharias,,
-
, Isaure Chauvot de Beauchene
Isaure Chauvot de Beauchene,,
-
Journal seriesJournal of Computational Chemistry, ISSN 0192-8651, [1096-987X], (A 35 pkt)
Issue year2019
Vol40
No14
Pages1429-1439
Publication size in sheets0.5
ASJC Classification2605 Computational Mathematics; 1600 General Chemistry
DOIDOI:10.1002/jcc.25797
URL https://doi.org/10.1002/jcc.25797
Languageen angielski
Score (nominal)35
ScoreMinisterial score = 35.0, 16-07-2019, ArticleFromJournal
Ministerial score (2013-2016) = 35.0, 16-07-2019, ArticleFromJournal
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2016 = 1.297; WoS Impact Factor: 2017 = 3.221 (2) - 2017=4.161 (5)
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