Perspective on computational simulations of glycosaminoglycans

Abstract

Glycosaminoglycans (GAGs) represent a formidable frontier for chemists, biochemists, biologists, medicinal chemists, and drug delivery specialists because of massive structural complexity. GAGs are arguably the most complex, natural linear biopolymers with theoretical diversity orders of magnitude higher than proteins and nucleic acids. Yet, this diversity remains generally untapped. Computational approaches offer major routes to understand GAG structure and dynamics so as to enable novel applications of these biopolymers. In fact, computational algorithms, softwares, online tools, and techniques have reached a level of sophistication that help understand atomistic details of conformational variation and protein recognition of individual GAG sequences. This review describes current approaches and challenges in computational study of GAGs. It presents a history of major findings since the earliest mention of GAGs (the 1960s), the development of parameters and force fields specific for GAGs, and the application of these tools in understanding GAG structure–function relationship. This review also presents a section on how to perform simulation of GAGs, which is directed toward researchers interested in entering this promising field with potential to impact therapy. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Structure and Mechanism > Molecular Structures

The Development of Hyaluronan/Fucoidan-Based Nanoparticles as Macrophages Targeting an Epigallocatechin-3-Gallate Delivery System

“…One of these is CD44, and it has been observed using murine models that the recruitment of macrophages and neutrophils into sites of inflammation is CD44-dependent [ 37 , 38 , 39 ]. Hyaluronan is a linear glycosaminoglycan that is formed by alternating disaccharide units of repeating (1→3)-β- d -GlcNAc and (1→4)-β- d -GlcA residues [ 40 , 41 ]. Some studies have shown that hyaluronan of different molecular weights can be covalently attached to the surface of nanoparticles to engage the activity of targeted hyaluronan receptors (CD44 and CD168), which are used in various carcinoma cells (e.g., ovarian, colon, and stomach) or innate immune cells (e.g., macrophages) [ 42 , 43 , 44 ].…”

A Bittersweet Computational Journey among Glycosaminoglycans

“…A growing amount of work is mainly devoted to the comprehension of the dynamic behavior of GAGs (which are characterized by an ensemble of conformations rather than a single secondary or tertiary structure). Additionally, there is an increased focus on the characterization of the conformational changes occurring in GAGs and proteins following their mutual interaction [ 9 ].…”

“…Among the computational methods that can boost the understanding of how GAGs/PGs bind to proteins, particularly promising are molecular modeling, docking and molecular dynamics (MD) simulations. In effect, by working in a virtual environment, these methodologies benefit from a high resilience and potential for high throughput [ 7 , 8 , 9 , 10 ]. Briefly, molecular modeling uses molecular mechanics models to construct three-dimensional molecular structures; molecular docking gives favorable arrangements of molecules in complexes (e.g., GAG/protein complexes); MD simulations reproduce the dynamic behavior of individual molecules or complexes.…”

“…Many reviews have been published on GAGs/PGs [ 13 , 14 , 15 , 16 ] and on the latest developments in computational studies on GAGs/PGs [ 7 , 8 , 9 ]. In this review, we attempt to bridge these two fields of research that have so far been kept apart by conceptual and technical differences, meaning that computational approaches have not yet been fully exploited for studying GAGs/PGs.…”

Structural Insights Into How Proteoglycans Determine Chemokine-CXCR1/CXCR2 Interactions: Progress and Challenges

“…However, in principle, their interactions can be characterized in silico. In recent years, much effort has gone into this arena, including developing energy functions, MD and docking programs, and user-friendly software that can be accessed by the GAG community at large (107)(108)(109)(110)(111)(112). Promising variants from this exercise could then be tested by experimental methods.…”