Constructing 3-Dimensional Atomic-Resolution Models of Nonsulfated Glycosaminoglycans with Arbitrary Lengths Using Conformations from Molecular Dynamics

Abstract

Glycosaminoglycans (GAGs) are the linear carbohydrate components of proteoglycans (PGs) and are key mediators in the bioactivity of PGs in animal tissue. GAGs are heterogeneous, conformationally complex, and polydisperse, containing up to 200 monosaccharide units. These complexities make studying GAG conformation a challenge for existing experimental and computational methods. We previously described an algorithm we developed that applies conformational parameters (i.e., all bond lengths, bond angles, and dihedral angles) from molecular dynamics (MD) simulations of nonsulfated chondroitin GAG 20-mers to construct 3-D atomic-resolution models of nonsulfated chondroitin GAGs of arbitrary length. In the current study, we applied our algorithm to other GAGs, including hyaluronan and nonsulfated forms of dermatan, keratan, and heparan and expanded our database of MD-generated GAG conformations. Here, we show that individual glycosidic linkages and monosaccharide rings in 10- and 20-mers of hyaluronan and nonsulfated dermatan, keratan, and heparan behave randomly and independently in MD simulation and, therefore, using a database of MD-generated 20-mer conformations, that our algorithm can construct conformational ensembles of 10- and 20-mers of various GAG types that accurately represent the backbone flexibility seen in MD simulations. Furthermore, our algorithm efficiently constructs conformational ensembles of GAG 200-mers that we would reasonably expect from MD simulations.

NMR Characterization of the Interactions Between Glycosaminoglycans and Proteins

“…The binding of AT III to heparin requires an absolute 2 S 0 conformation, but according to the NMR structure information, there is negligible 1 C 4 conformation in the whole binding process ( Guerrini et al, 2006 ). Even though IdoA brings more variable binding conformational selectivity, recent studies have shown that GlcA has a better effect on the overall conformation of GAGs ( Whitmore et al, 2020 ). In order to adapt to the ECM environment, the angle of the glycosidic linkages is allowed to change to a certain extent.…”

A Bittersweet Computational Journey among Glycosaminoglycans

“…To deal with the important issue of GAG length, which still represents a bottleneck and a challenge, different approaches have been described: the coarse-grained modeling approach, that has been applied to a library of heparin chains spanning from 6- to 68-mers [ 95 ]; dedicated algorithms have been developed to generate a library of non-sulfated chondroitin spanning from 10- to 200-mer which were compared to MD-generated ensembles for internal validation [ 84 ]; the same approach was applied to libraries of HA and non-sulfated dermatan, keratan and heparan [ 96 ]. …”

“…the same approach was applied to libraries of HA and non-sulfated dermatan, keratan and heparan [ 96 ].…”

“…Some other important observations can be extracted from Figure 11 : about 82% of all the computational studies considered deal with the interactions of GAGs with proteins, while, of the remainder, 12% deal with GAG structures alone (see [ 84 , 95 , 96 , 98 ] for some examples). Among the three remaining categories, one deals with the interactions of GAGs with drugs or other inorganic or synthetic compounds (accounting for 4% of the total) [ 99 , 100 , 101 ].…”

GAG-DB, the New Interface of the Three-Dimensional Landscape of Glycosaminoglycans

“…It implies that computational approaches will be critical in understanding GAG structural biology and their mechanisms of interaction with their protein partners [ 35 , 36 , 37 ]. Significant progress has been made to investigate GAG structures, isolated and complexed with proteins, both at all-atom and coarse-grained levels [ 33 , 38 , 39 , 40 , 41 ]. However, appropriate tools for data mining of GAG-protein interactions are still missing [ 12 , 14 ].…”