A Bittersweet Computational Journey among Glycosaminoglycans

Abstract

Glycosaminoglycans (GAGs) are linear polysaccharides. In proteoglycans (PGs), they are attached to a core protein. GAGs and PGs can be found as free molecules, associated with the extracellular matrix or expressed on the cell membrane. They play a role in the regulation of a wide array of physiological and pathological processes by binding to different proteins, thus modulating their structure and function, and their concentration and availability in the microenvironment. Unfortunately, the enormous structural diversity of GAGs/PGs has hampered the development of dedicated analytical technologies and experimental models. Similarly, computational approaches (in particular, molecular modeling, docking and dynamics simulations) have not been fully exploited in glycobiology, despite their potential to demystify the complexity of GAGs/PGs at a structural and functional level. Here, we review the state-of-the art of computational approaches to studying GAGs/PGs with the aim of pointing out the "bitter" and "sweet" aspects of this field of research. Furthermore, we attempt to bridge the gap between bioinformatics and glycobiology, which have so far been kept apart by conceptual and technical differences. For this purpose, we provide computational scientists and glycobiologists with the fundamentals of these two fields of research, with the aim of creating opportunities for their combined exploitation, and thereby contributing to a substantial improvement in scientific knowledge.

Keywords: glycosaminoglycans; heparan sulfate; heparin; molecular docking; molecular dynamic simulations; molecular modeling.

Figure 1

(A) Chemical structures of the disaccharide units composing the five main classes of GAGs. In red, “R” indicates potential points of sulfation. (B) Schematic representation of the distribution of GAGs/PGs inside the cell, on its surface, in the ECM or in body fluids.

Figure 2

Consequences of GAG/PG interactions with proteins. Upon their binding, GAGs/PGs exert different effects on proteins that impact various cellular functions.

Figure 3

Heparin (PDBid 1HPN) [49] and HA (PDBid 1HYA) [50] crystal structures depicted in sphere and stick representations and colored by elements (carbon, oxygen, nitrogen and sulphur atoms in grey, red, blue and yellow, respectively).

Figure 4

Experimental and computational methods used to generate models of GAGs alone or in complex with their binders. Each bar reports the percentage of papers in which the indicated experimental (black bars) or computational (grey bars) methods were employed. For the software grouped under “others”, see Table 2. db: database. For further details on the bibliographic research strategy, see Appendix A.

Figure 5

Docking software programs used to predict models of GAG complexes with their targets. Each bar reports the percentage of the published papers in which the indicated software programs were used. For the software programs grouped under “others”, see Table 2. For further details on the bibliographic research strategy, see Appendix A.

Figure 6

FFs used for MD simulations of GAGs alone or in complexes with targets. Each bar reports the percentage of papers in which the indicated FFs have been employed. For the FFs grouped under “others”, see Table 2. For further details on the bibliographic research strategy, see Appendix A.

Figure 7

(A) Crystal structure of a 12-mers heparin (PDBid 1HPN) shown in stick representation colored by elements with green carbons. (B) Structure of a 31-mers heparin obtained with the incremental docking method [44] and docked to the spike protein of SARS-CoV2 virus shown as electrostatic potential surface to highlight the basic path to which heparin binds. (C) Superimposition of 20 snapshots from 1 µs of MD simulation of the 31-mers heparin/spike complex showing the cloud of conformations adopted by heparin on the protein surface (adapted from Paiardi et al. https://arxiv.org/abs/2103.07722, accessed on 12 April 2021).

Figure 8

Flowchart schematizing the series of queries in an application of computational approaches aimed at a comprehensive characterization of a GAG or a GAG/target complex.

Figure 9

Number of papers containing computational studies of GAGs/PGs published since 1985. For further details on the bibliographic research strategy, see Appendix A.

Figure 10

Distribution of computational studies with respect to GAG length. For further details on the bibliographic research strategy, see Appendix A.

Figure 11

Distribution of computational structural studies of GAG alone and of GAGs complexed with the indicated ligand. For further details on the bibliographic research strategy, see Appendix A.

Figure 12

Distribution of computational studies among the different GAGs. The bar “others” includes other natural GAGs and synthetic GAG-mimicking compounds. For further details on the bibliographic research strategy, see Appendix A.

Figure 13

Virtuous circle between computational and experimental studies. As in the classical Yin and Yang principle, the two fields of research complement each other with the results from one pole helping the interpretation of the other. A correct balance between the two poles is needed in order to comprehend GAG/protein interactions and their biological consequences.