Coarse-Grained Model of Glycosaminoglycans for Biomolecular Simulations.

Proteoglycans contain glycosaminoglycans (GAGs) which are negatively charged linear polymers made of repeating disaccharide units of uronic acid and hexosamine units. They play vital roles in numerous physiological and pathological processes, particularly in governing cellular communication and attachment. Depending on their sulfonation state, acetylation, and glycosidic linkages, GAGs belong to different families. The high molecular weight, heterogeneity, and flexibility of GAGs hamper their characterization at atomic resolution, but this may be circumvented via coarse-grained (CG) approaches. In this work, we report a CG model for a library of common GAG types in their isolated or proteoglycan-linked states compatible with version 2.2 (v2.2) of the widely popular CG Martini force field. The model reproduces conformational and thermodynamic properties for a wide variety of GAGs, as well as matching structural and binding data for selected proteoglycan test systems. The parameters developed here may thus be employed to study a range of GAG-containing biomolecular systems, thereby benefiting from the efficiency and broad applicability of the Martini framework.

Uncovering cryptic pockets in the SARS-CoV-2 spike glycoprotein.

The COVID-19 pandemic has prompted a rapid response in vaccine and drug development. Herein, we modeled a complete membrane-embedded SARS-CoV-2 spike glycoprotein and used molecular dynamics simulations with benzene probes designed to enhance discovery of cryptic pockets. This approach recapitulated lipid and host metabolite binding sites previously characterized by cryo-electron microscopy, revealing likely ligand entry routes, and uncovered a novel cryptic pocket with promising druggable properties located underneath the 617-628 loop. A full representation of glycan moieties was essential to accurately describe pocket dynamics. A multi-conformational behavior of the 617-628 loop in simulations was validated using hydrogen-deuterium exchange mass spectrometry experiments, supportive of opening and closing dynamics. The pocket is the site of multiple mutations associated with increased transmissibility found in SARS-CoV-2 variants of concern including Omicron. Collectively, this work highlights the utility of the benzene mapping approach in uncovering potential druggable sites on the surface of SARS-CoV-2 targets.

Extending the Martini Coarse-Grained Force Field to N-Glycans.

Glycans play a vital role in a large number of cellular processes. Their complex and flexible nature hampers structure-function studies using experimental techniques. Molecular dynamics (MD) simulations can help in understanding dynamic aspects of glycans if the force field parameters used can reproduce key experimentally observed properties. Here, we present optimized coarse-grained (CG) Martini force field parameters for N-glycans, calibrated against experimentally derived binding affinities for lectins. The CG bonded parameters were obtained from atomistic (ATM) simulations for different glycan topologies including high mannose and complex glycans with various branching patterns. In the CG model, additional elastic networks are shown to improve maintenance of the overall conformational distribution. Solvation free energies and octanol-water partition coefficients were also calculated for various N-glycan disaccharide combinations. When using standard Martini nonbonded parameters, we observed that glycans spontaneously aggregated in the solution and required down-scaling of their interactions for reproduction of ATM model radial distribution functions. We also optimized the nonbonded interactions for glycans interacting with seven lectin candidates and show that a relatively modest scaling down of the glycan-protein interactions can reproduce free energies obtained from experimental studies. These parameters should be of use in studying the role of glycans in various glycoproteins and carbohydrate binding proteins as well as their complexes, while benefiting from the efficiency of CG sampling.