Coarse-Grain Model for Glucose, Cellobiose, and Cellotetraose in Water

Advancing plant cell wall modelling: Atomistic insights into cellulose, disordered cellulose, and hemicelluloses - A review

The complexity of plant cell walls on different hierarchical levels still impedes the detailed understanding of biosynthetic pathways, interferes with processing in industry and finally limits applicability of cellulose materials. While there exist many challenges to readily accessing these hierarchies at (sub-) angström resolution, the development of advanced computational methods has the potential to unravel important questions in this field. Here, we summarize the contributions of molecular dynamics simulations in advancing the understanding of the physico-chemical properties of natural fibres. We aim to present a comprehensive view of the advancements and insights gained from molecular dynamics simulations in the field of carbohydrate polymers research. The review holds immense value as a vital reference for researchers seeking to undertake atomistic simulations of plant cell wall constituents. Its significance extends beyond the realm of molecular modeling and chemistry, as it offers a pathway to develop a more profound comprehension of plant cell wall chemistry, interactions, and behavior. By delving into these fundamental aspects, the review provides invaluable insights into future perspectives for exploration. Researchers within the molecular modeling and carbohydrates community can greatly benefit from this resource, enabling them to make significant strides in unraveling the intricacies of plant cell wall dynamics.

Investigating the Hydrogen Bond-Induced Self-Assembly of Polysulfamides Using Molecular Simulations and Experiments

In this paper, we present a synergistic, experimental, and computational study of the self-assembly of N,N'-disubstituted polysulfamides driven by hydrogen bonds (H-bonds) between the H-bonding donor and acceptor groups present in repeating sulfamides as a function of the structural design of the polysulfamide backbone. We developed a coarse-grained (CG) polysulfamide model that captures the directionality of H-bonds between the sulfamide groups and used this model in molecular dynamics (MD) simulations to study the self-assembly of these polymers in implicit solvent. The CGMD approach was validated by reproducing experimentally observed trends in the extent of crystallinity for three polysulfamides synthesized with aliphatic and/or aromatic repeating units. After validation of our CGMD approach, we computationally predicted the effect of repeat unit bulkiness, length, and uniformity of segment lengths in the polymers on the extent of orientational and positional order among the self-assembled polysulfamide chains, providing key design principles for tuning the extent of crystallinity in polysulfamides in experiments. Those computational predictions were then experimentally tested through the synthesis and characterization of polysulfamide architectures.

Machine learning coarse-grained models of dissolutive wetting: a droplet on soluble surfaces

Dissolutive wetting is not only a key problem in application fields such as energy, medicine, micro-devices and etc., but also a frontier issue of academic research. As an important tool for exploring the micro-mechanisms of dissolutive wetting, molecular dynamics simulations are limited by simulation scale and force field parameters. Thus, artificial intelligence is introduced into the multi-scale simulation framework to tackle such challenges. By combining density functional theory, molecular dynamics simulations and experiments, we obtain a coarse-grained model of the glucose-water dissolution pair. Furthermore, the structure of the solid molecules and the hydration shell near the solute particles are calculated by quantum mechanics/molecular mechanics to verify the accuracy of the model. Finally, the applicability of the coarse-grained model in dissolutive wetting is proven by experimental results. We believe our machine learning method not only lays a foundation for exploring the micro-mechanisms of dissolutive wetting, but also provides a general approach for obtaining the force field parameters of different systems.

A Review of Cellulose Coarse-Grained Models and Their Applications

Cellulose is the most common biopolymer and widely used in our daily life. Due to its unique properties and biodegradability, it has been attracting increased attention in the recent years and various new applications of cellulose and its derivatives are constantly being found. The development of new materials with improved properties, however, is not always an easy task, and theoretical models and computer simulations can often help in this process. In this review, we give an overview of different coarse-grained models of cellulose and their applications to various systems. Various coarse-grained models with different mapping schemes are presented, which can efficiently simulate systems from the single cellulose fibril/crystal to the assembly of many fibrils/crystals. We also discuss relevant applications of these models with a focus on the mechanical properties, self-assembly, chiral nematic phases, conversion between cellulose allomorphs, composite materials and interactions with other molecules.

A multiscale coarse-grained model to predict the molecular architecture and drug transport properties of modified chitosan hydrogels

Hydrogels constructed with functionalized polysaccharides are of interest in a multitude of applications, chiefly the design of therapeutic and regenerative formulations. Tailoring the chemical modification of polysaccharide-based hydrogels to achieve specific drug release properties involves the optimization of many tunable parameters, including (i) the type, degree (χ), and pattern of the functional groups, (ii) the water-polymer ratio, and (iii) the drug payload. To guide the design of modified polysaccharide hydrogels for drug release, we have developed a computational toolbox that predicts the structure and physicochemical properties of acylated chitosan chains, and their impact on the transport of drug molecules. Herein, we present a multiscale coarse-grained model to investigate the structure of networks of chitosan chains modified with acetyl, butanoyl, or heptanoyl moieties, as well as the diffusion of drugs doxorubicin (Dox) and gemcitabine (Gem) through the resulting networks. The model predicts the formation of different network structures, in particular the hydrophobically-driven transition from a uniform to a cluster/channel morphology and the formation of fibers of chitin chains. The model also describes the impact of structural and physicochemical properties on drug transport, which was confirmed experimentally by measuring Dox and Gem diffusion through an ensemble of modified chitosan hydrogels.

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.

Coarse-grained molecular dynamics simulations of α-1,3-glucan

In this paper we present a computational study of aggregation in aqueous solutions of α-1,3-glucan captured using a coarse-grained (CG) model that can be extended to other polysaccharides. This CG model captures atomistic geometry (i.e., relative placement of the hydrogen bonding donors and acceptors within the monomer) of the α-1,3-glucan monomer, the directional interactions due to the donor-acceptor hydrogen bonds, and their effect on aggregation of multiple α-1,3-glucan chains without the extensive computational resources needed for simulations with atomistic models. Using this CG model, we conduct molecular dynamics simulations to assess the effect of varying α-1,3-glucan chain length and hydrogen bond interaction strengths on the aggregation of multiple chains at finite concentrations in implicit solvent. We quantify the hydrogen bonding strength needed for multiple chains to aggregate, the distribution of inter- and intra-chain hydrogen bonds within the aggregate and in some cases, the shapes of the aggregate. We also explore the effect of substitution/silencing of some randomly selected or specific hydrogen bonding sites in the chain on the aggregation and aggregate structure. In the unmodified α-1,3-glucan solution, the inter-chain hydrogen bonds cause the chains to aggregate into sheets. Random silencing of hydrogen bonding donor sites only increases the hydrogen bond strength needed for aggregation but retains the same aggregate structure as the unmodified chains. Specific silencing of the hydrogen-bonding site on the C6 carbon leads to the chains aggregating into planar sheets that then fold over to form hollow cylinders at intermediate hydrogen bond strength - 4.7 to 5.3 kcal mol-1. These cylindrical aggregates assemble end-to-end to form larger aggregates at higher hydrogen bond strengths.

An accurate coarse-grained model for chitosan polysaccharides in aqueous solution

Computational models can provide detailed information about molecular conformations and interactions in solution, which is currently inaccessible by other means in many cases. Here we describe an efficient and precise coarse-grained model for long polysaccharides in aqueous solution at different physico-chemical conditions such as pH and ionic strength. The Model is carefully constructed based on all-atom simulations of small saccharides and metadynamics sampling of the dihedral angles in the glycosidic links, which represent the most flexible degrees of freedom of the polysaccharides. The model is validated against experimental data for Chitosan molecules in solution with various degree of deacetylation, and is shown to closely reproduce the available experimental data. For long polymers, subtle differences of the free energy maps of the glycosidic links are found to significantly affect the measurable polymer properties. Therefore, for titratable monomers the free energy maps of the corresponding links are updated according to the current charge of the monomers. We then characterize the microscopic and mesoscopic structural properties of large chitosan polysaccharides in solution for a wide range of solvent pH and ionic strength, and investigate the effect of polymer length and degree and pattern of deacetylation on the polymer properties.

Perspective: Coarse-grained models for biomolecular systems

By focusing on essential features, while averaging over less important details, coarse-grained (CG) models provide significant computational and conceptual advantages with respect to more detailed models. Consequently, despite dramatic advances in computational methodologies and resources, CG models enjoy surging popularity and are becoming increasingly equal partners to atomically detailed models. This perspective surveys the rapidly developing landscape of CG models for biomolecular systems. In particular, this review seeks to provide a balanced, coherent, and unified presentation of several distinct approaches for developing CG models, including top-down, network-based, native-centric, knowledge-based, and bottom-up modeling strategies. The review summarizes their basic philosophies, theoretical foundations, typical applications, and recent developments. Additionally, the review identifies fundamental inter-relationships among the diverse approaches and discusses outstanding challenges in the field. When carefully applied and assessed, current CG models provide highly efficient means for investigating the biological consequences of basic physicochemical principles. Moreover, rigorous bottom-up approaches hold great promise for further improving the accuracy and scope of CG models for biomolecular systems.