Development of a Coarse-Grained Model of Chitosan for Predicting Solution Behavior

Area of Science:

• Biomaterials Science
• Computational Chemistry
• Polymer Science

Background:

• Chitosan's solution behavior is crucial for its applications.
• Predicting chitosan self-assembly requires accurate molecular models.
• The degree of acetylation (DA) significantly influences chitosan properties.

Purpose of the Study:

• To develop a coarse-grained (CG) model for chitosan.
• To predict chitosan solution behavior based on degree of acetylation (DA).
• To investigate the influence of DA and concentration on chitosan self-assembly.

Main Methods:

• Developed a new implicit-solvent CG model for chitosan.
• Derived model parameters from all-atom simulations.
• Utilized discontinuous molecular dynamics simulations.

Main Results:

• The CG model accurately represents protonated d-glucosamine (GlcN(+)) and N-acetyl-d-glucosamine (GlcNAc) monomers.
• Simulations show increased chitosan self-assembly with higher DA and concentration.
• Chitosan solutions exhibit earlier network formation and gel-like behavior at higher DA and concentration.

Conclusions:

• The new CG model effectively predicts chitosan solution behavior and self-assembly.
• Model results align with experimental observations of chitosan gel formation.
• DA and monomer sequence significantly impact chitosan self-assembly and interactions.