Evaluation of intermolecular interactions of self-etch dentin adhesive primer molecules with type 1 collagen: Computer modeling and in vitro binding analysis
Source abstract
Abstract
The objective of this investigation was to study adhesion of self-etch primer systems to dentin by computer-modeled docking simulations and in vitro binding assay methods. Computer modeling employed analysis of docking simulations of a self-etch primer molecule 10-methacryloxydecamethylene phosphoric acid (MDP) and its calcium salt (MDPCa) as ligands. Typical type 1 collagen segments were selected as targets to reflect potential differences in the amino acid residues in dentinal type 1 collagen triple helix motif. The binding assay involved immunochemical analysis of the modification of anti-collagen binding to collagen by prior exposure of the demineralized dentin to MDP. The estimated mean docking energy values ranged between -4.5 and -8.9kcalmol(-1). The results revealed significant differences in the docking energy estimates as a function of ligand and target structures (p<0.01). Van der Waals and electrostatic contributions were also significantly influenced by ligand selection and collagen structure. Both MDP and MDPCa appear to be important in the overall interactions. Binding assay studies also lend evidence of collagen-ligand intermolecular interactions. It is suggested that the ability of self-etch dentin primer systems to bond effectively to dentin is not limited to the interaction of the primer with the hydroxyapatite of dentin, but also due to the ability to prime dentin efficiently through intermolecular interactions between the primer and its calcium salt with the collagen matrix. Virtual screening methods may be very valuable to select primer molecules for dentin bonding.
Source: europepmc · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Dental materials and restorations · Collagen: Extraction and Characterization · Endodontics and Root Canal Treatments
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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