Decoding SP-D and glycan binding mechanisms using a novel computational workflow

Area of Science:

• Immunology
• Computational Biology
• Structural Biology

Background:

• Surfactant protein D (SP-D) is crucial for innate immunity, recognizing and clearing pathogens via glycan binding.
• Limited experimental structures hinder understanding of SP-D's precise glycan binding mechanisms.

Purpose of the Study:

• To develop and validate a computational workflow for predicting SP-D-glycan complex structures and binding mechanisms.
• To elucidate the molecular interactions governing SP-D's recognition of pathogenic glycans.

Main Methods:

• Integrated computational approach: induced fit docking, MM/GBSA binding free energy calculations, and binding pose metadynamics simulations.
• Application of the workflow to study SP-D binding with pilin glycan from Pseudomonas aeruginosa, comparing it with SP-A and MBL.

Main Results:

• Identified critical primary and secondary binding sites in SP-D for glycan recognition.
• Discovered a calcium chelation mode associated with high SP-D binding affinity.
• SP-D demonstrated significantly more stable binding to Pseudomonas aeruginosa pilin glycan compared to SP-A and MBL.

Conclusions:

• The computational workflow accurately predicts SP-D-glycan interactions, revealing key binding sites and mechanisms.
• SP-D plays a significant role in the innate immune response against Pseudomonas aeruginosa.
• Findings provide a foundation for engineering SP-D for therapeutic applications and offer a versatile tool for studying protein-ligand interactions.