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

Abstract

Surfactant protein D (SP-D) plays an important role in the innate immune system by recognizing and binding to glycans on the surface of pathogens, facilitating their clearance. Despite its importance, the detailed binding mechanisms between SP-D and various pathogenic surface glycans remain elusive due to the limited experimentally solved protein-glycan crystal structures. To address this, we developed and validated a computational workflow that integrates Induced Fit Docking, MMGBSA binding free energy calculations, and Binding Pose MetaDynamics simulations to accurately predict stable SP-D-glycan complex structure and binding mechanisms. By utilizing this workflow, we identified primary and secondary binding sites in SP-D critical for glycan recognition and uncovered a calcium chelation mode correlating with high binding affinity. To demonstrate the workflow's utility, we investigated the binding of pilin glycan from Pseudomonas aeruginosa (P. aeruginosa) to surfactant protein A (SP-A), SP-D, and mannose-binding lectin (MBL). We found that SP-D exhibited the most stable binding with pilin glycan versus SP-A and MBL, highlighting its potential role in the innate immune response against P. aeruginosa infection. These findings deepen our understanding of SP-D's role in the innate immune response and provide a basis for engineering SP-D variants for therapeutic applications. Moreover, our computational workflow can serve as a powerful tool for exploring protein-ligand interactions in diverse biologically significant systems. It provides a robust framework to guide experimental studies and accelerates the development of novel therapeutics, effectively bridging the gap between computational insights and practical applications.