Development and Evaluation of GlycanDock: A Protein-Glycoligand Docking Refinement Algorithm in Rosetta

Area of Science:

• Biochemistry
• Structural Biology
• Computational Biology

Background:

• Carbohydrate chains (glycans) are crucial for biological functions, mediating interactions with protein receptors.
• Understanding protein-glycoligand interactions at high resolution is key for scientific and engineering applications.
• Experimental structure determination can be challenging, necessitating computational approaches.

Purpose of the Study:

• To introduce GlycanDock, a novel algorithm for refining protein-glycoligand docking models.
• To assess the accuracy and reliability of GlycanDock using benchmark datasets.
• To provide a computational pipeline for predicting protein-glycoligand complexes.

Main Methods:

• Development of GlycanDock, a residue-centric docking refinement algorithm within the Rosetta software suite.
• Benchmark assessment using 109 experimentally determined protein-glycoligand complexes and 62 unbound protein structures.
• Evaluation of GlycanDock's ability to replicate known binding specificities.

Main Results:

• GlycanDock successfully refines protein-glycoligand models to native-like structural accuracy from starting models with up to 7 Å RMSD.
• The algorithm demonstrates statistical reliability in discriminating correct models.
• GlycanDock-refined models accurately predicted the binding specificity of a bacterial carbohydrate-binding module.

Conclusions:

• GlycanDock is a valuable computational tool for predicting protein-glycoligand complex structures.
• The algorithm accelerates research in glycosciences by overcoming experimental limitations.
• GlycanDock, combined with other tools, enhances the study of carbohydrate recognition.