Combining 3D structure with glycan array data provides insight into the origin of glycan specificity

Area of Science:

• Structural Biology
• Biochemistry
• Computational Chemistry

Background:

• Understanding how glycan-binding proteins (GBPs) select specific glycans is crucial for deciphering cellular processes.
• Experimental determination of 3D structures for GBP-glycan complexes is challenging, limiting insights into recognition mechanisms.

Purpose of the Study:

• To develop and validate an automated computational method for generating accurate 3D structures of GBP-glycan complexes.
• To integrate glycan array screening data with computational modeling to rationalize binding specificities.

Main Methods:

• Computational carbohydrate grafting: An automated technique for generating 3D structures of GBP-glycan complexes.
• Integration of glycan array data with computational modeling and existing crystallographic data.
• Iterative refinement of models to achieve agreement with experimental specificity data.

Main Results:

• The computational carbohydrate grafting method successfully generated putative co-complex structures.
• The approach identified key binding determinants and accounted for induced fit or revised minimal binding determinants when necessary.
• Applied to 10 GBP-glycan systems, the method explained the binding specificity for over 90% of 1223 tested glycans.

Conclusions:

• Computational carbohydrate grafting provides a powerful, structure-based approach to rationalize GBP binding specificity.
• This method significantly aids in understanding molecular recognition between proteins and carbohydrates.
• A webtool is available for researchers to perform computational carbohydrate grafting.