Modeling interactions between Heparan sulfate and proteins based on the Heparan sulfate microarray analysis

Author:

Melo-Filho Cleber C12,Su Guowei3,Liu Kevin3,Muratov Eugene N12,Tropsha Alexander12,Liu Jian45ORCID

Affiliation:

1. Laboratory for Molecular Modeling , Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, 301 Beard Hall, , Chapel Hill, NC 27599 , United States

2. University of North Carolina , Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, 301 Beard Hall, , Chapel Hill, NC 27599 , United States

3. Glycan Therapeutics , 617 Hutton Street, Raleigh, NC 27606 , United States

4. Division of Chemical Biology and Medicinal Chemistry , Eshelman School of Pharmacy, 1044 Genetic Medicine Bldg., , Chapel Hill, NC 27599 , United States

5. University of North Carolina , Eshelman School of Pharmacy, 1044 Genetic Medicine Bldg., , Chapel Hill, NC 27599 , United States

Abstract

Abstract Heparan sulfate (HS), a sulfated polysaccharide abundant in the extracellular matrix, plays pivotal roles in various physiological and pathological processes by interacting with proteins. Investigating the binding selectivity of HS oligosaccharides to target proteins is essential, but the exhaustive inclusion of all possible oligosaccharides in microarray experiments is impractical. To address this challenge, we present a hybrid pipeline that integrates microarray and in silico techniques to design oligosaccharides with desired protein affinity. Using fibroblast growth factor 2 (FGF2) as a model protein, we assembled an in-house dataset of HS oligosaccharides on microarrays and developed two structural representations: a standard representation with all atoms explicit and a simplified representation with disaccharide units as “quasi-atoms.” Predictive Quantitative Structure–Activity Relationship (QSAR) models for FGF2 affinity were developed using the Random Forest (RF) algorithm. The resulting models, considering the applicability domain, demonstrated high predictivity, with a correct classification rate of 0.81–0.80 and improved positive predictive values (PPV) up to 0.95. Virtual screening of 40 new oligosaccharides using the simplified model identified 15 computational hits, 11 of which were experimentally validated for high FGF2 affinity. This hybrid approach marks a significant step toward the targeted design of oligosaccharides with desired protein interactions, providing a foundation for broader applications in glycobiology.

Funder

National Institutes of Health

Publisher

Oxford University Press (OUP)

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