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

• Published in: Glycobiology (Oxford) Vol. 34; no. 7
• Main Authors: Melo-Filho, Cleber C, Su, Guowei, Liu, Kevin, Muratov, Eugene N, Tropsha, Alexander, Liu, Jian
• Format: Journal Article
• Language: English
• Published: England 26.05.2024
• Subjects: Fibroblast Growth Factor 2 - chemistry; Fibroblast Growth Factor 2 - metabolism; Heparitin Sulfate - chemistry; Heparitin Sulfate - metabolism; Humans; Microarray Analysis; Models, Molecular; Oligosaccharides - chemistry; Oligosaccharides - metabolism; Protein Binding; Quantitative Structure-Activity Relationship; Glycan microarray; heparin; chemoenzymatic synthesis; oligosaccharides
• ISSN: 1460-2423; 1460-2423
• DOI: 10.1093/glycob/cwae039

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.