Finding a Needle in a Haystack:  Development of a Combinatorial Virtual Screening Approach for Identifying High Specificity Heparin/Heparan Sulfate Sequence(s)

Abstract

We describe a combinatorial virtual screening approach for predicting high specificity heparin/heparan sulfate sequences using the well-studied antithrombin-heparin interaction as a test case. Heparan sulfate hexasaccharides were simulated in the 'average backbone' conformation, wherein the inter-glycosidic bond angles were held constant at the mean of the known solution values, irrespective of their sequence. Molecular docking utilized GOLD with restrained inter-glycosidic torsions and intra-ring conformations, but flexible substituents at the 2-, 3-, and 6-positions and explicit incorporation of conformational variability of the iduronate residues. The approach reproduces the binding geometry of the sequence-specific heparin pentasaccharide to within 2.5 A. Screening of a combinatorial virtual library of 6,859 heparin hexasaccharides using a dual filter strategy, in which predicted antithrombin affinity was the first filter and self-consistency of docking was the second, resulted in only 10 sequences. Of these, nine were found to bind antithrombin in a manner identical to the natural pentasaccharide, while a novel hexasaccharide bound the inhibitor in a unique but dramatically different geometry and orientation. This work presents the first approach on combinatorial library screening for heparin/heparan sulfate GAGs to determine high specificity sequences and opens up huge opportunities to investigate numerous other physiologically relevant GAG-protein interactions.

2-O, 3-O Desulfated Heparin Blocks High Mobility Group Box 1 Release by Inhibition of p300 Acetyltransferase Activity

“…In Silico Molecular Modeling of ODSH and p300 Interaction Molecular modeling of the ODSH-p300 complex was performed using GOLD v5.2 software with a 3BIY crystal structure of p300 (21) and a library of 1,728 ODSH sequences, following the strategy described earlier for glycosaminoglycans (GAGs) (22,23) (see online supplement. )…”

“…This in silico molecular modeling approach has been used extensively in the literature for studying interactions of GAGs with proteins of diverse types (22,23,29,30). The structure of p300 shows several regions that are rich in basic amino acids.…”

A Hexasaccharide Containing Rare 2‐O‐Sulfate‐Glucuronic Acid Residues Selectively Activates Heparin Cofactor II

“…Only 0.8 % sequences recognize the HCII with high GOLDscores between 80–106 units (see the Supporting Information for details on methodology). In comparison, the HS‐AT system14, 16 displayed 20–30 units higher GOLDscores suggesting that the HS–HCII interaction is intrinsically likely to exhibit lower affinity than HS–AT.…”

“…We had earlier developed a dual‐filter computational algorithm, called combinatorial virtual library screening (CVLS) strategy, to rapidly sort HS sequences into “specific” and “non‐specific” bins 14, 15. We wondered whether this tool could pinpoint HS sequences that preferentially activate HCII for inhibition of thrombin.…”

“…The algorithm relies on GOLDscore, a measure of in silico “affinity” (the first filter) and consistency of binding, a measure of in silico “specificity” (the second filter), as described earlier 14, 15. The algorithm rapidly identifies key sequences, i.e., “specific” sequences, from the majority that recognize the binding site in a “non‐specific” manner.…”