Novel Scaffolds for Modulation of NOD2 Identified by Pharmacophore-Based Virtual Screening

Abstract

Nucleotide-binding oligomerization domain-containing protein 2 (NOD2) is an innate immune pattern recognition receptor responsible for the recognition of bacterial peptidoglycan fragments. Given its central role in the formation of innate and adaptive immune responses, NOD2 represents a valuable target for modulation with agonists and antagonists. A major challenge in the discovery of novel small-molecule NOD2 modulators is the lack of a co-crystallized complex with a ligand, which has limited previous progress to ligand-based design approaches and high-throughput screening campaigns. To that end, a hybrid docking and pharmacophore modeling approach was used to identify key interactions between NOD2 ligands and residues in the putative ligand-binding site. Following docking of previously reported NOD2 ligands to a homology model of human NOD2, a structure-based pharmacophore model was created and used to virtually screen a library of commercially available compounds. Two compounds, 1 and 3, identified as hits by the pharmacophore model, exhibited NOD2 antagonist activity and are the first small-molecule NOD2 modulators identified by virtual screening to date. The newly identified NOD2 antagonist scaffolds represent valuable starting points for further optimization.

Establishing the foundations for a data-centric AI approach for virtual drug screening through a systematic assessment of the properties of chemical data

“…Although originally developed for molecular docking screens, DUD-E's decoys are also used as inactives in LBVS [43][44][45][46][47][48] . A recent study showed that the superior enrichment efficiency in convolutional neural network models for SBVS was achieved in the DUD-E dataset because of hidden bias in this dataset rather than a successful generalization of the pattern of protein-ligand interactions 49 .…”

FOCUS on NOD2: Advancing IBD Drug Discovery with a User-Informed Machine Learning Framework

“…Hydrogen bonds with SER913 and TRP911 were observed in both MDP and a NOD2 agonist. 68 However, MDP forms an additional H bond with ARG857 (interacts with both peptide and carbohydrate part), whereas the inhibitor forms both a hydrogen and a cation-π bond with the same residue. 68 The molecules generated by FOCUS were a mixture of both agonists and inhibitors, as it was observed that there were molecules which formed cation-π bonds with ARG857.…”

“…68 However, MDP forms an additional H bond with ARG857 (interacts with both peptide and carbohydrate part), whereas the inhibitor forms both a hydrogen and a cation-π bond with the same residue. 68 The molecules generated by FOCUS were a mixture of both agonists and inhibitors, as it was observed that there were molecules which formed cation-π bonds with ARG857. Other notable residues include PHE831, TRP911, and ARG857, with ARG857 being significantly involved in hydrogen bonding, enhancing molecular recognition and interaction with ligands.…”

“…Agonists and inhibitors exhibit very similar sets of residues, but the nature of interactions might differ. Hydrogen bonds with SER913 and TRP911 were observed in both MDP and a NOD2 agonist . However, MDP forms an additional H bond with ARG857 (interacts with both peptide and carbohydrate part), whereas the inhibitor forms both a hydrogen and a cation-π bond with the same residue .…”

In Silico Drug Design and Discovery: Big Data for Small Molecule Design

“…The work by Guzelj et al described the first modulators of nucleotide-binding oligomerization domain-containing protein 2 (NOD2) identified by virtual screening [9]. NOD2 plays an important role in the development of innate and adaptive immunity because it recognizes bacterial peptidoglycan fragments.…”