Serine hydrolases are important class of enzymes which include lipases, esterases, thioesterases, amidases, peptidases and proteases. They belong to one of the largest and most diverse classes of enzymes found in nature and represent ~1% of all proteins in mammalians playing vital roles in such pathophysiological processes as blood clotting, digestion, nervous system signalling, inflammation and cancer.

Despite the fact that pharmaceutical industry avoid developing therapeutics that form covalent bonds with protein targets, several commercialized serine hydrolase inhibitors as well as promising drug-candidates contain electrophilic chemical groups that interact covalently with Serine residue in the active site of their targets.

The library of mild electrophilic compounds has been plated for most convenient and quick access. Using our Serine hydrolase focused library you receive multiple benefits, allowing you to save on time and costs in hit expansion and optimization:

• Hit confirmation support – resupply of dry samples after QC check, samples repurification or resynthesis upon request.
• Analogs from dry stock of over 4.6M compounds or REAL Space of 78.1B make-on-demand molecules.
• Straightforward & low-cost synthesis of follow-up libraries through our REAL Database technology

You have also an option to screen the library directly at Enamine. In this case we will be happy to offer discount on library cost depending on the collaboration scope.

Library design

The main criterion of the selection was presence of the electrophilic groups (cyano, epoxide, carbonyl etc.), which can provide the key interaction with serine in the active site of targeted enzyme. Next we estimated the 3D-shape of the molecules and compared them by throughput docking with the volume of the active sites taken from X-ray data of several serine hydrolases. The compounds which did not fit to the active site were withdrawn.

Representative examples of commercialized serine hydrolase covalent inhibitors and examples of functional groups which usually bind in the active site.

In addition, we applied several MedChem structural filters in order to remove unattractive moieties, trivial chemotypes and enhance the library with compounds with the latest scaffolds, sp³-rich frameworks and peptidomimetic structural motifs.

Lipoxygenases (LOXs) are enzymes that catalyze formation of the corresponding hydroperoxides from polyunsaturated fatty acids (e. g. linoleic or arachidonic). LOXs are widely expressed in immune, epithelial, and tumor cells, and activation of these enzymes induces numerous structural and metabolic changes. Abnormal LOX activity contributes to a number of pathophysiological conditions including inflammation, skin disorders and tumor genesis.

The 1 388 - compound Enamine lipoxygenase library was designed using two different approaches:

• Docking-based in silico screening, which identified compounds derived from novel scaffolds;
• Similarity search using 2D linear fingerprints and 3D pharmacophore features of the reported lipoxygenase inhibitors, which gave molecules with new side chains.

Library design

Molecular docking

Docking models were built from the PDB reported protein structures 3O8Y, 4NRE and 3D3L. Prior to docking, all structures were optimized and reconstructed to correct gaps and missing side chains (Figure 1). Water molecules coordinated to Fe³⁺ ion were restrained, and their ionization state was assigned. Each docking model contained electrostatic descriptors of the binding site and mandatory positional constraints.

The resulting models were used to screen the MedChem refined subset of ~1.0M molecules of Enamine’s entire stock collection (over 4.6M compounds). Hits were defined as possessing an obligatory alignment for the positional constraints – metal coordination (red) and hydrophobic center (orange) (Figure 2). The docking results were evaluated through comparison of the docking scores and visual inspection of binding poses (presence of H-bonds, degree of exposure to water), which gave 950 compounds (Figure 3).

Similarity search

• Search of ChEMBL database against the lipoxygenase targets gave a reference set of 322 active compounds for three lipoxygenase types (A5, A12, A15). These compounds were further filtered using potency values cut-off (IC₅₀ ≤ 1μM) and MedChem toxicophore filters.
• 2D similarity search with a 0.85 threshold was done using linear fingerprints and Tanimoto algorithm.
• Nearly 500 compounds were selected from Enamine’s screening collection and added to the LOX library.

Examples of the molecules in the library

Z57167646

CHEMBL381149
IC₅₀= 920 nM, 15-hLOX

CHEMBL1571456
IC₅₀= 120 nM, 15-hLOX

Z1213743775

Z45929261

CHEMBL518103
IC₅₀= 40 nM, 5-AhLOX

CHEMBL3604141
IC₅₀= 92 nM, 15-AhLOX

Z2001236270

The kynurenine pathway is a metabolic pathway leading to the production of nicotinamide adenine dinucleotide (NAD+) from the degradation of tryptophan. Disruption in the pathway is associated with wide range of diseases and disorders including infectious diseases (e.g. HIV), neurological disorders (Alzheimer’s disease (AD), Huntington’s disease (HD) and ALS), affective disorders (schizophrenia, depression and anxiety), autoimmune diseases, peripheral conditions and malignancy. The aim of our work was to conduct searches for new potential active compounds for the kynurenine pathway, which, in turn, could be used as convenient and quality starting points for early drug development.

Library design

The search of potential actives was performed using all available protein and ligand structural data for the following targets: indoleamine dioxygenase (IDO), tryptophan dioxygenase (TDO), 3-hydroxyanthranilic acid dioxygenase (3-HAO), kynurenine aminotransferases (KATs), kynurenine 3-monooxygenase (KMO).

Fig. 1. The kynurenine pathway of tryptophan degradation in mammals.

Screening Models preparation and validation

All available 3D structures of the targets were retrieved from PDB. Alternate superposition and comparison of the protein structures showed high sequence identity in every separate protein target > 90 % with RMSD in a range 0.4 - 0.71 Ų. Considering all representative structures only one has been selected for model preparation taking into account ligand binding parameters (protein- native ligand) and geometric parameters of the binding sites.

Fig. 2.Binding site of IDO1 (2D0T (top), 4PK5 (bottom) and example two main subpockets (in blue and yellow) used for molecular docking).

Molecular docking & Pharmacophore search

Basing on compounds with known activity and “protein–native ligand” complexes, ligand-based (IDO: 3, TDO: 4, KATs: 3, KMO: 2) and structure-based (IDO: 3, TDO: 6, 3-HAO: 2, KATs: 3, KMO: 4) pharmacophore models were build and validated. Drug-like Enamine database of over 1M compounds were then screened.

Fig. 3. Results of molecular docking: superposition of native (grey) and hit ligands (yellow) with representation of structure-based pharmacophore features.

Examples of the molecules

Fig. 4. Ligand-based Pharmacophore models used for in silico screening.

Number of compounds in targeted libraries after molecular docking calculation and pharmacophore searches:

Careful analyzes of proteins and their ligand interactions involved in kynurenine pathway were performed. The iterative in silico searches using all available state-of-the-art methodologies were curried-out to yield unique sets of potentially active molecules. Chemotype control was used to enrich the libraries with structural motifs of true positives and new “patent-free” structural cores.

Developed targeted sets are intended for high-probability initial hit discovery in one step for any particular target.

Development of efficient kinase inhibitors has been a long-standing challenge in drug development. The structural and mechanistic characterization of kinase inhibitor provides new strategies to develop specific kinase inhibitors by targeting a binding pocket adjacent or not adjacent to the ATP binding pocket.

Allosteric kinase inhibition is among the most promising and sensitive deactivation mechanism of kinase activity. Four type of possible allosteric kinase pockets were determined by now:

1. Myristoyl pocket;
2. Inhibitor binding mode that occupies part of binding pocket adjacent to the ATP-site;
3. PIF-pocket (regulatory site targeted);
4. Pocket type I^1/2 relatively allosteric regulators of kinases.

All available PDB structures with kinase allosteric inhibitors were analyzed. Most representative structures were used for molecular docking calculation. General algorithm of library design represented on the scheme below:

Examples of the molecules

Fig. 1. Example of molecular docking result into the Myristoyl pocket (4aw1 PDB entry). Native ligand represented in grey sticks, docked ligand – in yellow.

Fig. 2. Example of molecular docking into the ATP-kinase subpocket (3e8n PDB entry). Docking calculations were performed with CoA features.

Fig. 3. Example of molecular docking into the PIF pocket (4aw1 PDB entry).

Fig. 4. Example of molecular docking into the PIF I^1/2 binding site (4aw1). Hit binding mode.