Annotated database creation

We considered the ChemBridge GPCR-focused library containing approximately 13,000 compounds as the database for our ligand-based screen. To generate the low energy conformers, we used the ConFirm/CatConf module from the Catalyst program implemented using the “best” mode in Discovery Studio 3.1 (Discovery Studio 3.1, Accelrys, Inc. San Diego, CA). To refine the conformers, a modified version of the CHARMm force field12 was used along with a poling technique13 that biased the sampling of conformations towards geometries that were far from a local minimum but energetically near each other14. This method generated ~100 conformers of each compound within an energy cutoff of 10 kcal/mol.

Common-feature pharmacophore model building and database screening

Surveying ChEMBL (version 13), we selected 162 CXCR4 antagonists that were reported to have IC 50 values in the range of 1 nM to 10 μM. Using cluster analysis protocols implemented in Discovery Studio 3.1 (Discovery Studio 3.1, Accelrys, Inc. San Diego, CA), we grouped the compounds into 5 different clusters. Since these 162 antagonists had been evaluated using different assay conditions from multiple laboratories, it was inappropriate to apply activity-based techniques. After clustering, we selected 2 molecules from each cluster to build a training set (Supplemental Table 1). We then applied the common feature pharmacophore modeling tool implemented in Discovery Studio 3.1 (Discovery Studio 3.1, Accelrys, Inc. San Diego, CA) to build a set of 10 pharmacophores, called “hypotheses”, using the training set of compounds. The default parameters were used to build the hypotheses. We then selected another 2 molecules from each of the 5 clusters (10 molecules total) to make a test set (Supplemental Table 2). All 10 hypotheses were tested with the test set and one 5-point pharmacophore model was found to fit well to all 10 compounds of the test set. This hypothesis consisted of two hydrophobic (Hy), two Hydrogen Bond Acceptor (HBA) and one Positive Ionizable (PI) feature (Fig. 1). Each of the pharmacophoric features was assigned a weight value of 1, providing a fit value of 100% for a molecule that matched all 5 features. This pharmacophore model was selected for screening the annotated GPCR compound database. Database screening produced 26 structures with >85% fit values along with conformational energy less than 5 kcal/mol. Based on availability and synthetic tractability, we purchased 6 compounds from this ligand-based hit set. Mapping of the selected pharmacophore with one of the eventual hits (NUCC-390) is shown in Fig. 1a.

Figure 1 (a) Five-point pharmacophore used in virtual screening. Compound NUCC-390 is shown with the pharmacophore overlaid. Green = Hydrogen Bond Acceptor, Red = Positive Ionizable, Cyan = Hydrophobic. (b) Docked pose of NUCC-397 in the CXCR4 crystal structure. Dotted lines show putative hydrogen bonds. Full size image

Structure-based virtual screen

Analyzing the CXCR4 crystal structures (accession codes 3ODU and 3OE0)20, we observed the 16-residue cyclic peptide filling a large ligand binding site, whereas the small molecule IT1t only occupies a small part of the pocket. To obtain consensus binding poses with flexible ligand docking tools, we selected two docking engines built upon orthogonal algorithms. The Surflex26 docking engine implemented using Sybyl-X (Sybyl-X, Certara, Inc. St. Louis, MO) and the Glide docking tool version 6.5 (Schrödinger, LLC, New York, NY) were both used as they have been found to be superior both in pose prediction and virtual screening of compound databases27. The Surflex docking engine is built upon a fragment-based algorithm whereas the Glide docking tool is built on a grid-based technique. Since we considered a relatively small 13,000 compound database of GPCR-focused molecules, we carried out the docking using both Surflex and Glide docking engines.

To prepare the protein for the docking experiments, the small-molecule bound CXCR4 crystal structure (pdb code 3ODU) was validated using Prime version 3.8 (Schrödinger, LLC, New York, NY) to correct for irrelevant side chains, missing atoms, undesired orientation of Asn, Gln or His residues, to replace the b-values by the OPLS charges and to fix the protonation states of the residues at physiological pH. Next, the ‘Prot-Prep’ module was used to prepare and refine the co-crystal structure to generate the receptor (protein) and the bound ligand. A 12 Å3 grid box was generated using the centroid of the bound ligand to prepare for Glide docking.

For Surflex docking, the ligand (IT1t) was extracted from the co-crystal structure and the protein was subjected to the protein preparation panel in the Sybyl interface. In this panel, hydrogens were added in hydrogen bonding orientation, b-values were replaced by the Gasteiger charges, irrelevant torsions were eliminated and the protonation states of the residues were fixed at pH 7.4. A ligand-based protomol was generated in the active site which represented the template for an ideal active-site ligand.

Library screening

We first docked the 20 reported antagonists (Training and Test sets, see above) using the Glide-XP module with the standard sampling mode of maxkeep = 5000 and maxref = 400. The van der Waals radii for nonpolar ligand atoms were scaled to 0.8. After docking, the docked poses of the 20 compounds were analyzed and we noted the interactions of the antagonists with the different protein residues. As these compounds were of different chemotypes, they showed different binding poses interacting with different residues. The Lig-Prep module of the Schrodinger suite was utilized to prepare the GPCR-focused library for docking. Using the same docking protocols as described above, we docked the library structures into the ligand-binding site of CXCR4. Compounds showing a Glide score of <−6.0 (Schrödinger, LLC, New York, NY, 2014) were considered for further analysis. The interacting residues identified from the known antagonist set guided our analysis of the docked poses of the unknown compounds from library. Based on the docked scores and the interactions with critical residues, we selected 52 compounds from this set as in silico hits.

We carried out a similar approach for this docking experiment using the Surflex docking tool implemented in Sybyl interface. At first we docked the 20 antagonist set in the earlier-defined ligand-binding site of CXCR4. The default set of run time parameters had been used along with the GeomX docking mode, which generated the best docking pose of the ligands. After docking the known antagonists, we analyzed the docked poses and identified the critical interacting residues of the CXCR4 active site. Then we prepared the GPCR ligand set using the ligand preparation panel implemented in the Sybyl interface. Using similar docking protocols, we docked the library and 48 compounds showed good interactions with active site residues and had a total score >6.0 where total score is a function of −logK d 26.

We found 22 compounds with similar binding poses and favorable interactions with the active site residues in common between the Glide and Surflex docking experiments. These in silico hits underwent further evaluation for their presence of potentially toxic or metabolically unstable groups, reactive functional groups, non-drug like features, synthetic feasibility, structural diversity and commercial availability. Based on these criteria, 9 of these structure-based virtual hits were purchased. The docked pose of NUCC-397 along with the interacting residues is shown in Fig. 1b. Structures of all 15 compounds subjected to in vitro testing and their associated fit values, docking scores and conformational energies are shown in Supplemental Table 3.

Calcium imaging assay

Our initial assay for examining the activity of different molecules was based on the fact that activation of CXCR4 receptors produces an increase in the intracellular free Ca2+ concentration (Ca)i. This signal can easily be observed using a fluorescent Ca2+ sensing dye such as fura-228,29,30. The quantitative nature of this assay makes it ideal for screening purposes. Moreover, the assay can also distinguish potential antagonists from potential agonists. We initially used the aggressive human melanoma cell line C8161 which expresses numerous human CXCR4 receptors and produces strong (Ca)i signals when stimulated with SDF-1 (Fig. 2). In the assay, cells were usually stimulated twice with SDF-1. As can be observed in Fig. 2a (control), this resulted in two (Ca)i responses of similar magnitude indicating that when applied acutely in this manner little desensitization was noted. To test a drug, the compound in question was usually added prior to the second stimulation with SDF-1. At this point it was possible to observe whether the compound itself acted as an agonist by giving its own response or if it reduced the magnitude of the second response to SDF-1. Our 15 vHTS hits were assayed at an initial single screening concentration of 10 μM and several compounds showed significant biological activity (Table 1). Some of the compounds such NUCC-388, 392, 397 and 54120 antagonized the effects of SDF-1 (Fig. 2b). Each of the four antagonists were then assayed at multiple concentrations to obtain a dose-response relationship and an estimated IC 50 . Antagonists NUCC-388, 397, 392 and 51420 had IC 50 values of 0.3 μM, 3 μM, 1 μM and 1 μM respectively.

Table 1 Structures and calcium imaging behavior of CXCR4 modulators. Full size table

Figure 2 (Ca)i mobilization assay using CXCR4 expressing C8161 melanoma cells. Each colored line represents the response of a different single cell (a) Control using endogenous CXCR4 agonist SDF-1 (100 nM) shows two strong (Ca)i responses. Addition of ATP (10 μM) to activate purinergic receptors was performed as a positive control for cell viability (b) Antagonist NUCC-388 (10 μM) blocks the effect of SDF-1. (c) Agonist NUCC-390 (10 μM) produces strong (Ca)i response which is blocked by the known potent and selective CXCR4 antagonist AMD3100 (1 μM). (d) Agonist effects of NUCC-398 (10 μM). (e) Comparison of the effects of SDF-1 and NUCC-390 averaged over 74 cells. Full size image

Interestingly, several other molecules displayed clear agonist activity. For example, compound NUCC-390 (Fig. 2c) exhibited effects that were similar to those produced by SDF-1. The effects of NUCC-390 were clearly mediated by activation of CXCR4 receptors as they were inhibited by both AMD3100 (a highly-selective CXCR4 antagonist, Fig. 2c) and NUCC-388 (one of the novel CXCR4 antagonists, not shown) which both also blocked the effects of SDF-1. Interestingly, several other molecules in this series including NUCC-398 (Fig. 2d), 54118, 54121 and 54127 all displayed robust agonist activity when tested on C8161 cells. In each case this stimulation was demonstrated to be inhibited by AMD3100 (data not shown). Averaging data collected over a large number of cells demonstrated that the kinetics of the responses to SDF-1 and NUCC-390 were similar (Fig. 2e). In order to further demonstrate that these agonist molecules were not producing some general off-target effect, we tested some of them on the HEK 293 cell line due to its very low endogenous CXCR4 expression. We observed that SDF-1 or agonists such as NUCC-54118 and NUCC-390 produced no effect on these cells (Supplementary Figure S1).

ERK activation by agonist NUCC-390

To further explore the agonist potential of compound NUCC-390, we examined changes in signaling downstream of CXCR4. For these experiments, we collected lysates from treated C8161 cells and analyzed them using Western blot. Activation of the CXCR4 receptor has been shown to indirectly mediate phosphorylation of ERK31,32, a key signaling molecule in the MAP kinase pathway. As expected, we observed that cells treated with SDF-1 for 30 min. displayed increased levels of phosphorylated ERK (pERK). Interestingly, treatment with drug NUCC-390 also led to increased levels of pERK (Fig. 3). That drug NUCC-390 has the capability of stimulating signaling activity downstream of CXCR4 receptors further supports the observation that NUCC-390 acts as a CXCR4 agonist.

Figure 3 Increase in pERK produced by SDF-1 (100 nM) and NUCC-390 (10 μM) in CXCR4 expressing C8161 cells. ***p < 0.001, *p < 0.05. #Different from the effect of SDF-1, p < 0.05, n = 6. Full size image

NUCC-390 induces internalization of CXCR4 receptors

Another characteristic feature of CXCR4 receptors and many other GPCRs is receptor internalization following agonist stimulation33,34. In order to determine if NUCC-390 exhibited the ability to induce CXCR4 receptor internalization, we assessed the cellular localization of YFP-tagged CXCR4 receptors expressed in HEK293 cells following treatment with SDF-1 or NUCC-390. Non-treated cells showed some diffuse expression of CXCR4-YFP throughout the cytosol and clear expression in the cell membrane (Fig. 4). Treatment with SDF-1 for a period of 2 hours led to pronounced internalization of CXCR4-YFP, producing noticeable aggregates of the receptors in the cytosol but excluded from the nucleus. Similar effects were produced by NUCC-390. The effects of NUCC-390 were completely inhibited by AMD-3100 (Fig. 4d) or NUCC-388 (not shown). Interestingly, following antagonist treatment virtually all of the CXCR4-YFP was localized in the cell membrane. This might indicate some constitutive activity of the receptor and possibly inverse agonist activity for both AMD-3100 and NUCC-39035.

Figure 4 CXCR4-YFP transfected HEK293 cells treated with agonist 390. (A) CXCR4-YFP transfected cells show normal CXCR4 expression in the cell membrane. Pretreatment with agonist SDF-1 (100 nM) (B) or NUCC-390 (10 μM) (C) for 2 hours causes most of the CXCR4 receptor to become internalized inside cell vesicles. (D) Selective CXCR4 antagonist AMD3100 (1 μM) blocks internalization of agonist NUCC-390. Full size image

SDF-1 and NUCC-390 mediate chemotaxis

Chemokines are well known for their ability to stimulate chemotaxis of leukocytes and stem cells. In order to further establish the biological activity of our novel CXCR4 agonists we compared the ability of SDF-1 and NUCC-390 to produce chemotaxis of C8161 cells using a Boyden chamber assay. SDF-1 produced robust chemotactic activity which was matched by the effects of NUCC-390 demonstrating that this novel agonist can produce one of the major biological effects of chemokines (Fig. 5).

Figure 5 Chemotaxis produced by SDF-1 (100 nM) or NUCC-390 (10 μM) using C8161 cells in a Boyden chamber. Both SDF-1 and NUCC-390 produced significant effects (p < 0.01, n = 6). Full size image

125I-SDF-1α binding to the CXCR4 receptor

We assessed the interaction of NUCC-390 with CXCR4 receptors by examining the binding of 125I labelled SDF-1α to CXCR4 receptors in human Chem-1 cells36. NUCC-390 showed no significant ability to inhibit binding of 125I-SDF-1α to CXCR4 in concentrations up to 10−5 M.