Identification and characterization of fragment binding sites for allosteric ligand design using the site identification by ligand competitive saturation hotspots approach (SILCS-Hotspots)

Abstract

Background: Fragment-based ligand design is used for the development of novel ligands that target macromolecules, most notably proteins. Central to its success is the identification of fragment binding sites that are spatially adjacent such that fragments occupying those sites may be linked to create drug-like ligands. Current experimental and computational approaches that address this problem typically identify only a limited number of sites as well as use a limited number of fragment types.

Methods: The site-identification by ligand competitive saturation (SILCS) approach is extended to the identification of fragment bindings sites, with the method termed SILCS-Hotspots. The approach involves precomputation of the SILCS FragMaps following which the identification of Hotspots, performed by identifying of all possible fragment binding sites on the full 3D structure of the protein followed by spatial clustering.

Results: The SILCS-Hotspots approach identifies a large number of sites on the target protein, including many sites not accessible in experimental structures due to low binding affinities and binding sites on the protein interior. The identified sites are shown to recapitulate the location of known drug-like molecules in both allosteric and orthosteric binding sites on seven proteins including the androgen receptor, the CDK2 and Erk5 kinases, PTP1B phosphatase and three GPCRs; the β2-adrenergic, GPR40 fatty-acid binding and M2-muscarinic receptors. Analysis indicates the importance of considering all possible fragment binding sites, and not just those accessible to experimental methods, when identifying novel binding sites and performing ligand design versus just considering the most favorable sites. The approach is shown to identify a larger number of known binding sites of drug-like molecules versus the commonly used FTMap and Fpocket methods.

General significance: The present results indicate the potential utility of the SILCS-Hotspots approach for fragment-based rational design of ligands, including allosteric modulators.

Keywords: Allosteric pocket; Competitive inhibitor; Computer-aided drug design; Cryptic pocket; Fragment-based drug design; Orthosteric.

Figure 1)

Androgen receptor (transparent cartoon representation) showing the SILCS-Hotspots (VDW spheres, colored by LGFE ranking, red: most favorable, blue: least favorable) along with the SILCS FragMaps (mesh representations) and, as indicated by arrows, the orthosteric ligand, dihydrotestosterone (DHT, central region of figure, see arrow) and the allosteric modulator, flufenamic acid (FLA, top of figure, see arrow). SILCS FragMaps are shown for generic apolar (green, −0.9 kcal/mol), generic H-bond donor (blue, −0.9 kcal/mol), generic H-bond donor (red, −0.9 kcal/mol), negative (orange, −1.5 kcal/mol) and positive (cyan, −1.5 kcal/mol) groups.

Figure 2)

Crystallographic orientations of dihydrotestosterone (DHT, left) and flufenamic acid (FLA, Licorice representation, atom colored) overlaid on the Androgen receptor (cartoon) along with the SILCS-Hotspots (VDW spheres, colored by LGFE ranking, red: most favorable, blue: least favorable) and the SILCS FragMaps (see Figure 1 legend).

Figure 3)

Hotspots-site mean LGFE score as a function of the site rank order for the Androgen Receptor. Hotspots associated with the dihydrotestosterone (DHT, red circles) and flufenamic acid (FLA, blue squares) ligand binding sites are shown. Hotspots-site mean LGFE scores are the averages over the LGFE scores of all the fragments bound to each hotspot.

Figure 4)

Example fragments that bind to the Androgen receptor as identified by SILCS-Hotspots analysis. A) Two nonpolar ligands that bind to a large number of Hotspots. Selected polar ligands that bind to Hotspots 3 B) and 26 C) that comprise part of the FLA and DHT binding sites on the Androgen receptor.

Figure 5)

Hotspots-site mean LGFE score as a function of the site rank order for the studied proteins. A) Data for all the sites identified in each protein and B) for the 20 top ranked sites for each protein. Hotspots-site mean LGFE scores are the averages over the LGFE scores of all the fragments bound to each hotspot.

Figure 6)

CDK2 backbone cartoon structures for the A) and C) active conformation (PDB ID: 3MY5, cyan cartoon) and B) and D) inactive conformation (PDB ID: 1PW2, blue cartoon) for two approximately orthogonal orientations of the protein. Included are the two orientations, a and b, of the allosteric modulator 2AN (from PDB ID 3PXF) along with the SILCS-Hotspots for the respective conformations (vdW spheres, colored based on mean LGFE score, red most favorable, blue least favorable). Protein structures were aligned prior to visualization as described in Table S1 of the supporting information.

Figure 7)

Androgen receptor (cartoon, cyan) with A) the allosteric modulator flufenamic acid (FLA, CPK, atom color) or B) selected Fragments for selected Hotspots (labeled and colored by rank). Shown are the SILCS exclusion maps (tan, solid surface), protein backbone (cyan, cartoon representation), selected Hotspots (vdW spheres, coloring based on mean LGFE scores, Table 3), and SILCS FragMaps with cutoff energies for visualization: Positive (cyan, −1.2 kcal/mol), Negative (orange, −1.2 kcal/mol), Apolar (green, −1.2 kcal/mol), H-bond donor (blue, −0.9 kcal/mol) and H-bond acceptor (−0.9 kcal/mol). Fragments shown include 1, 3, 9, 21, and 29, for site 3, 3, 11, 31, 42, 84 for site 17, and 48, 49, 61, 63c and 74 for site 34 (Figure S1 supporting information).

Figure 8)

Erk5 (cartoon, cyan) with A) the competitive inhibitor 4WG (CPK, atom color) or B) selected Fragments for selected Hotspots (labeled and colored by rank). Shown are the SILCS exclusion maps (tan, solid surface), protein backbone (cyan, cartoon representation), selected Hotspots (vdW spheres, coloring based on mean LGFE scores, Table 3), and SILCS FragMaps with cutoff energies for visualization: Positive (cyan, −1.2 kcal/mol), Negative (orange, −1.2 kcal/mol), Apolar (green, −1.2 kcal/mol), H-bond donor (blue, −0.9 kcal/mol) and H-bond acceptor (−0.9 kcal/mol). Fragments shown include 4b, 7b, 29, 71 and 84 for site 8, 38, 41, 45, 76 and 88 for site 25, 7b and 29 for site 71, 4b for site 78, 5 and 6 for site 86, (Figure S1 supporting information).

Figure 9)

GPR40 (cartoon, cyan, PDB ID 5KW2) with A) the positive allosteric modulator MK6 (CPK, atom color) or B) selected Fragments for selected Hotspots (labeled and colored by rank). Shown are the SILCS exclusion maps (tan, solid surface), protein backbone (cyan, cartoon representation), selected Hotspots (vdW spheres, coloring based on mean LGFE scores, Table 3), and SILCS FragMaps with cutoff energies for visualization: Positive (cyan, −1.2 kcal/mol), Negative (orange, −1.2 kcal/mol), Apolar (green, −1.2 kcal/mol), H-bond donor (blue, −0.9 kcal/mol) and H-bond acceptor (−0.9 kcal/mol). Fragments shown include 6, 11, 15, 37, and 45 for site 27, 6, 35, 48, 74 and 77 for site 57 and 5 for site 61 (Figure S1 supporting information).

Figure 10)

Allosteric binding site of the M2 muscarinic receptor showing the A) crystallographic orientation of allosteric modulator 2CU (CPK) and B) selected fragments from the Hotspots analysis. Included are the SILCS exclusion maps (tan, solid surface), protein backbone (cyan, cartoon representation), the three Hotspots (vdW spheres, coloring based on mean LGFE scores, Table 3), and selected SILCS FragMaps with cutoff energies for visualization: Positive (cyan, −1.2 kcal/mol), Negative (orange, −1.2 kcal/mol), Apolar (green, −1.2 kcal/mol), H-bond donor (blue, −0.9 kcal/mol) and H-bond acceptor (−0.9 kcal/mol). Fragments shown include 3, 37, 41, 45 and 84 for site 33, 6, 49, 61, 76 and 83 for site 66, and 4b, 52b, 63c and 88 for site 77 (Figure S1 supporting information).

Scheme 1)

Workflow defining the SILCS-Hotspots process