Pharmacophore Modeling Using Site-Identification by Ligand Competitive Saturation (SILCS) with Multiple Probe Molecules

Abstract

Receptor-based pharmacophore modeling is an efficient computer-aided drug design technique that uses the structure of the target protein to identify novel leads. However, most methods consider protein flexibility and desolvation effects in a very approximate way, which may limit their use in practice. The Site-Identification by Ligand Competitive Saturation (SILCS) assisted pharmacophore modeling protocol (SILCS-Pharm) was introduced recently to address these issues as SILCS naturally takes both protein flexibility and desolvation effects into account by using full MD simulations to determine 3D maps of the functional group-affinity patterns on a target receptor. In the present work, the SILCS-Pharm protocol is extended to use a wider range of probe molecules including benzene, propane, methanol, formamide, acetaldehyde, methylammonium, acetate and water. This approach removes the previous ambiguity brought by using water as both the hydrogen-bond donor and acceptor probe molecule. The new SILCS-Pharm protocol is shown to yield improved screening results as compared to the previous approach based on three target proteins. Further validation of the new protocol using five additional protein targets showed improved screening compared to those using common docking methods, further indicating improvements brought by the explicit inclusion of additional feature types associated with the wider collection of probe molecules in the SILCS simulations. The advantage of using complementary features and volume constraints, based on exclusion maps of the protein defined from the SILCS simulations, is presented. In addition, re-ranking using SILCS-based ligand grid free energies is shown to enhance the diversity of identified ligands for the majority of targets. These results suggest that the SILCS-Pharm protocol will be of utility in rational drug design.

Iminoguanidines as Allosteric Inhibitors of the Iron-Regulated Heme Oxygenase (HemO) of Pseudomonas aeruginosa

“…42 Figure 3 shows the predicted binding orientation of compound 23 , as well as overlap with selected FragMaps for both the heme-binding site (upper right) and the allosteric site (upper left). Overlap with the FragMaps in the heme-binding pocket is poor, while strong overlap in the allosteric site indicates preferred binding at the putative site.…”

Structure-based design of N-substituted 1-hydroxy-4-sulfamoyl-2-naphthoates as selective inhibitors of the Mcl-1 oncoprotein

“…Thus, subsequent modeling involved docking of each studied compound using the SILCS-Pharm approach as described in the supporting information (Figure S1). 46,47 The docked orientation of each compound was then subjected to MC-SILCS sampling to allow the molecules to conformationally sample the local binding region as defined by the FragMaps and the exclusion maps, with the LGFE scores calculated from the MC SILCS conformational sampling. Comparison of the LGFE values for all the tested compounds with the experimental affinities converted to free energies (ΔG = -RTln K i , where R is the Boltzmann constant and T is the temperature) for Mcl-1 yields a correlation of R 2 = 0.53 and a high predictive index 51 of 0.70, (Figure S2, supporting information).…”

“…Thus, the LGFE scores correlate with the Mcl-1 experimental data, indicating the quality of the bound orientations from the SILCS-Pharm-MC-SILCS protocol. 45-47 …”

“…To identify the binding modes of the tested compounds with Mcl-1, the SILCS-Pharm protocol 46,47 was used in which the FragMaps are used to define the pharmacophore features. This involved identifying all possible pharmacophore features in the peptide binding pocket based on the region close to inhibitor 2 (PDB ID: 4HW3).…”

“…44,45 In addition, to assure that all regions accessible to solute atoms can be occupied by the studied ligands, the protein surface is defined based on SILCS exclusion maps. 47 Notably, the information content of SILCS may be used to qualitatively direct ligand design as well as to rapidly make quantitative estimates of relative ligand affinities. 48 …”