Reproducing Crystal Binding Modes of Ligand Functional Groups Using Site-Identification by Ligand Competitive Saturation (SILCS) Simulations

Abstract

The applicability of a computational method, Site Identification by Ligand Competitive Saturation (SILCS), to identify regions on a protein surface with which different types of functional groups on low-molecular weight inhibitors interact is demonstrated. The method involves molecular dynamics (MD) simulations of a protein in an aqueous solution of chemically diverse small molecules from which probability distributions of fragments types, termed FragMaps, are obtained. In the present application, SILCS simulations are performed with an aqueous solution of 1 M benzene and propane to map the affinity pattern of the protein for aromatic and aliphatic functional groups. In addition, water hydrogen and oxygen atoms serve as probes for hydrogen bond donor and acceptor affinity, respectively. The method is tested using a set of 7 proteins for which crystal structures of complexes with several high affinity inhibitors are known. Good agreement is obtained between FragMaps and the positions of chemically similar functional groups in inhibitors as observed in the X-ray crystallographic structures. Quantitative capabilities of the SILCS approach are demonstrated by converting FragMaps to free energies, termed Grid Free Energies (GFE), and showing correlation between the GFE values and experimental binding affinities. For proteins for which ligand decoy sets are available, GFE values are shown to typically score the crystal conformation and conformations similar to it more favorable than decoys. Additionally, SILCS is tested for its ability to capture the subtle differences in ligand affinity across homologous proteins, information which may be of utility towards specificity-guided drug design. Taken together, our results show that SILCS can recapitulate the known location of functional groups of bound inhibitors for a number of proteins, suggesting that the method may be of utility for rational drug design.

Computational allosteric ligand binding site identification on Ras proteins

“…This method incorporates protein motion directly, thus allowing for effects of conformational selection or induced fit to be captured during the pocket identification process [109,110]. In pMD, MD simulation is conducted in the presence of probe molecules as part of the solvent environment (typically a 20:1 water-to-probe ratio) [92,108,109,[111][112][113][114]. The most common probes include ethanol, isopropanol, isobutanol, acetone, acetaldehyde, and benzene [91].…”

“…The method can also be used to estimate the maximal binding affinity (K d ) per site, although this must be used with caution as the results can be sensitive to the cutoff used to group interaction spots (see [92] for more details). Probe-based MD has been tested on a number of systems [108,109,[111][112][113][114], including the isolated catalytic domain of K-Ras in solution [92] where it was able to identify all four allosteric pockets described in the 'Discovery and Validation of Four Allosteric Binding Sites on Ras' section (Fig. 5).…”

Using Ligand‐Mapping Simulations to Design a Ligand Selectively Targeting a Cryptic Surface Pocket of Polo‐Like Kinase 1

“…[12,13] Although hydrophilic molecules have previously been used for this purpose at high (2.7 m) concentrations, [27] a lower concentration of 0.2 m was needed in this work to avoid phase separation of benzene and water. Note that an alternative method, site-identification by ligand competitive saturation (SILCS), [28][29][30] uses high concentrations of hydrophobic ligands (1m) in conjunction with an inter-ligand repulsive interaction energy term. However, SILCSs main motivation is to characterize the protein surface through competition between ligands representative of different functionalities.…”

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

“…The FragMaps include contributions from solute-protein interactions and both solute and protein desolvation in the context of protein flexibility. 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.…”