Site Identification By Ligand Competitive Saturation Research Articles

Exploring the Interhelical Landscape of the β-2 Adrenergic Receptor to Identify Druggable Intermediate States using Enhanced-Sampling Molecular Dynamics and Site Identification by Ligand Competitive Saturation (SILCS)

Exploring the Interhelical Landscape of the β-2 Adrenergic Receptor to Identify Druggable Intermediate States using Enhanced-Sampling Molecular Dynamics and Site Identification by Ligand Competitive Saturation (SILCS)

Exploring protein-protein interactions using the site-identification by ligand competitive saturation methodology.

Protein docking methods are powerful computational tools to study protein-protein interactions (PPI). While a significant number of docking algorithms have been developed, they are usually based on rigid protein models or with limited considerations of protein flexibility and the desolvation effect is rarely considered in docking energy functions, which may lower the accuracy of the predictions. To address these issues, we introduce a PPI energy function based on the site-identification by ligand competitive saturation (SILCS) framework and utilize the fast Fourier transform (FFT) correlation approach. The free energy content of the SILCS FragMaps represent an alternative to traditional energy grids and they can be efficiently utilized to guide FFT-based protein docking. Application of the approach to eight diverse test cases, including seven from Protein Docking Benchmark 5.0, showed the PPI prediction using SILCS approach (SILCS-PPI) to be competitive with several commonly used protein docking methods indicating that the method has the ability to both qualitatively and quantitatively inform the prediction of PPI. Results show the utility of the SILCS-PPI docking approach for determination of probability distributions of PPI interactions over the surface of both partner proteins, allowing for identification of alternate binding poses. Such binding poses are confirmed by experimental crystal contacts in our test cases. While more computationally demanding than available PPI docking technologies, we anticipate that the SILCS-PPI docking approach will offer an alternative methodology for improved evaluation of PPIs that could be used in a variety of fields from systems biology to excipient design for biologics-based drugs.

Estimation of relative free energies of binding using pre-computed ensembles based on the single-step free energy perturbation and the site-identification by Ligand competitive saturation approaches.

Accurate and rapid estimation of relative binding affinities of ligand-protein complexes is a requirement of computational methods for their effective use in rational ligand design. Of the approaches commonly used, free energy perturbation (FEP) methods are considered one of the most accurate, although they require significant computational resources. Accordingly, it is desirable to have alternative methods of similar accuracy but greater computational efficiency to facilitate ligand design. In the present study relative free energies of binding are estimated for one or two non-hydrogen atom changes in compounds targeting the proteins ACK1 and p38 MAP kinase using three methods. The methods include standard FEP, single-step free energy perturbation (SSFEP) and the site-identification by ligand competitive saturation (SILCS) ligand grid free energy (LGFE) approach. Results show the SSFEP and SILCS LGFE methods to be competitive with or better than the FEP results for the studied systems, with SILCS LGFE giving the best agreement with experimental results. This is supported by additional comparisons with published FEP data on p38 MAP kinase inhibitors. While both the SSFEP and SILCS LGFE approaches require a significant upfront computational investment, they offer a 1000-fold computational savings over FEP for calculating the relative affinities of ligand modifications once those pre-computations are complete. An illustrative example of the potential application of these methods in the context of screening large numbers of transformations is presented. Thus, the SSFEP and SILCS LGFE approaches represent viable alternatives for actively driving ligand design during drug discovery and development. © 2016 Wiley Periodicals, Inc.

Ribosome-Templated Azide-Alkyne Cycloadditions: Synthesis of Potent Macrolide Antibiotics by In Situ Click Chemistry.

Over half of all antibiotics target the bacterial ribosome-nature's complex, 2.5 MDa nanomachine responsible for decoding mRNA and synthesizing proteins. Macrolide antibiotics, exemplified by erythromycin, bind the 50S subunit with nM affinity and inhibit protein synthesis by blocking the passage of nascent oligopeptides. Solithromycin (1), a third-generation semisynthetic macrolide discovered by combinatorial copper-catalyzed click chemistry, was synthesized in situ by incubating either E.coli 70S ribosomes or 50S subunits with macrolide-functionalized azide 2 and 3-ethynylaniline (3) precursors. The ribosome-templated in situ click method was expanded from a binary reaction (i.e., one azide and one alkyne) to a six-component reaction (i.e., azide 2 and five alkynes) and ultimately to a 16-component reaction (i.e., azide 2 and 15 alkynes). The extent of triazole formation correlated with ribosome affinity for the anti (1,4)-regioisomers as revealed by measured Kd values. Computational analysis using the site-identification by ligand competitive saturation (SILCS) approach indicated that the relative affinity of the ligands was associated with the alteration of macrolactone+desosamine-ribosome interactions caused by the different alkynes. Protein synthesis inhibition experiments confirmed the mechanism of action. Evaluation of the minimal inhibitory concentrations (MIC) quantified the potency of the in situ click products and demonstrated the efficacy of this method in the triaging and prioritization of potent antibiotics that target the bacterial ribosome. Cell viability assays in human fibroblasts confirmed 2 and four analogues with therapeutic indices for bactericidal activity over in vitro mammalian cytotoxicity as essentially identical to solithromycin (1).

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

Structure-based drug design was utilized to develop novel, 1-hydroxy-2-naphthoate-based small-molecule inhibitors of Mcl-1. Ligand design was driven by exploiting a salt bridge with R263 and interactions with the p2 pocket of the protein. Significantly, target molecules were accessed in just two synthetic steps, suggesting further optimization will require minimal synthetic effort. Molecular modeling using the Site-Identification by Ligand Competitive Saturation (SILCS) approach was used to qualitatively direct ligand design as well as develop quantitative models for inhibitor binding affinity to Mcl-1 and the Bcl-2 relative Bcl-xL as well as for the specificity of binding to the two proteins. Results indicated hydrophobic interactions in the p2 pocket dominated affinity of the most favourable binding ligand (3bl: Ki = 31 nM). Compounds were up to 19-fold selective for Mcl-1 over Bcl-xL. Selectivity of the inhibitors was driven by interactions with the deeper p2 pocket in Mcl-1 versus Bcl-xL. The SILCS-based SAR of the present compounds represents the foundation for the development of Mcl-1 specific inhibitors with the potential to treat a wide range of solid tumours and hematological cancers, including acute myeloid leukemia.

Cyclopropyl-containing positive allosteric modulators of metabotropic glutamate receptor subtype 5.

Positive allosteric modulators (PAMs) binding to the transmembrane (TM) domain of metabotropic glutamate receptor 5 (mGluR5) are promising therapeutic agents for psychiatric disorders and traumatic brain injury (TBI). Novel PAMs based on a trans-2-phenylcyclopropane amide scaffold have been designed and synthesized. Facilitating ligand design and allowing estimation of binding affinities to the mGluR5 TM domain was the novel computational strategy, site identification by ligand competitive saturation (SILCS). The potential protective activity of the new compounds was evaluated using nitric oxide (NO) production in BV2 microglial cell cultures treated with lipopolysaccharide (LPS), and the toxicity of the new compounds tested using a cell viability assay. One of the new compounds, 3a, indicated promising activity with potency of 30 μM, which is 4.5-fold more potent than its lead compound 3,3'-difluorobenzaldazine (DFB), and showed no detectable toxicity with concentrations as high as 1000 μM. Thus this compound represents a new lead for possible development as treatment for TBI and related neurodegenerative disorders.

Acyl-2-aminobenzimidazoles: A novel class of neuroprotective agents targeting mGluR5

Positive allosteric modulators (PAMs) of the metabotropic glutamate receptor 5 (mGluR5) are promising therapeutic agents for treating traumatic brain injury (TBI). Using computational and medicinal methods, the structure–activity relationship of a class of acyl-2-aminobenzimidazoles (1–26) is reported. The new compounds are designed based on the chemical structure of 3,3′-difluorobenzaldazine (DFB), a known mGluR5 PAM. Ligand design and prediction of binding affinities of the new compounds have been performed using the site identification by ligand competitive saturation (SILCS) method. Binding affinities of the compounds to the transmembrane domain of mGluR5 have been evaluated using nitric oxide (NO) production assay, while the safety of the compounds is tested. One new compound found in this study, compound 22, showed promising activity with an IC50 value of 6.4μM, which is ∼20 fold more potent than that of DFB. Compound 22 represents a new lead for possible development as a treatment for TBI and related neurodegenerative conditions.

Mapping functional group free energy patterns at protein occluded sites: nuclear receptors and G-protein coupled receptors.

Occluded ligand-binding pockets (LBP) such as those found in nuclear receptors (NR) and G-protein coupled receptors (GPCR) represent a significant opportunity and challenge for computer-aided drug design. To determine free energies maps of functional groups of these LBPs, a Grand-Canonical Monte Carlo/Molecular Dynamics (GCMC/MD) strategy is combined with the Site Identification by Ligand Competitive Saturation (SILCS) methodology. SILCS-GCMC/MD is shown to map functional group affinity patterns that recapitulate locations of functional groups across diverse classes of ligands in the LBPs of the androgen (AR) and peroxisome proliferator-activated-γ (PPARγ) NRs and the metabotropic glutamate (mGluR) and β2-adreneric (β2AR) GPCRs. Inclusion of protein flexibility identifies regions of the binding pockets not accessible in crystal conformations and allows for better quantitative estimates of relative ligand binding affinities in all the proteins tested. Differences in functional group requirements of the active and inactive states of the β2AR LBP were used in virtual screening to identify high efficacy agonists targeting β2AR in Airway Smooth Muscle (ASM) cells. Seven of the 15 selected ligands were found to effect ASM relaxation representing a 46% hit rate. Hence, the method will be of use for the rational design of ligands in the context of chemical biology and the development of therapeutic agents.

Pharmacophore modeling using site-identification by ligand competitive saturation (SILCS) with multiple probe molecules.

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 molecular dynamics 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, reranking 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.

Site Identification by Ligand Competitive Saturation (SILCS) simulations for fragment-based drug design.

Fragment-based drug design (FBDD) involves screening low molecular weight molecules ("fragments") that correspond to functional groups found in larger drug-like molecules to determine their binding to target proteins or nucleic acids. Based on the principle of thermodynamic additivity, two fragments that bind nonoverlapping nearby sites on the target can be combined to yield a new molecule whose binding free energy is the sum of those of the fragments. Experimental FBDD approaches, like NMR and X-ray crystallography, have proven very useful but can be expensive in terms of time, materials, and labor. Accordingly, a variety of computational FBDD approaches have been developed that provide different levels of detail and accuracy.The Site Identification by Ligand Competitive Saturation (SILCS) method of computational FBDD uses all-atom explicit-solvent molecular dynamics (MD) simulations to identify fragment binding. The target is "soaked" in an aqueous solution with multiple fragments having different identities. The resulting computational competition assay reveals what small molecule types are most likely to bind which regions of the target. From SILCS simulations, 3D probability maps of fragment binding called "FragMaps" can be produced. Based on the probabilities relative to bulk, SILCS FragMaps can be used to determine "Grid Free Energies (GFEs)," which provide per-atom contributions to fragment binding affinities. For essentially no additional computational overhead relative to the production of the FragMaps, GFEs can be used to compute Ligand Grid Free Energies (LGFEs) for arbitrarily complex molecules, and these LGFEs can be used to rank-order the molecules in accordance with binding affinities.

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

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 molecular dynamics 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, reranking 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.

Mapping Functional Group Requirements of Ligands at the Occluded Binding Pocket of β2-Adrenergic G-Protein Coupled Receptor using Site Identification by Ligand Competitive Saturation Simulations

β2-adrenergic G-protein coupled receptor (B2AR) is an important therapeutic target for obstructive pulmonary diseases. The ligand binding pocket (LBP) in this and a number of other drug targets is deeply buried, offering significant challenges to computer-aided drug design approaches. Site Identification by Ligand Competitive Saturation (SILCS) is an in-situ fragment sampling method that maps the spatial distributions and approximate affinities of chemically diverse functional groups on a macromolecule through molecular dynamics (MD) simulations of the macromolecule in an aqueous solution of small molecules. Notable is the simultaneous inclusion of waters and protein flexibility such that the method accounts for ligand and binding site desolvation when mapping the affinity patterns of the different fragments (FragMaps). To probe an occluded LBP, a novel Grand-Canonical Monte-Carlo/MD (GCMC/MD) strategy is extended to the SILCS methodology. GCMC drives the small molecules and explicit solvent sampling of the occluded pocket, and the MD allows for the conformational sampling of the macromolecule in the presence of the small molecules, which is useful in identifying regions in the LBP that were inaccesible in the crystal conformation. Good agreement is obtained between the FragMaps and the positions of chemically similar functional groups of ligands observed in the crystal structures of B2AR. Ligand grid free energy (LGFE), an approximate estimation of binding affinity derived from FragMaps had good correlation with the experimentally measured binding affinity for diverse ligands. Clear differences were found in the FragMaps at the LBP across the activated and inactivated conformations of the B2AR. These differences were useful in determining the nature of a ligand that would preferentially bind to one of the two states and consequently guide drug design.

Site-Identification by Ligand Competitive Saturation (SILCS) assisted pharmacophore modeling

Database screening using receptor-based pharmacophores is a computer-aided drug design technique that uses the structure of the target molecule (i.e. protein) to identify novel ligands that may bind to the target. Typically receptor-based pharmacophore modeling methods only consider a single or limited number of receptor conformations and map out the favorable binding patterns in vacuum or with a limited representation of the aqueous solvent environment, such that they may suffer from neglect of protein flexibility and desolvation effects. Site-Identification by Ligand Competitive Saturation (SILCS) is an approach that takes into account these, as well as other, properties to determine 3-dimensional maps of the functional group-binding patterns on a target receptor (i.e. FragMaps). In this study, a method to use the FragMaps to automatically generate receptor-based pharmacophore models is presented. It converts the FragMaps into SILCS pharmacophore features including aromatic, aliphatic, hydrogen-bond donor and acceptor chemical functionalities. The method generates multiple pharmacophore hypotheses that are then quantitatively ranked using SILCS grid free energies. The pharmacophore model generation protocol is validated using three different protein targets, including using the resulting models in virtual screening. Improved performance and efficiency of the SILCS derived pharmacophore models as compared to published docking studies, as well as a recently developed receptor-based pharmacophore modeling method is shown, indicating the potential utility of the approach in rational drug design.

Inclusion of Multiple Fragment Types in the Site Identification by Ligand Competitive Saturation (SILCS) Approach

The site identification by ligand competitive saturation (SILCS) method identifies the location and approximate affinities of small molecular fragments on a target macromolecular surface by performing molecular dynamics (MD) simulations of the target in an aqueous solution of small molecules representative of different chemical functional groups. In this study, we introduce a set of small molecules to map potential interactions made by neutral hydrogen bond donors and acceptors and charged donor and acceptor fragments in addition to nonpolar fragments. The affinity pattern is obtained in the form of discretized probability or, equivalently, free energy maps, called FragMaps, which can be visualized with the target surface. We performed SILCS simulations for four proteins for which structural and thermodynamic data is available for multiple diverse ligands. Good overlap is shown between high affinity regions identified by the FragMaps and the crystallographic positions of ligand functional groups with similar chemical functionality, thus demonstrating the validity of the qualitative information obtained from the simulations. To test the ability of FragMaps in providing quantitative predictions, we calculate the previously introduced ligand grid free energy (LGFE) metric and observe its correspondence with experimentally measured binding affinity. LGFE is computed for different conformational ensembles and improvement in prediction is shown with increasing ligand conformational sampling. Ensemble generation includes a Monte Carlo sampling approach that uses the GFE FragMaps directly as the energy function. The results show that some but not all experimental trends are predicted and warrant improvements in the scoring methodology. In addition, the potential utility of atom-based free energy contributions to the LGFE scores and the use of multiple ligands in SILCS to identify displaceable water molecules during ligand design are discussed.

Reproducing crystal binding modes of ligand functional groups using Site-Identification by Ligand Competitive Saturation (SILCS) simulations.

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 toward 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.

Protein Affinity Pattern Calculations using Protein-Fragment Site Identification by Ligand Competitive Saturation (SILCS)

We demonstrate the applicability of a computational method, Site Identification by Ligand Competitive Saturation (SILCS) to identify regions on a protein surface with which different classes of functional groups interact. The method involves MD simulations of a protein in an aqueous solution of chemically diverse small molecules. 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 hydrogens and oxygen serve as probes for hydrogen bond donor and acceptor functionality, respectively. The method is tested using a set of proteins for which crystal structures of complexes with several high affinity inhibitors are known. SILCS simulations are performed for these proteins and the affinity pattern is obtained as 3D probability distributions of fragment atom types on a 3D-grid surrounding the protein called “FragMaps”. Good agreement is obtained between FragMaps of each type and the positions of chemically similar functional group in inhibitors as observed in the Xray crystallographic structures. For proteins for which inhibitor decoy sets are available, we demonstrate the statistical significance of the SILCS predictions by showing a significantly higher degree of overlap of the ligand atoms in the experimental conformation with the FragMaps. For a few test cases, we correlate the extent of overlap of the ligand functional groups with FragMaps to the experimental binding affinities. SILCS is also shown to capture the subtle differences in protein affinity across homologs, information which may be of utility towards specificity-guided drug design. Taken together, our results suggest that SILCS can recapitulate the location of functional groups of bound inhibitors, suggesting that the method may be of utility for rational drug design.

Optimization of a BCL6 Inhibitor using the Site-Identification by Ligand Competitive Saturation (SILCS)

Fragment-based drug design has developed significantly over the past ten years and is now recognized as a successful method of lead compound generation and optimization[1]. Computational approaches to fragment-based drug discovery have the potential to dramatically mitigate the costs of experimental based approach. However, the majority of computational methods suffer from limited representation of protein flexibility and solvation effects. Recently, a fragment-based approach based on explicit solvent all-atom molecular dynamics simulations (SILCS: Site Identification by Ligand Competitive Saturation) was developed in our lab to overcome these drawbacks[2]. As a test case, we applied this method to the BTB domain of BCL6 protein. Good agreements between calculated three-dimensional probability maps of fragment binding and the X-ray structure of the BCL6 inhibitor 79-6[3] were found. In addition, a more specific SILCS simulation was performed and several potential functional group binding sites around the location of 79-6 were identified. Based on the location of these functional groups, modifications of 79-6 were proposed. Free energy perturbation (FEP) calculations, including FEP using the orthogonal space random walk (OSRW) approach[4], were applied to obtain quantitative computational estimates of the relative free energy of binding associated with these modifications. Two FEP methods show consistent results that the modified compound has higher binding affinity to BCL6 than parent compound 79-6. This result indicates that the SILCS method has the ability to qualitatively inform the optimization of small-molecule inhibitors.[1] Schulz, M.; Hubbard, R. Curr. Opin. Pharmacol. 2009, 5, 615.[2] Guvench, O.; MacKerell, A. D., Jr. PLoS Comput. Biol. 2009, 5, e1000435.[3] Cerchietti, L. C.; Ghetu, A. F.; Zhu, X. and et al. Cancer Cell 2010, 17, 1.[4] Zheng, L.; Chen, M.; Yang, W. Proc. Natl. Acad. Sci. U.S.A. 2008, 105, 20227.