Samy Meroueh

Name:

Organization: Indiana University School of Medicine

Department: Department of Biochemistry and Molecular Biology

Title:

Chemicals
References

Molecular Recognition in a Diverse Set of Protein–Ligand Interactions Studied with Molecular Dynamics Simulations and End-Point Free Energy Calculations

Co-reporter:Bo Wang, Liwei Li, Thomas D. Hurley, and Samy O. Meroueh

Journal of Chemical Information and Modeling 2013 Volume 53(Issue 10) pp:2659-2670

Publication Date(Web):September 15, 2013

DOI:10.1021/ci400312v

End-point free energy calculations using MM-GBSA and MM-PBSA provide a detailed understanding of molecular recognition in protein–ligand interactions. The binding free energy can be used to rank-order protein–ligand structures in virtual screening for compound or target identification. Here, we carry out free energy calculations for a diverse set of 11 proteins bound to 14 small molecules using extensive explicit-solvent MD simulations. The structure of these complexes was previously solved by crystallography and their binding studied with isothermal titration calorimetry (ITC) data enabling direct comparison to the MM-GBSA and MM-PBSA calculations. Four MM-GBSA and three MM-PBSA calculations reproduced the ITC free energy within 1 kcal·mol–1 highlighting the challenges in reproducing the absolute free energy from end-point free energy calculations. MM-GBSA exhibited better rank-ordering with a Spearman ρ of 0.68 compared to 0.40 for MM-PBSA with dielectric constant (ε = 1). An increase in ε resulted in significantly better rank-ordering for MM-PBSA (ρ = 0.91 for ε = 10), but larger ε significantly reduced the contributions of electrostatics, suggesting that the improvement is due to the nonpolar and entropy components, rather than a better representation of the electrostatics. The SVRKB scoring function applied to MD snapshots resulted in excellent rank-ordering (ρ = 0.81). Calculations of the configurational entropy using normal-mode analysis led to free energies that correlated significantly better to the ITC free energy than the MD-based quasi-harmonic approach, but the computed entropies showed no correlation with the ITC entropy. When the adaptation energy is taken into consideration by running separate simulations for complex, apo, and ligand (MM-PBSAADAPT), there is less agreement with the ITC data for the individual free energies, but remarkably good rank-ordering is observed (ρ = 0.89). Interestingly, filtering MD snapshots by prescoring protein–ligand complexes with a machine learning-based approach (SVMSP) resulted in a significant improvement in the MM-PBSA results (ε = 1) from ρ = 0.40 to ρ = 0.81. Finally, the nonpolar components of MM-GBSA and MM-PBSA, but not the electrostatic components, showed strong correlation to the ITC free energy; the computed entropies did not correlate with the ITC entropy.

Small-molecule inhibition of the uPAR·uPA interaction: Synthesis, biochemical, cellular, in vivo pharmacokinetics and efficacy studies in breast cancer metastasis

Co-reporter:Timmy Mani, Fang Wang, William Eric Knabe, Anthony L. Sinn, May Khanna, Inha Jo, George E. Sandusky, George W. Sledge Jr., David R. Jones, Rajesh Khanna, Karen E. Pollok, Samy O. Meroueh

Bioorganic & Medicinal Chemistry 2013 Volume 21(Issue 7) pp:2145-2155

Publication Date(Web):1 April 2013

DOI:10.1016/j.bmc.2012.12.047

The uPAR·uPA protein–protein interaction (PPI) is involved in signaling and proteolytic events that promote tumor invasion and metastasis. A previous study had identified 4 (IPR-803) from computational screening of a commercial chemical library and shown that the compound inhibited uPAR·uPA PPI in competition biochemical assays and invasion cellular studies. Here, we synthesize 4 to evaluate in vivo pharmacokinetic (PK) and efficacy studies in a murine breast cancer metastasis model. First, we show, using fluorescence polarization and saturation transfer difference (STD) NMR, that 4 binds directly to uPAR with sub-micromolar affinity of 0.2 μM. We show that 4 blocks invasion of breast MDA-MB-231, and inhibits matrix metalloproteinase (MMP) breakdown of the extracellular matrix (ECM). Derivatives of 4 also inhibited MMP activity and blocked invasion in a concentration-dependent manner. Compound 4 also impaired MDA-MB-231 cell adhesion and migration. Extensive in vivo PK studies in NOD-SCID mice revealed a half-life of nearly 5 h and peak concentration of 5 μM. Similar levels of the inhibitor were detected in tumor tissue up to 10 h. Female NSG mice inoculated with highly malignant TMD-MDA-MB-231 in their mammary fat pads showed that 4 impaired metastasis to the lungs with only four of the treated mice showing severe or marked metastasis compared to ten for the untreated mice. Compound 4 is a promising template for the development of compounds with enhanced PK parameters and greater efficacy.

Probing Binding and Cellular Activity of Pyrrolidinone and Piperidinone Small Molecules Targeting the Urokinase Receptor

Co-reporter:Dr. Timmy Mani;Dr. Degang Liu;Dr. Donghui Zhou;Dr. Liwei Li;Dr. William Eric Knabe;Fang Wang; Kyungsoo Oh; Samy O. Meroueh

ChemMedChem 2013 Volume 8( Issue 12) pp:1963-1977

Publication Date(Web):

DOI:10.1002/cmdc.201300340

Abstract

The urokinase receptor (uPAR) is a cell-surface protein that is part of an intricate web of transient and tight protein interactions that promote cancer cell invasion and metastasis. Here, we evaluate the binding and biological activity of a new class of pyrrolidinone and piperidinone compounds, along with derivatives of previously-identified pyrazole and propylamine compounds. Competition assays revealed that the compounds displace a fluorescently labeled peptide (AE147-FAM) with inhibition constant (K_i) values ranging from 6 to 63 μM. Structure-based computational pharmacophore analysis followed by extensive explicit-solvent molecular dynamics (MD) simulations and free energy calculations suggested the pyrazole-based and piperidinone-based compounds adopt different binding modes, despite their similar two-dimensional structures. In cells, pyrazole-based compounds showed significant inhibition of breast adenocarcinoma (MDA-MB-231) and pancreatic ductal adenocarcinoma (PDAC) cell proliferation, but piperidinone-containing compounds exhibited no cytotoxicity even at concentrations of 100 μM. One pyrazole-based compound impaired MDA-MB-231 invasion, adhesion, and migration in a concentration-dependent manner, while the piperidinone inhibited only invasion. The pyrazole derivative inhibited matrix metalloprotease-9 (gelatinase) activity in a concentration-dependent manner, while the piperidinone showed no effect suggesting different mechanisms for inhibition of cell invasion. Signaling studies further highlighted these differences, showing that pyrazole compounds completely inhibited ERK phosphorylation and impaired HIF1α and NF-κB signaling, while pyrrolidinones and piperidinones had no effect. Annexin V staining suggested that the effect of the pyrazole-based compound on proliferation was due to cell killing through an apoptotic mechanism. The compounds identified represent valuable leads in the design of further derivatives with higher affinities and potential probes to unravel the protein–protein interactions of uPAR.

Design, synthesis, biochemical studies, cellular characterization, and structure-based computational studies of small molecules targeting the urokinase receptor

Co-reporter:Fang Wang, W. Eric Knabe, Liwei Li, Inha Jo, Timmy Mani, Hartmut Roehm, Kyungsoo Oh, Jing Li, May Khanna, Samy O. Meroueh

Bioorganic & Medicinal Chemistry 2012 Volume 20(Issue 15) pp:4760-4773

Publication Date(Web):1 August 2012

DOI:10.1016/j.bmc.2012.06.002

The urokinase receptor (uPAR) serves as a docking site to the serine protease urokinase-type plasminogen activator (uPA) to promote extracellular matrix (ECM) degradation and tumor invasion and metastasis. Previously, we had reported a small molecule inhibitor of the uPAR·uPA interaction that emerged from structure-based virtual screening. Here, we measure the affinity of a large number of derivatives from commercial sources. Synthesis of additional compounds was carried out to probe the role of various groups on the parent compound. Extensive structure-based computational studies suggested a binding mode for these compounds that led to a structure–activity relationship study. Cellular studies in non-small cell lung cancer (NSCLC) cell lines that include A549, H460 and H1299 showed that compounds blocked invasion, migration and adhesion. The effects on invasion of active compounds were consistent with their inhibition of uPA and MMP proteolytic activity. These compounds showed weak cytotoxicity consistent with the confined role of uPAR to metastasis.

Virtual Screening Targeting the Urokinase Receptor, Biochemical and Cell-Based Studies, Synthesis, Pharmacokinetic Characterization, and Effect on Breast Tumor Metastasis

Co-reporter:Fang Wang ; Jing Li ; Anthony L. Sinn ; W. Eric Knabe ; May Khanna ; Inha Jo ; Jayne M. Silver ; Kyungsoo Oh ; Liwei Li ; George E. Sandusky ; George W. Sledge ; Jr.◆●; Harikrishna Nakshatri ●; David R. Jones ◆; Karen E. Pollok ;Samy O. Meroueh ●+

Journal of Medicinal Chemistry 2011 Volume 54(Issue 20) pp:7193-7205

Publication Date(Web):August 18, 2011

DOI:10.1021/jm200782y

Virtual screening targeting the urokinase receptor (uPAR) led to (±)-3-(benzo[d][1,3]dioxol-5-yl)-N-(benzo[d][1,3]dioxol-5-ylmethyl)-4-phenylbutan-1-amine 1 (IPR-1) and N-(3,5-dimethylphenyl)-1-(4-isopropylphenyl)-5-(piperidin-4-yl)-1H-pyrazole-4-carboxamide 3 (IPR-69). Synthesis of an analogue of 1, namely, 2 (IPR-9), and 3 led to breast MDA-MB-231 invasion, migration and adhesion assays with IC50 near 30 μM. Both compounds blocked angiogenesis with IC50 of 3 μM. Compounds 2 and 3 inhibited cell growth with IC50 of 6 and 18 μM and induced apoptosis. Biochemical assays revealed leadlike properties for 3, but not 2. Compound 3 administered orally reached peak concentration of nearly 40 μM with a half-life of about 2 h. In NOD-SCID mice inoculated with breast TMD-231 cells in their mammary fat pads, compound 3 showed a 20% reduction in tumor volumes and less extensive metastasis was observed for the treated mice. The suitable pharmacokinetic properties of 3 and the encouraging preliminary results in metastasis make it an ideal starting point for next generation compounds.

Targeting Multiple Conformations Leads to Small Molecule Inhibitors of the uPAR·uPA Protein–Protein Interaction That Block Cancer Cell Invasion

Co-reporter:May Khanna, Fang Wang, Inha Jo, W. Eric Knabe, Sarah M. Wilson, Liwei Li, Khuchtumur Bum-Erdene, Jing Li, George W. Sledge Jr., Rajesh Khanna, and Samy O. Meroueh

ACS Chemical Biology 2011 Volume 6(Issue 11) pp:1232

Publication Date(Web):August 29, 2011

DOI:10.1021/cb200180m

Interaction of the urokinase receptor (uPAR) with its binding partners such as the urokinase-type plasminogen activator (uPA) at the cell surface triggers a series of proteolytic and signaling events that promote invasion and metastasis. Here, we report the discovery of a small molecule (IPR-456) and its derivatives that inhibit the tight uPAR·uPA protein–protein interaction. IPR-456 was discovered by virtual screening against multiple conformations of uPAR sampled from explicit-solvent molecular dynamics simulations. Biochemical characterization reveal that the compound binds to uPAR with submicromolar affinity (Kd = 310 nM) and inhibits the tight protein–protein interaction with an IC50 of 10 μM. Free energy calculations based on explicit-solvent molecular dynamics simulations suggested the importance of a carboxylate moiety on IPR-456, which was confirmed by the activity of several derivatives including IPR-803. Immunofluorescence imaging showed that IPR-456 inhibited uPA binding to uPAR of breast MDA-MB-231 tumor cells with an IC50 of 8 μM. The compounds blocked MDA-MB-231 cell invasion, but IPR-456 showed little effect on MDA-MB-231 migration and no effect on adhesion, suggesting that uPAR mediates these processes through its other binding partners.

Target-Specific Support Vector Machine Scoring in Structure-Based Virtual Screening: Computational Validation, In Vitro Testing in Kinases, and Effects on Lung Cancer Cell Proliferation

Co-reporter:Liwei Li, May Khanna, Inha Jo, Fang Wang, Nicole M. Ashpole, Andy Hudmon, and Samy O. Meroueh

Journal of Chemical Information and Modeling 2011 Volume 51(Issue 4) pp:755-759

Publication Date(Web):March 25, 2011

DOI:10.1021/ci100490w

We assess the performance of our previously reported structure-based support vector machine target-specific scoring function across 41 targets, 40 among them from the Directory of Useful Decoys (DUD). The area under the curve of receiver operating characteristic plots (ROC-AUC) revealed that scoring with SVM-SP resulted in consistently better enrichment over all target families, outperforming Glide and other scoring functions, most notably among kinases. In addition, SVM-SP performance showed little variation among protein classes, exhibited excellent performance in a test case using a homology model, and in some cases showed high enrichment even with few structures used to train a model. We put SVM-SP to the test by virtual screening 1125 compounds against two kinases, EGFR and CaMKII. Among the top 25 EGFR compounds, three compounds (1−3) inhibited kinase activity in vitro with IC50 of 58, 2, and 10 μM. In cell cultures, compounds 1−3 inhibited nonsmall cell lung carcinoma (H1299) cancer cell proliferation with similar IC50 values for compound 3. For CaMKII, one compound inhibited kinase activity in a dose-dependent manner among 20 tested with an IC50 of 48 μM. These results are encouraging given that our in-house library consists of compounds that emerged from virtual screening of other targets with pockets that are different from typical ATP binding sites found in kinases. In light of the importance of kinases in chemical biology, these findings could have implications in future efforts to identify chemical probes of kinases within the human kinome.

Support Vector Regression Scoring of Receptor–Ligand Complexes for Rank-Ordering and Virtual Screening of Chemical Libraries

Co-reporter:Liwei Li, Bo Wang, and Samy O. Meroueh

Journal of Chemical Information and Modeling 2011 Volume 51(Issue 9) pp:2132-2138

Publication Date(Web):July 5, 2011

DOI:10.1021/ci200078f

The community structure–activity resource (CSAR) data sets are used to develop and test a support vector machine-based scoring function in regression mode (SVR). Two scoring functions (SVR-KB and SVR-EP) are derived with the objective of reproducing the trend of the experimental binding affinities provided within the two CSAR data sets. The features used to train SVR-KB are knowledge-based pairwise potentials, while SVR-EP is based on physicochemical properties. SVR-KB and SVR-EP were compared to seven other widely used scoring functions, including Glide, X-score, GoldScore, ChemScore, Vina, Dock, and PMF. Results showed that SVR-KB trained with features obtained from three-dimensional complexes of the PDBbind data set outperformed all other scoring functions, including best performing X-score, by nearly 0.1 using three correlation coefficients, namely Pearson, Spearman, and Kendall. It was interesting that higher performance in rank ordering did not translate into greater enrichment in virtual screening assessed using the 40 targets of the Directory of Useful Decoys (DUD). To remedy this situation, a variant of SVR-KB (SVR-KBD) was developed by following a target-specific tailoring strategy that we had previously employed to derive SVM-SP. SVR-KBD showed a much higher enrichment, outperforming all other scoring functions tested, and was comparable in performance to our previously derived scoring function SVM-SP.

Docking to Erlotinib Off-Targets Leads to Inhibitors of Lung Cancer Cell Proliferation with Suitable in Vitro Pharmacokinetics

Co-reporter:Liwei Li, Jing Li, May Khanna, Inha Jo, Jason P. Baird and Samy O. Meroueh

ACS Medicinal Chemistry Letters 2010 Volume 1(Issue 5) pp:229

Publication Date(Web):May 25, 2010

DOI:10.1021/ml100031a

In an effort to develop a rational approach to identify anticancer agents with selective polypharmacology, we mined millions of docked protein−ligand complexes involving more than a thousand cancer targets from multiple signaling pathways to identify new structural templates for proven pharmacophores. Our method combines support vector machine-based scoring to enrich the initial library of 1592 molecules, with a fingerprint-based search for molecules that have the same binding profile as the EGFR kinase inhibitor erlotinib. Twelve new compounds were identified. In vitro activity assays revealed three inhibited EGFR with IC50 values ranging from 250 nM to 200 μM. Additional in vitro studies with hERG, CYP450, DNA, and cell culture-based assays further compared their properties to erlotinib. One compound combined suitable pharmacokinetic properties while closely mimicking the binding profile of erlotinib. The compound also inhibited H1299 and H460 tumor cell proliferation. The other two compounds shared some of the binding profile of erlotinib, and one gave the most potent inhibition of tumor cell growth. Interestingly, among the compounds that had not shown inhibition of EGFR, four blocked H1299 and H460 proliferation, one potently with IC50 values near 1 μM. This compound was from the menogaril family, which reached phase II clinical trials for the treatment of lymphomas. This suggests that our computational approach comparing binding profiles may have favored molecules with anticancer properties like erlotinib.Keywords: Docking; lung cancer; proteome; scoring; support vector machine; systems biology

Exploring the Molecular Design of Protein Interaction Sites with Molecular Dynamics Simulations and Free Energy Calculations

Co-reporter:Shide Liang, Liwei Li, Wei-Lun Hsu, Meaghan N. Pilcher, Vladimir Uversky, Yaoqi Zhou, A. Keith Dunker and Samy O. Meroueh

Biochemistry 2009 Volume 48(Issue 2) pp:

Publication Date(Web):December 29, 2008

DOI:10.1021/bi8017043

The significant work that has been invested toward understanding protein−protein interaction has not translated into significant advances in structure-based predictions. In particular redesigning protein surfaces to bind to unrelated receptors remains a challenge, partly due to receptor flexibility, which is often neglected in these efforts. In this work, we computationally graft the binding epitope of various small proteins obtained from the RCSB database to bind to barnase, lysozyme, and trypsin using a previously derived and validated algorithm. In an effort to probe the protein complexes in a realistic environment, all native and designer complexes were subjected to a total of nearly 400 ns of explicit-solvent molecular dynamics (MD) simulation. The MD data led to an unexpected observation: some of the designer complexes were highly unstable and decomposed during the trajectories. In contrast, the native and a number of designer complexes remained consistently stable. The unstable conformers provided us with a unique opportunity to define the structural and energetic factors that lead to unproductive protein−protein complexes. To that end we used free energy calculations following the MM-PBSA approach to determine the role of nonpolar effects, electrostatics and entropy in binding. Remarkably, we found that a majority of unstable complexes exhibited more favorable electrostatics than native or stable designer complexes, suggesting that favorable electrostatic interactions are not prerequisite for complex formation between proteins. However, nonpolar effects remained consistently more favorable in native and stable designer complexes reinforcing the importance of hydrophobic effects in protein−protein binding. While entropy systematically opposed binding in all cases, there was no observed trend in the entropy difference between native and designer complexes. A series of alanine scanning mutations of hot-spot residues at the interface of native and designer complexes showed less than optimal contacts of hot-spot residues with their surroundings in the unstable conformers, resulting in more favorable entropy for these complexes. Finally, disorder predictions revealed that secondary structures at the interface of unstable complexes exhibited greater disorder than the stable complexes.

PDBcal: A Comprehensive Dataset for ReceptorLigand Interactions with Three-dimensional Structures and Binding Thermodynamics from Isothermal Titration Calorimetry

Co-reporter:Liwei Li;Justin J. Dantzer;Jonathan Nowacki;Brian J. O’Callaghan;Samy O. Meroueh

Chemical Biology & Drug Design 2008 Volume 71( Issue 6) pp:529-532

Publication Date(Web):

DOI:10.1111/j.1747-0285.2008.00661.x

Compounds designed solely based on structure often do not result in any improvement of the binding affinity because of entropy–enthalpy compensation. Thermodynamic data along with structure provide an opportunity to gain a deeper understanding of this effect and aid in the refinement of scoring functions used in computational drug design. Here, we scoured the literature and constructed the most comprehensive hand-curated calorimetry dataset to date. It contains thermodynamic and structural data for more than 400 receptor–ligand complexes. The dataset can be accessed through a web interface at http://www.pdbcal.org. The thermodynamic data consists of free energy, enthalpy, entropy and heat capacity as measured by isothermal titration calorimetry (ITC). The dataset also contains the experimental conditions that were used to carry out the ITC experiments. The chemical structures of the ligands are also provided. Analysis of the data confirms the existence of enthalpy-entropy compensation effect for the first time using strictly ITC data.