Browsing by Author "Li, Liwei"

Now showing 1 - 8 of 8
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    BioDrugScreen: a computational drug design resource for ranking molecules docked to the human proteome
    (Oxford University Press, 2009-11-18) Li, Liwei; Bum-Erdene, Khuchtumur; Baenziger, Peter H.; Rosen, Joshua J.; Hemmert, Jamison R.; Nellis, Joy A.; Pierce, Marlon E.; Meroueh, Samy O.; Biochemistry and Molecular Biology, School of Medicine
    BioDrugScreen is a resource for ranking molecules docked against a large number of targets in the human proteome. Nearly 1600 molecules from the freely available NCI diversity set were docked onto 1926 cavities identified on 1589 human targets resulting in >3 million receptor–ligand complexes requiring >200 000 cpu-hours on the TeraGrid. The targets in BioDrugScreen originated from Human Cancer Protein Interaction Network, which we have updated, as well as the Human Druggable Proteome, which we have created for the purpose of this effort. This makes the BioDrugScreen resource highly valuable in drug discovery. The receptor–ligand complexes within the database can be ranked using standard and well-established scoring functions like AutoDock, DockScore, ChemScore, X-Score, GoldScore, DFIRE and PMF. In addition, we have scored the complexes with more intensive GBSA and PBSA approaches requiring an additional 120 000 cpu-hours on the TeraGrid. We constructed a simple interface to enable users to view top-ranking molecules and access purchasing and other information for further experimental exploration.
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    Discovery and characterization of small molecules that target the Ral GTPase
    (Nature Publishing Group, 2014-11-20) Yan, Chao; Liu, Degang; Li, Liwei; Wempe, Michael F.; Guin, Sunny; Khanna, May; Meier, Jeremy; Hoffman, Brenton; Owens, Charles; Wysoczynski, Christina L.; Nitz, Matthew D.; Knabe, Eric W.; Brautigan, David L.; Paschal, Bryce M.; Schwartz, Martin A.; Jones, David; Ross, David; Meroueh, Samy O.; Theodorescu, Dan; Department of Biochemistry & Molecular Biology, IU School of Medicine
    The Ras-like GTPases RalA and B are important drivers of tumor growth and metastasis. Chemicals that block Ral function would be valuable as research tools and for cancer therapeutics. Here, we used protein structure analysis and virtual screening to identify drug-like molecules that bind a site on the GDP-form of Ral. Compounds RBC6, RBC8 and RBC10 inhibited Ral binding to its effector RalBP1, Ral-mediated cell spreading in murine fibroblasts and anchorage-independent growth of human cancer cell lines. Binding of RBC8 derivative BQU57 to RalB was confirmed by isothermal titration calorimetry, surface plasma resonance and 15N-HSQC NMR. RBC8 and BQU57 show selectivity for Ral relative to Ras or Rho and inhibit xenograft tumor growth similar to depletion of Ral by siRNA. Our results show the utility of structure-based discovery for development of therapeutics for Ral-dependent cancers.
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    Enrichment of Chemical Libraries Docked to Protein Conformational Ensembles and Application to Aldehyde Dehydrogenase 2
    (American Chemical Society, 2014-07-28) Wang, Bo; Buchman, Cameron D.; Li, Liwei; Hurley, Thomas D.; Meroueh, Samy O.; Department of Biochemistry & Molecular Biology, IU School of Medicine
    Molecular recognition is a complex process that involves a large ensemble of structures of the receptor and ligand. Yet, most structure-based virtual screening is carried out on a single structure typically from X-ray crystallography. Explicit-solvent molecular dynamics (MD) simulations offer an opportunity to sample multiple conformational states of a protein. Here we evaluate our recently developed scoring method SVMSP in its ability to enrich chemical libraries docked to MD structures of seven proteins from the Directory of Useful Decoys (DUD). SVMSP is a target-specific rescoring method that combines machine learning with statistical potentials. We find that enrichment power as measured by the area under the ROC curve (ROC-AUC) is not affected by increasing the number of MD structures. Among individual MD snapshots, many exhibited enrichment that was significantly better than the crystal structure, but no correlation between enrichment and structural deviation from crystal structure was found. We followed an innovative approach by training SVMSP scoring models using MD structures (SVMSPMD). The resulting models were applied to two difficult cases (p38 and CDK2) for which enrichment was not better than random. We found remarkable increase in enrichment power, particularly for p38, where the ROC-AUC increased by 0.30 to 0.85. Finally, we explored approaches for a priori identification of MD snapshots with high enrichment power from an MD simulation in the absence of active compounds. We found that the use of randomly selected compounds docked to the target of interest using SVMSP led to notable enrichment for EGFR and Src MD snapshots. SVMSP rescoring of protein–compound MD structures was applied for the search of small-molecule inhibitors of the mitochondrial enzyme aldehyde dehydrogenase 2 (ALDH2). Rank-ordering of a commercial library of 50 000 compounds docked to MD structures of ALDH2 led to five small-molecule inhibitors. Four compounds had IC50s below 5 μM. These compounds serve as leads for the design and synthesis of more potent and selective ALDH2 inhibitors.
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    Exploring a structural protein-drug interactome for new therapeutics in lung cancer
    (Royal Society of Chemistry, 2014-03-04) Peng, Xiaodong; Wang, Fang; Li, Liwei; Bum-Erdene, Khuchtumur; Xu, David; Wang, Bo; Sinn, Tony; Pollok, Karen; Sandusky, George; Li, Lang; Turchi, John; Jalal, Shadia I.; Meroueh, Samy; Department of Biochemistry & Molecular Biology, IU School of Medicine
    The pharmacology of drugs is often defined by more than one protein target. This property can be exploited to use approved drugs to uncover new targets and signaling pathways in cancer. Towards enabling a rational approach to uncover new targets, we expand a structural protein-ligand interactome () by scoring the interaction among 1000 FDA-approved drugs docked to 2500 pockets on protein structures of the human genome. This afforded a drug-target network whose properties compared favorably with previous networks constructed using experimental data. Among drugs with the highest degree and betweenness two are cancer drugs and one is currently used for treatment of lung cancer. Comparison of predicted cancer and non-cancer targets reveals that the most cancer-specific compounds were also the most selective compounds. Analysis of compound flexibility, hydrophobicity, and size showed that the most selective compounds were low molecular weight fragment-like heterocycles. We use a previously-developed screening approach using the cancer drug erlotinib as a template to screen other approved drugs that mimic its properties. Among the top 12 ranking candidates, four are cancer drugs, two of them kinase inhibitors (like erlotinib). Cellular studies using non-small cell lung cancer (NSCLC) cells revealed that several drugs inhibited lung cancer cell proliferation. We mined patient records at the Regenstrief Medical Record System to explore the possible association of exposure to three of these drugs with occurrence of lung cancer. Preliminary in vivo studies using the non-small cell lung cancer (NCLSC) xenograft model showed that losartan- and astemizole-treated mice had tumors that weighed 50 (p < 0.01) and 15 (p < 0.01) percent less than the treated controls. These results set the stage for further exploration of these drugs and to uncover new drugs for lung cancer therapy.
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    Molecular Recognition in a Diverse Set of Protein-Ligand Interactions Studied with Molecular Dynamics Simulations and End-Point Free Energy Calculations
    (ACS Publications, 2013-10-28) Wang, Bo; Li, Liwei; Hurley, Thomas D.; Meroueh, Samy O.; Department of Biochemistry & Molecular Biology, School of Medicine
    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 non-polar and entropy components, rather than a better representation of the electrostatics. 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 pre-scoring 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 non-polar 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.
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    Probing binding and cellular activity of pyrrolidinone and piperidinone small molecules targeting the urokinase receptor
    (Wiley, 2013-12) Mani, Timmy; Liu, Degang; Zhou, Donghui; Li, Liwei; Knabe, William Eric; Wang, Fang; Oh, Kyungsoo; Meroueh, Samy O.; Biochemistry & Molecular Biology, School of Medicine
    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 (Ki ) 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.
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    Structure-Based Target-Specific Screening Leads to Small-Molecule CaMKII Inhibitors
    (Wiley, 2017-05-09) Xu, David; Li, Liwei; Zhou, Donghui; Liu, Degang; Hudmon, Andy; Meroueh, Samy O.; BioHealth Informatics, School of Informatics and Computing
    Target-specific scoring methods are more commonly used to identify small-molecule inhibitors among compounds docked to a target of interest. Top candidates that emerge from these methods have rarely been tested for activity and specificity across a family of proteins. In this study we docked a chemical library into CaMKIIδ, a member of the Ca2+ /calmodulin (CaM)-dependent protein kinase (CaMK) family, and re-scored the resulting protein-compound structures using Support Vector Machine SPecific (SVMSP), a target-specific method that we developed previously. Among the 35 selected candidates, three hits were identified, such as quinazoline compound 1 (KIN-1; N4-[7-chloro-2-[(E)-styryl]quinazolin-4-yl]-N1,N1-diethylpentane-1,4-diamine), which was found to inhibit CaMKIIδ kinase activity at single-digit micromolar IC50 . Activity across the kinome was assessed by profiling analogues of 1, namely 6 (KIN-236; N4-[7-chloro-2-[(E)-2-(2-chloro-4,5-dimethoxyphenyl)vinyl]quinazolin-4-yl]-N1,N1-diethylpentane-1,4-diamine), and an analogue of hit compound 2 (KIN-15; 2-[4-[(E)-[(5-bromobenzofuran-2-carbonyl)hydrazono]methyl]-2-chloro-6-methoxyphenoxy]acetic acid), namely 14 (KIN-332; N-[(E)-[4-(2-anilino-2-oxoethoxy)-3-chlorophenyl]methyleneamino]benzofuran-2-carboxamide), against 337 kinases. Interestingly, for compound 6, CaMKIIδ and homologue CaMKIIγ were among the top ten targets. Among the top 25 targets of 6, IC50 values ranged from 5 to 22 μm. Compound 14 was found to be not specific toward CaMKII kinases, but it does inhibit two kinases with sub-micromolar IC50 values among the top 25. Derivatives of 1 were tested against several kinases including several members of the CaMK family. These data afforded a limited structure-activity relationship study. Molecular dynamics simulations with explicit solvent followed by end-point MM-GBSA free-energy calculations revealed strong engagement of specific residues within the ATP binding pocket, and also changes in the dynamics as a result of binding. This work suggests that target-specific scoring approaches such as SVMSP may hold promise for the identification of small-molecule kinase inhibitors that exhibit some level of specificity toward the target of interest across a large number of proteins.
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    Virtual Screening Targeting the Urokinase Receptor, Biochemical and Cell-Based Studies, Synthesis, Pharmacokinetic Characterization, and Effect on Breast Tumor Metastasis
    (American Chemical Society, 2011-10-27) Wang, Fang; Li, Jing; Sinn, Anthony L.; Knabe, William Eric; Khanna, May; Jo, Inha; Silver, Jayne M.; Oh, Kyungsoo; Li, Liwei; Sandusky, George E.; Sledge, George W.; Nakshatri, Harikrishna; Jones, David R.; Pollok, Karen E.; Meroueh, Samy O.
    Virtual screening targeting the urokinase receptor (uPAR) led to (3R)-4-cyclohexyl-3-(hexahydrobenzo[d][1,3]dioxol-5-yl)-N-((hexahydrobenzo[d][1,3]dioxol-5-yl)methyl)butan-1-aminium 1 (IPR-1) and 4-(4-((3,5-dimethylcyclohexyl)carbamoyl)-2-(4-isopropylcyclohexyl)pyrazolidin-3-yl)piperidin-1-ium 3 (IPR-69). Synthesis of an analog 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 lead-like properties for 3, but not 2. Compound 3 administered orally reached peak concentration of nearly 40 μM with a half-life of about 2 hours. 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.