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author:("Li, liuwei")
1.  Design, Synthesis, Biochemical Studies, Cellular Characterization, and Structure-Based Computational Studies of Small Molecules Targeting the Urokinase Receptor 
Bioorganic & medicinal chemistry  2012;20(15):4760-4773.
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.
doi:10.1016/j.bmc.2012.06.002
PMCID: PMC3437670  PMID: 22771232
2.  Targeting Multiple Conformations Leads to Small Molecule Inhibitors of the uPAR·uPA Protein-Protein Interaction that Block Cancer Cell Invasion 
ACS chemical biology  2011;6(11):1232-1243.
Interaction of the urokinase receptor (uPAR) with its binding partners including 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 sub-micromolar 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.
doi:10.1021/cb200180m
PMCID: PMC3220747  PMID: 21875078
Virtual screening; small molecule; protein-protein interaction; inhibitor; urokinase receptor; invasion; migration; metastasis; MDA-MB-231; cancer; breast cancer; urokinase-type plasminogen activator; uPAR; uPA; docking; scoring; flexible docking
3.  Virtual Screening Targeting the Urokinase Receptor, Biochemical and Cell-Based Studies, Synthesis, Pharmacokinetic Characterization, and Effect on Breast Tumor Metastasis 
Journal of Medicinal Chemistry  2011;54(20):7193-7205.
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.
doi:10.1021/jm200782y
PMCID: PMC3280887  PMID: 21851064
4.  Support Vector Regression Scoring of Receptor-Ligand Complexes for Rank-Ordering and Virtual Screening of Chemical Libraries 
The Community Structure-Activity Resource (CSAR) datasets are used 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 datasets. The features used to train SVR-KB are knowledge-based pairwise potentials, while SVR-EP is based on physico-chemical 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 dataset 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 much higher enrichment outperforming all other scoring functions tested, and was comparable in performance to our previously-derived scoring function SVM-SP.
doi:10.1021/ci200078f
PMCID: PMC3209528  PMID: 21728360
5.  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 
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 characteristic plots (ROC-AUC) revealed that scoring with SVMSP resulted in consistently better enrichment over all targets families and 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 1,125 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 culture, compounds 1–3 inhibited non-small 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.
doi:10.1021/ci100490w
PMCID: PMC3092157  PMID: 21438548
6.  Docking Small Molecules to Predicted Off-Targets of the Cancer Drug Erlotinib Leads to Inhibitors of Lung Cancer Cell Proliferation with Suitable In vitro Pharmacokinetic Properties 
ACS medicinal chemistry letters  2010;1(5):229-233.
In an effort to develop a rational approach to identify anti-cancer agents with selective polypharmacology, we mine 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 1,592 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 that 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 trial for the treatment of lymphomas. This suggests that our computational approach comparing binding profile may have favored molecules with anti-cancer properties like erlotinib.
doi:10.1021/ml100031a
PMCID: PMC2931832  PMID: 20824148
7.  Benzyl­triphenyl­phospho­nium perchlorate 
The asymmetric unit of the title compound, C25H22P+·ClO4 −, contains two independent cations and two independent anions. The closest inter­molecular contact is a weak inter­molecular C—H⋯π(arene) inter­action.
doi:10.1107/S1600536811021660
PMCID: PMC3151880  PMID: 21837041
8.  Analysis of Structured and Intrinsically Disordered Regions of Transmembrane Proteins 
Molecular bioSystems  2009;5(12):1688-1702.
Integral membrane proteins display two major types of transmembrane structures, helical bundles and beta barrels. The main functional roles of transmembrane proteins are the transport of small molecules and cell signaling, and sometimes these two roles are coupled. For cytosolic, water-soluble proteins, signaling and regulatory functions are often carried out by intrinsically disordered regions. Our long range goal is to determine whether integral membrane proteins likewise often use disordered regions for signaling and regulation. Here we carried out a systematic bioinformatics investigation of intrinsically disordered regions obtained from integral membrane proteins for which crystal structures have been determined, and for which the intrinsic disorder was identified as missing electron density. We found 120 disorder-containing integral membrane proteins having a total of 33,675 residues, with 3209 of the residues distributed among 240 different disordered regions. These disordered regions were compared with those obtained from water-soluble proteins with regard to their amino acid compositional biases, and with regard to accuracies of various disorder predictors. The results of these analyses show that the disordered regions from helical bundle integral membrane proteins, those from beta barrel integral membrane proteins, and those from water soluble proteins all exhibit statistically distinct amino acid compositional biases. Despite these differences in composition, current algorithms make reasonably accurate predictions of disorder for these membrane proteins. Although the small size of the current data sets are limiting, these results suggest that developing new predictors that make use of data from disordered regions in helical bundles and beta barrels, especially as these datasets increase in size, will likely lead to significantly more accurate disorder predictions for these two classes of integral membrane proteins.
doi:10.1039/B905913J
PMCID: PMC2887740  PMID: 19585006
9.  Identification of Immunodominant B- and T-Cell Combined Epitopes in Outer Membrane Lipoproteins LipL32 and LipL21 of Leptospira interrogans▿  
Leptospirosis is a serious infectious disease caused by pathogenic Leptospira. B- and T-cell-mediated immune responses contribute to the mechanisms of Leptospira interrogans infection and immune intervention. LipL32 and LipL21 are the conserved outer membrane lipoproteins of L. interrogans and are considered vaccine candidates. In this study, we identified B- and T-cell combined epitopes within LipL32 and LipL21 to further develop a novel vaccine. By using a computer prediction algorithm, two B- and T-cell combined epitopes of LipL21 and four of LipL32 were predicted. All of the predicted epitopes were expressed in a phage display system. Four epitopes, LipL21 residues 97 to 112 and 176 to 184 (LipL2197-112 and LipL21176-184, respectively) and LipL32133-160 and LipL32221-247 of LipL32 were selected as antigens by Western blotting and enzyme-linked immunosorbent assay. These selected epitopes were also recognized by CD4+ T lymphocytes derived from LipL21- or LipL32-immunized BALB/c (H-2d) mice and mainly polarized the immune response toward a Th1 phenotype. The identification of epitopes that have both B- and T-cell immune reactivities is of value for studying the immune mechanisms in response to leptospiral infection and for designing an effective vaccine for leptospirosis.
doi:10.1128/CVI.00405-09
PMCID: PMC2863375  PMID: 20237196
10.  Exploring the Molecular Design of Protein Interaction Sites with Molecular Dynamics Simulations and Free Energy Calculations† 
Biochemistry  2009;48(2):399-414.
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.
doi:10.1021/bi8017043
PMCID: PMC2754190  PMID: 19113835
11.  BioDrugScreen: a computational drug design resource for ranking molecules docked to the human proteome 
Nucleic Acids Research  2009;38(Database issue):D765-D773.
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.
doi:10.1093/nar/gkp852
PMCID: PMC2808957  PMID: 19923229

Results 1-11 (11)