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1.  Measuring the Effect of Inter-Study Variability on Estimating Prediction Error 
PLoS ONE  2014;9(10):e110840.
The biomarker discovery field is replete with molecular signatures that have not translated into the clinic despite ostensibly promising performance in predicting disease phenotypes. One widely cited reason is lack of classification consistency, largely due to failure to maintain performance from study to study. This failure is widely attributed to variability in data collected for the same phenotype among disparate studies, due to technical factors unrelated to phenotypes (e.g., laboratory settings resulting in “batch-effects”) and non-phenotype-associated biological variation in the underlying populations. These sources of variability persist in new data collection technologies.
Here we quantify the impact of these combined “study-effects” on a disease signature’s predictive performance by comparing two types of validation methods: ordinary randomized cross-validation (RCV), which extracts random subsets of samples for testing, and inter-study validation (ISV), which excludes an entire study for testing. Whereas RCV hardwires an assumption of training and testing on identically distributed data, this key property is lost in ISV, yielding systematic decreases in performance estimates relative to RCV. Measuring the RCV-ISV difference as a function of number of studies quantifies influence of study-effects on performance.
As a case study, we gathered publicly available gene expression data from 1,470 microarray samples of 6 lung phenotypes from 26 independent experimental studies and 769 RNA-seq samples of 2 lung phenotypes from 4 independent studies. We find that the RCV-ISV performance discrepancy is greater in phenotypes with few studies, and that the ISV performance converges toward RCV performance as data from additional studies are incorporated into classification.
We show that by examining how fast ISV performance approaches RCV as the number of studies is increased, one can estimate when “sufficient” diversity has been achieved for learning a molecular signature likely to translate without significant loss of accuracy to new clinical settings.
PMCID: PMC4201588  PMID: 25330348
2.  Novel small molecule inhibitors of Bcl-XL to treat lung cancer 
Cancer research  2013;73(17):5485-5496.
Bcl-XL is a major anti-apoptotic protein in the Bcl-2 family whose overexpression is more widely observed in human lung cancer cells than that of Bcl-2, suggesting that Bcl-XL is more biologically relevant and therefore a better therapeutic target for lung cancer. Here, we screened small molecules that selectively target the BH3 domain (aa 90–98) binding pocket of Bcl-XL using the UCSF DOCK 6.1 program suite and the NCI chemical library database. We identified two new Bcl-XL inhibitors (BXI-61 and BXI-72) that exhibit selective toxicity against lung cancer cells compared with normal human bronchial epithelial cells. Fluorescence polarization assay reveals that BXI-61 and BXI-72 preferentially bind to Bcl-XL protein but not Bcl2, Bcl-w, Bfl-1/A1 or Mcl-1 in vitro with high binding affinities. Treatment of cells with BXI-72 results in disruption of Bcl-XL/Bak or Bcl-XL/Bax interaction, oligomerization of Bak and cytochrome c release from mitochondria. Importantly, BXI-61 and BXI-72 exhibit more potent efficacy against human lung cancer than ABT-737 but less degree in platelet reduction in vivo. BXI-72 overcomes acquired radioresistance of lung cancer. Based on our findings, the development of BXI(s) as a new class of anticancer agents is warranted and represents a novel strategy for improving lung cancer outcome.
PMCID: PMC3774010  PMID: 23824742
3.  Inhibiting the interaction of cMET and IGF-1R with FAK effectively reduces growth of pancreatic cancer cells in vitro and in vivo 
Pancreatic cancer is one of the most lethal diseases with no effective treatment. Previously, we have shown that FAK is overexpressed in pancreatic cancer and plays a key role in cancer cell survival and proliferation. FAK has been shown to interact with growth factor receptors including cMET and IGF-1R. As a novel therapeutic approach, we targeted the protein interaction of FAK with growth factor receptors to block tumor growth, alter signaling pathways and sensitize cells to chemotherapy. We have selected a small molecule compound (INT2-31) that decreases phosphorylation of AKT via disrupting interaction of FAK with cMET and IGF-1R. Our results demonstrate that interaction of a small molecule compound with FAK decreases phosphorylation of FAK Y397 while increasing FAK Y407 phosphorylation, without inhibiting the kinase activity of FAK and dramatically reduces downstream signaling to AKT. Our lead compound, INT2-31, demonstrates significant inhibition of tumor cell growth in two orthotopic models of pancreatic cancer. In addition, INT2-31increases sensitivity to gemcitabine chemotherapy in a direct fresh biopsy xenograft model of pancreatic cancer growth.
PMCID: PMC4052463  PMID: 23272972
FAK; IGF-1R; pancreatic cancer; protein interactions
4.  Multi-study Integration of Brain Cancer Transcriptomes Reveals Organ-Level Molecular Signatures 
PLoS Computational Biology  2013;9(7):e1003148.
We utilized abundant transcriptomic data for the primary classes of brain cancers to study the feasibility of separating all of these diseases simultaneously based on molecular data alone. These signatures were based on a new method reported herein – Identification of Structured Signatures and Classifiers (ISSAC) – that resulted in a brain cancer marker panel of 44 unique genes. Many of these genes have established relevance to the brain cancers examined herein, with others having known roles in cancer biology. Analyses on large-scale data from multiple sources must deal with significant challenges associated with heterogeneity between different published studies, for it was observed that the variation among individual studies often had a larger effect on the transcriptome than did phenotype differences, as is typical. For this reason, we restricted ourselves to studying only cases where we had at least two independent studies performed for each phenotype, and also reprocessed all the raw data from the studies using a unified pre-processing pipeline. We found that learning signatures across multiple datasets greatly enhanced reproducibility and accuracy in predictive performance on truly independent validation sets, even when keeping the size of the training set the same. This was most likely due to the meta-signature encompassing more of the heterogeneity across different sources and conditions, while amplifying signal from the repeated global characteristics of the phenotype. When molecular signatures of brain cancers were constructed from all currently available microarray data, 90% phenotype prediction accuracy, or the accuracy of identifying a particular brain cancer from the background of all phenotypes, was found. Looking forward, we discuss our approach in the context of the eventual development of organ-specific molecular signatures from peripheral fluids such as the blood.
Author Summary
From a multi-study, integrated transcriptomic dataset, we identified a marker panel for differentiating major human brain cancers at the gene-expression level. The ISSAC molecular signatures for brain cancers, composed of 44 unique genes, are based on comparing expression levels of pairs of genes, and phenotype prediction follows a diagnostic hierarchy. We found that sufficient dataset integration across multiple studies greatly enhanced diagnostic performance on truly independent validation sets, whereas signatures learned from only one dataset typically led to high error rate. Molecular signatures of brain cancers, when obtained using all currently available gene-expression data, achieved 90% phenotype prediction accuracy. Thus, our integrative approach holds significant promise for developing organ-level, comprehensive, molecular signatures of disease.
PMCID: PMC3723500  PMID: 23935471
5.  Disruption of focal adhesion kinase and p53 interaction with small molecule compound R2 reactivated p53 and blocked tumor growth 
BMC Cancer  2013;13:342.
Focal Adhesion Kinase (FAK) is a 125 kDa non-receptor kinase that plays a major role in cancer cell survival and metastasis.
We performed computer modeling of the p53 peptide containing the site of interaction with FAK, predicted the peptide structure and docked it into the three-dimensional structure of the N-terminal domain of FAK involved in the complex with p53. We screened small molecule compounds that targeted the site of the FAK-p53 interaction and identified compounds (called Roslins, or R compounds) docked in silico to this site.
By different assays in isogenic HCT116p53+/+ and HCT116 p53-/- cells we identified a small molecule compound called Roslin 2 (R2) that bound FAK, disrupted the binding of FAK and p53 and decreased cancer cell viability and clonogenicity in a p53-dependent manner. In addition, dual-luciferase assays demonstrated that the R2 compound increased p53 transcriptional activity that was inhibited by FAK using p21, Mdm-2, and Bax-promoter targets. R2 also caused increased expression of p53 targets: p21, Mdm-2 and Bax proteins. Furthermore, R2 significantly decreased tumor growth, disrupted the complex of FAK and p53, and up-regulated p21 in HCT116 p53+/+ but not in HCT116 p53-/- xenografts in vivo. In addition, R2 sensitized HCT116p53+/+ cells to doxorubicin and 5-fluorouracil.
Thus, disruption of the FAK and p53 interaction with a novel small molecule reactivated p53 in cancer cells in vitro and in vivo and can be effectively used for development of FAK-p53 targeted cancer therapy approaches.
PMCID: PMC3712010  PMID: 23841915
Focal adhesion kinase; p53Cancer; Small molecule; p21; Tumor; Apoptosis
6.  A small molecule focal adhesion kinase (FAK) inhibitor, targeting Y397 site: 1-(2-hydroxyethyl) -3, 5, 7-triaza-1-azoniatricyclo [,7]decane; bromide effectively inhibits FAK autophosphorylation activity and decreases cancer cell viability, clonogenicity and tumor growth in vivo 
Carcinogenesis  2012;33(5):1004-1013.
Focal adhesion kinase (FAK) is a protein tyrosine kinase that is overexpressed in most solid types of tumors and plays an important role in the survival signaling. Recently, we have developed a novel computer modeling combined with a functional assay approach to target the main autophosphorylation site of FAK (Y397). Using these approaches, we identified 1-(2-hydroxyethyl)-3, 5, 7-triaza-1-azoniatricyclo [,7]decane; bromide, called Y11, a small molecule inhibitor targeting Y397 site of FAK. Y11 significantly and specifically decreased FAK autophosphorylation, directly bound to the N-terminal domain of FAK. In addition, Y11 decreased Y397-FAK autophosphorylation, inhibited viability and clonogenicity of colon SW620 and breast BT474 cancer cells and increased detachment and apoptosis in vitro. Moreover, Y11 significantly decreased tumor growth in the colon cancer cell mouse xenograft model. Finally, tumors from the Y11-treated mice demonstrated decreased Y397-FAK autophosphorylation and activation of poly (ADP ribose) polymerase and caspase-3. Thus, targeting the major autophosphorylation site of FAK with Y11 inhibitor is critical for development of cancer therapeutics and carcinogenesis field.
PMCID: PMC3334519  PMID: 22402131
7.  A Small-molecule Inhibitor, 5′-O-Tritylthymidine, targets FAK and Mdm-2 Interaction, and Blocks Breast and Colon Tumorigenesis in vivo 
Focal Adhesion Kinase (FAK) is overexpressed in many types of tumors and plays an important role in survival. We developed a novel approach, targeting FAK-protein interactions by computer modeling and screening of NCI small molecule drug database. In this report we targeted FAK and Mdm-2 protein interaction to decrease tumor growth. By macromolecular modeling we found a model of FAK and Mdm-2 interaction and performed screening of >200,000 small molecule compounds from NCI database with drug-like characteristics, targeting the FAK-Mdm-2 interaction. We identified 5′-O-Tritylthymidine, called M13 compound that significantly decreased viability in different cancer cells. M13 was docked into the pocket of FAK and Mdm-2 interaction and was directly bound to the FAK-N terminal domain by ForteBio Octet assay. In addition, M13 compound affected FAK and Mdm-2 levels and decreased complex of FAK and Mdm-2 proteins in breast and colon cancer cells. M13 re-activated p53 activity inhibited by FAK with Mdm-2 promoter. M13 decreased viability, clonogenicity, increased detachment and apoptosis in a dose-dependent manner in BT474 breast and in HCT116 colon cancer cells in vitro. M13 decreased FAK, activated p53 and caspase-8 in both cell lines. In addition, M13 decreased breast and colon tumor growth in vivo. M13 activated p53 and decreased FAK in tumor samples consistent with decreased tumor growth. The data demonstrate a novel approach for targeting FAK and Mdm-2 protein interaction, provide a model of FAK and Mdm-2 interaction, identify M13 compound targeting this interaction and decreasing tumor growth that is critical for future targeted therapeutics.
PMCID: PMC3625481  PMID: 22292771
Apoptosis; Focal Adhesion Kinase; Mdm-2; Small molecule compound; p53; Tumor growth
8.  Mitoxantrone Targets the ATP-binding Site of FAK, Binds the FAK Kinase Domain and Decreases FAK, Pyk-2, c-Src, and IGF-1R, In Vitro Kinase Activities 
Focal Adhesion Kinase (FAK) is a non-receptor kinase that is overexpressed in many types of tumors and plays a key role in cell adhesion, spreading, motility, proliferation, invasion, angiogenesis, and survival. Recently, FAK has been proposed as a target for cancer therapy, and we performed computer modeling and screening of the National Cancer Institute (NCI) small molecule compounds database to target the ATP-binding site of FAK, K454. More than 140,000 small molecule compounds were docked into the crystal structure of the kinase domain of FAK in 100 different orientations using DOCK5.1 that identified small molecule compounds, targeting the K454 site, called A-compounds. To find the therapeutic efficacy of these compounds, we examined the effect of twenty small molecule compounds on cell viability by MTT assays in different cancer cell lines. One compound, A18 (1,4-bis(diethylamino)-5,8-dihydroxy anthraquinon) was a mitoxantrone derivative and significantly decreased viability in most of the cells comparable to the to the level of FAK kinase inhibitors TAE-226 (Novartis, Inc) and PF-573,228 (Pfizer). The A18 compound specifically blocked autophosphorylation of FAK like TAE-226 and PF-228. ForteBio Octet Binding assay demonstrated that mitoxantrone (1,4-dihydroxy-5,8-bis[2-(2-hydroxyethylamino) ethylamino] anthracene-9,10-dione directly binds the FAK-kinase domain. In addition, mitoxantrone significantly decreased the viability of breast cancer cells in a dose-dependent manner and inhibited the kinase activity of FAK and Y56/577 FAK phosphorylation at 10-20 μM. Mitoxantrone did not affect phosphorylation of EGFR, but decreased Pyk-2, c-Src, and IGF-1R kinase activities. The data demonstrate that mitotraxone decreases cancer viability, binds FAK-Kinase domain, inhibits its kinase activity, and also inhibits in vitro kinase activities of Pyk-2 and IGF-1R. Thus, this novel function of the mitoxantrone drug can be critical for future development of anti-cancer agents and FAK-targeted therapy research.
PMCID: PMC3625494  PMID: 22292772
ATP; cancer; enzyme activity; FAK; Focal adhesion kinase; kinase; therapy
9.  AUREA: an open-source software system for accurate and user-friendly identification of relative expression molecular signatures 
BMC Bioinformatics  2013;14:78.
Public databases such as the NCBI Gene Expression Omnibus contain extensive and exponentially increasing amounts of high-throughput data that can be applied to molecular phenotype characterization. Collectively, these data can be analyzed for such purposes as disease diagnosis or phenotype classification. One family of algorithms that has proven useful for disease classification is based on relative expression analysis and includes the Top-Scoring Pair (TSP), k-Top-Scoring Pairs (k-TSP), Top-Scoring Triplet (TST) and Differential Rank Conservation (DIRAC) algorithms. These relative expression analysis algorithms hold significant advantages for identifying interpretable molecular signatures for disease classification, and have been implemented previously on a variety of computational platforms with varying degrees of usability. To increase the user-base and maximize the utility of these methods, we developed the program AUREA (Adaptive Unified Relative Expression Analyzer)—a cross-platform tool that has a consistent application programming interface (API), an easy-to-use graphical user interface (GUI), fast running times and automated parameter discovery.
Herein, we describe AUREA, an efficient, cohesive, and user-friendly open-source software system that comprises a suite of methods for relative expression analysis. AUREA incorporates existing methods, while extending their capabilities and bringing uniformity to their interfaces. We demonstrate that combining these algorithms and adaptively tuning parameters on the training sets makes these algorithms more consistent in their performance and demonstrate the effectiveness of our adaptive parameter tuner by comparing accuracy across diverse datasets.
We have integrated several relative expression analysis algorithms and provided a unified interface for their implementation while making data acquisition, parameter fixing, data merging, and results analysis ‘point-and-click’ simple. The unified interface and the adaptive parameter tuning of AUREA provide an effective framework in which to investigate the massive amounts of publically available data by both ‘in silico’ and ‘bench’ scientists. AUREA can be found at
PMCID: PMC3599560  PMID: 23496976
10.  Crystallization and preliminary X-ray analysis of the human long myosin light-chain kinase 1-specific domain IgCAM3 
To gain insight into the structure of the IgCAM3 domain, the IgCAM3 domain of MLCK1 has been expressed, purified and crystallized.
Myosin light-chain kinase-dependent tight junction regulation is a critical event in inflammatory cytokine-induced increases in epithelial paracellular permeability. MLCK is expressed in human intestinal epithelium as two isoforms, long MLCK1 and long MLCK2, and MLCK1 is specifically localized to the tight junction, where it regulates paracellular permeability. The sole difference between these long MLCK splice variants is the presence of an immunoglobulin-like cell-adhesion molecule domain, IgCAM3, in MLCK1. To gain insight into the structure of the IgCAM3 domain, the IgCAM3 domain of MLCK1 has been expressed, purified and crystallized. Preliminary X-ray diffraction data were collected to 2.0 Å resolution and were consistent with the primitive trigonal space group P212121.
PMCID: PMC3034612  PMID: 21301090
IgCAM3 domain; myosin light-chain kinase 1
11.  A46, a Benzothiophene Derived Compound, Suppresses Jak2-Mediated Pathologic Cell Growth 
Experimental hematology  2011;40(1):22-34.
Hyperkinetic Jak2 tyrosine kinase signaling has been implicated in several hematological disorders including the myeloproliferative neoplasms (MPNs). Effective Jak2 inhibitors can thus have significant therapeutic potential. Here, using structure based virtual screening, we identified a benzothiophene derived Jak2 inhibitor named A46. We hypothesized that this compound would inhibit Jak2-V617F mediated pathologic cell growth. To test this, A46 was analyzed for its ability to i) inhibit recombinant Jak2 protein catalysis ii) suppress Jak2-mediated pathogenic cell growth in vitro iii) inhibit the aberrant ex vivo growth of Jak2-V617F expressing primary human bone marrow cells and iv) inhibit Jak2-mediated pathogenesis in vivo. To this end, we found that A46 selectively inhibited Jak2-V617F protein when compared to wild type Jak2 protein. The drug also selectively inhibited the proliferation of Jak2-V617F expressing cells in both a time- and dose-dependent manner and this correlated with decreased Jak2 and STAT5 phosphorylation within treated cells. The Jak2-V617F cell growth inhibition correlated with an induction of cell cycle arrest and promotion of apoptosis. A46 also inhibited the pathologic growth of primary Jak2-V617F expressing bone marrow cells, ex vivo. Lastly, using a mouse model of Jak2-V617F mediated MPN, A46 significantly reduced the splenomegaly and megakaryocytic hyperplasia in the spleens of treated mice and the levels of IL-6 in the plasma. Collectively, our data demonstrate that the benzothiophene based compound, A46, suppresses Jak2-mediated pathogenesis, thereby making it a potential candidate drug against Jak2-mediated disorders.
PMCID: PMC3237899  PMID: 22019628
Jak2 kinase; V617F; Small Molecule Inhibitor; Benzothiophene; Myeloproliferative Neoplasms
12.  The top-scoring ‘N’ algorithm: a generalized relative expression classification method from small numbers of biomolecules 
BMC Bioinformatics  2012;13:227.
Relative expression algorithms such as the top-scoring pair (TSP) and the top-scoring triplet (TST) have several strengths that distinguish them from other classification methods, including resistance to overfitting, invariance to most data normalization methods, and biological interpretability. The top-scoring ‘N’ (TSN) algorithm is a generalized form of other relative expression algorithms which uses generic permutations and a dynamic classifier size to control both the permutation and combination space available for classification.
TSN was tested on nine cancer datasets, showing statistically significant differences in classification accuracy between different classifier sizes (choices of N). TSN also performed competitively against a wide variety of different classification methods, including artificial neural networks, classification trees, discriminant analysis, k-Nearest neighbor, naïve Bayes, and support vector machines, when tested on the Microarray Quality Control II datasets. Furthermore, TSN exhibits low levels of overfitting on training data compared to other methods, giving confidence that results obtained during cross validation will be more generally applicable to external validation sets.
TSN preserves the strengths of other relative expression algorithms while allowing a much larger permutation and combination space to be explored, potentially improving classification accuracies when fewer numbers of measured features are available.
PMCID: PMC3663421  PMID: 22966958
Classification; Top-scoring pair; Relative expression; Cross validation; Support vector machine; Graphics processing unit; Microarray
13.  Graphics processing unit implementations of relative expression analysis algorithms enable dramatic computational speedup 
Bioinformatics  2011;27(6):872-873.
Summary: The top-scoring pair (TSP) and top-scoring triplet (TST) algorithms are powerful methods for classification from expression data, but analysis of all combinations across thousands of human transcriptome samples is computationally intensive, and has not yet been achieved for TST. Implementation of these algorithms for the graphics processing unit results in dramatic speedup of two orders of magnitude, greatly increasing the searchable combinations and accelerating the pace of discovery.
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3051334  PMID: 21257608
14.  The Constitutive Activation of Jak2-V617F is Mediated by a π Stacking Mechanism Involving Phe 595 and Phe 617 
Biochemistry  2010;49(46):9972-9984.
Somatic mutations in the Jak2 allele that lead to constitutive kinase activation of the protein have been identified in human disease conditions such as the myeloproliferative neoplasms (MPNs). The most common mutation in these patients is a V617F substitution mutation, which is believed to play a causative role in the MPN pathogenesis. As such, identifying the molecular basis for the constitutive activation of Jak2-V617F is important for understanding its clinical implications and potential treatment. Here, we hypothesized that conversion of residue 617 from Val to Phe resulted in the formation of novel π stacking interactions with neighboring Phe residues. To test this, we first examined the Jak2 structure via molecular modeling and identified a potential π stacking interaction between F594, F595 and F617. Disruption of this interaction through site directed mutagenesis impaired Jak2 auto-phosphorylation, Jak2 dependent gene transcription and in vitro kinase activity of the Jak2-V617F protein. Further, substitution of F594 and F595 with Trp did not affect Jak2 function significantly, but replacement with charged residues did, showing the conservation of aromaticity and hydropathy index at these positions. Using molecular dynamics (MD) simulations, we found that the π stacking interaction between residues 595 and 617 in the Jak2-V617F protein was of much greater energy and conformed to the properties of π stacking, relative to the Jak2-WT or Jak2-V617F/F594A/F595A. In summary, we have identified a π stacking interaction between F595 and F617 that is specific to and is critical for the constitutive activation of Jak2-V617F.
PMCID: PMC2982877  PMID: 20958061
Janus kinase 2 (Jak2); Pi stacking; Constitutive Activation
15.  Noise Contributions in an Inducible Genetic Switch: A Whole-Cell Simulation Study 
PLoS Computational Biology  2011;7(3):e1002010.
Stochastic expression of genes produces heterogeneity in clonal populations of bacteria under identical conditions. We analyze and compare the behavior of the inducible lac genetic switch using well-stirred and spatially resolved simulations for Escherichia coli cells modeled under fast and slow-growth conditions. Our new kinetic model describing the switching of the lac operon from one phenotype to the other incorporates parameters obtained from recently published in vivo single-molecule fluorescence experiments along with in vitro rate constants. For the well-stirred system, investigation of the intrinsic noise in the circuit as a function of the inducer concentration and in the presence/absence of the feedback mechanism reveals that the noise peaks near the switching threshold. Applying maximum likelihood estimation, we show that the analytic two-state model of gene expression can be used to extract stochastic rates from the simulation data. The simulations also provide mRNA–protein probability landscapes, which demonstrate that switching is the result of crossing both mRNA and protein thresholds. Using cryoelectron tomography of an E. coli cell and data from proteomics studies, we construct spatial in vivo models of cells and quantify the noise contributions and effects on repressor rebinding due to cell structure and crowding in the cytoplasm. Compared to systems without spatial heterogeneity, the model for the fast-growth cells predicts a slight decrease in the overall noise and an increase in the repressors rebinding rate due to anomalous subdiffusion. The tomograms for E. coli grown under slow-growth conditions identify the positions of the ribosomes and the condensed nucleoid. The smaller slow-growth cells have increased mRNA localization and a larger internal inducer concentration, leading to a significant decrease in the lifetime of the repressor–operator complex and an increase in the frequency of transcriptional bursts.
Author Summary
Expressing genes in a bacterial cell is noisy and random. A colony of bacteria grown from a single cell can show remarkable differences in the copy number per cell of a given protein after only a few generations. In this work we use computer simulations to study the variation in how individual cells in a population express a set of genes in response to an environmental signal. The modeled system is the lac genetic switch that Escherichia coli uses to find, collect, and process lactose sugar from the environment. The noise inherent in the genetic circuit controlling the cell's response determines how similar the cells are to each other and we study how the different components of the circuit affect this noise. Furthermore, an estimated 30–50% of the cell volume is taken up by a wide variety of large biomolecules. To study the response of the circuit caused by crowding, we simulate the circuit inside a three-dimensional model of an E. coli cell built using data from cryoelectron tomography reconstructions of a single cell and proteomics data. Correctly including random effects of molecular crowding will be critical to developing fully dynamic models of living cells.
PMCID: PMC3053318  PMID: 21423716
16.  Targeting of the protein interaction site between FAK and IGF-1R 
The interaction of focal adhesion kinase (FAK) and insulin-like growth factor-1 receptor (IGF-1R) plays an important role in cancer cell survival. Targeting this interaction with small molecule drugs could be a novel strategy in cancer therapy. By a series of pull-down assay using GST-tagged FAK fragments and His-tagged IGF-1R intracellular fragments, we showed that the FAK-NT2 (aa 127–243) domain directly interacts with the N-terminal part of the IGF-1R intracellular domain. Overexpressed FAK-NT2 domain was also shown to co-localize with IGF-1R in pancreatic cells. Computational modeling was used to predict the biding configuration of these two domains and to screen for small molecules binding to the interaction site. This strategy successfully identified a lead compound that disrupts FAK/IGF-1R interaction.
PMCID: PMC2742998  PMID: 19664602
17.  The Small Molecule Chloropyramine Hydrochloride (C4) Targets the Binding Site of Focal Adhesion Kinase and Vascular Endothelial Growth Factor Receptor 3 and Suppresses Breast Cancer Growth in vivo 
Journal of medicinal chemistry  2009;52(15):4716-4724.
FAK is a tyrosine kinase that functions as a key orchestrator of signals leading to invasion and metastasis. Since FAK interacts directly with a number of critical proteins involved in survival signaling in tumor cells, we hypothesized that targeting a key protein-protein interface with drug-like small molecules was a feasible strategy for inhibiting tumor growth. In this study, we targeted the protein-protein interface between FAK and VEGFR-3 and identified compound C4 (chloropyramine hydrochloride) as a drug capable of 1) inhibiting the biochemical function of VEGFR-3 and FAK, 2) inhibiting proliferation of a diverse set of cancer cell types in vitro, and 3) reducing tumor growth in vivo. Chloropyramine hydrochloride reduced tumor growth as a single agent, while concomitant administration with doxorubicin had a pronounced synergistic effect. Our data demonstrate that the FAK-VEGFR-3 interaction can be targeted by small drug-like molecules and this interaction can provide the basis for highly-specific novel cancer therapeutics.
PMCID: PMC2765121  PMID: 19610651
18.  Identification of Enoxacin as an Inhibitor of Osteoclast Formation and Bone Resorption by Structure-Based Virtual Screening 
Journal of medicinal chemistry  2009;52(16):5144-5151.
An interaction between the B2 subunit of vacuolar H+-ATPase (V-ATPase) and microfilaments is required for osteoclast bone resorption. An atomic homology model of the actin binding site on B2 was generated and molecular docking simulations were performed. Enoxacin, a fluoroquinolone antibiotic, was identified and in vitro testing demonstrated that enoxacin blocked binding between purified B2 and microfilaments. Enoxacin dose dependently reduced the number of osteoclasts differentiating in mouse marrow cultures stimulated with 1,25-dihydroxyvitamin D3, as well as markers of osteoclast activity, and the number of resorption lacunae formed on bone slices. Enoxacin inhibited osteoclast formation at concentrations where osteoblast formation was not altered. In summary, enoxacin is a novel small molecule inhibitor of osteoclast bone resorption that acts by an unique mechanism and is therefore an attractive lead molecule for the development of a new class of antiosteoclastic agents.
PMCID: PMC2889180  PMID: 19630402
19.  Crystallization of the focal adhesion kinase targeting (FAT) domain in a primitive orthorhombic space group 
X-ray diffraction data from the targeting (FAT) domain of focal adhesion kinase (FAK) were collected from a single crystal that diffracted to 1.99 Å resolution.
X-ray diffraction data from the targeting (FAT) domain of focal adhesion kinase (FAK) were collected from a single crystal that diffracted to 1.99 Å resolution and reduced to the primitive orthorhombic lattice. A single molecule was predicted to be present in the asymmetric unit based on the Matthews coefficient. The data were phased using molecular-replacement methods using an existing model of the FAK FAT domain. All structures of human focal adhesion kinase FAT domains solved to date have been solved in a C-centered orthorhombic space group.
PMCID: PMC2496861  PMID: 18540077
focal adhesion kinase; targeting domain
20.  A small molecule inhibitor 1,2,4,5-Benzenetetraamine tetrahydrochloride, targeting the Y397 site of Focal Adhesion Kinase decreases tumor growth 
Journal of medicinal chemistry  2008;51(23):7405-7416.
Focal Adhesion Kinase (FAK) is a non-receptor kinase that is overexpressed in many types of tumors. We developed a novel cancer-therapy approach, targeting the main autophosphorylation site of FAK, Y397 by computer modeling and screening of the National Cancer Institute (NCI) small molecule compounds database. More than 140,000 small molecule compounds were docked into the N-terminal domain of the FAK crystal structure in 100 different orientations that identified 35 compounds. One compound 14 (1,2,4,5-Benzenetetraamine tetrahydrochloride) significantly decreased viability in most of the cells to the levels equal or higher than control FAK inhibitor, 1a (2-[5-Chloro-2-[2-methoxy-4-(4-morpholinyl)phenylamino]pyrimidin-4-ylamino]-N-methylbenzamide; TAE226) from Novartis, Inc. The compound 14 specifically and directly blocked phosphorylation of Y397-FAK in a dose- and time-dependent manner. It increased cell detachment and inhibited cell adhesion in a dose-dependent manner. Furthermore, 14 effectively caused breast tumor regression in vivo. Thus, targeting the Y397 site of FAK with 14 inhibitor can be effectively used in cancer therapy.
PMCID: PMC2662449  PMID: 18989950
Focal Adhesion Kinase; Y397 site; autophosphorylation; tumor; inhibitor
21.  Crystallization and X-ray diffraction analysis of a novel immune-type receptor from Ictalurus punctatus and phasing by selenium anomalous dispersion methods 
A highly diversified novel immune-type receptor from catfish, NITR10, was crystallized to reveal novel mechanisms of immune recognition.
X-ray diffraction data from crystals of a novel immune-type receptor (NITR10 from the catfish Ictalurus punctatus) were collected to 1.65 Å resolution and reduced to the primitive hexagonal lattice. Native and selenomethionine derivatives of NITR10 crystallized under different conditions yielded P3121 crystals. SeMet NITR10 was phased to a correlation coefficient of 0.77 by SAD methods and experimental electron-density maps were calculated to 1.65 Å. Five NITR10 molecules are predicted to be present in the asymmetric unit based on the Matthews coefficient.
PMCID: PMC2344108  PMID: 18084086
immune-type receptors; NITR10; Ictalurus punctatus
22.  Novel Immune-Type Receptors Mediate Allogeneic Recognition 
Immunity  2008;29(2):228-237.
Novel immune-type receptors (NITRs) comprise an exceptionally large, diversified family of activating/inhibitory receptors that has been identified in bony fish. In this study, we characterize the structure of an activating NITR that is expressed by a cytotoxic NK-like cell line and specifically binds an allogeneic B cell target. A single amino acid residue within the NITR immunoglobulin variable (V)-type domain accounts for specificity of the interaction. Structures solved by x-ray crystallography reveal: (1) the V-type domains of NITRs form homodimers resembling heterodimers formed by rearranging antigen binding receptors and (2) both subunits of NITR dimers form ligand-binding surfaces in CDR1 that determine specificity for the nonself target. In the evolution of immune function, it appears that a specific NK-type of innate recognition may be mediated by a complex germline multigene family of V structures resembling those that are somatically diversified in adaptive immune responses.
PMCID: PMC2603606  PMID: 18674935
23.  Z3, a Novel Jak2 Tyrosine Kinase Small Molecule Inhibitor that Suppresses Jak2-mediated Pathologic Cell Growth 
Molecular cancer therapeutics  2008;7(8):2308-2318.
Jak2 tyrosine kinase is essential for animal development and hyper-kinetic Jak2 function has been linked to a host of human diseases. Control of this pathway using Jak2-specific inhibitors would therefore potentially serve as a useful research tool and/or therapeutic agent. Here, we used a high throughput program called DOCK, to predict the ability of 20,000 small molecules to interact with a structural pocket adjacent to the ATP binding site of murine Jak2. One small molecule, 2-methyl-1-phenyl-4-pyridin-2-yl-2-(2-pyridin-2-ylethyl)butan-1-one (herein designated as Z3) bound to Jak2 with a favorable energy score. Z3 inhibited Jak2-V617F and Jak2-WT autophosphorylation in a dose-dependent manner, but was not cytotoxic to cells at concentrations that inhibited kinase activity. Z3 selectively inhibited Jak2 kinase function with no effect of Tyk2 or c-Src kinase function. Z3 significantly inhibited proliferation of the Jak2-V617F-expressing, human erythroleukemia cell line, HEL 92.1.7. The Z3-mediated reduction in cell proliferation correlated with reduced Jak2 and STAT3 tyrosine phosphorylation levels as well as marked cell cycle arrest. Finally, Z3 inhibited the growth of hematopoietic progenitor cells isolated from the bone marrow of an essential thrombocythemia patient harboring the Jak2-V617F mutation and a polycythemia vera patient carrying a Jak2-F537I mutation. Collectively, the data suggest that Z3 is a novel specific inhibitor of Jak2 tyrosine kinase.
PMCID: PMC2579271  PMID: 18723478
Small Molecule Inhibitor; Jak2; Myeloproliferative Disorders

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