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A joint analysis of the NCI60 small molecule screening data, their genetically defective genes, and mechanisms of action (MOA) of FDA approved cancer drugs screened in the NCI60 is proposed for identifying links between chemosensitivity, genomic defects and MOA. Self-Organizing-Maps (SOMs) are used to organize the chemosensitivity data. Student's t-tests are used to identify SOM clusters with enhanced chemosensitivity for tumor cell lines with versus without genetically defective genes. Fisher's exact and chi-square tests are used to reveal instances where defective gene to chemosensitivity associations have enriched MOAs. The results of this analysis find a relatively small set of defective genes, inclusive of ABL1, AXL, BRAF, CDC25A, CDKN2A, IGF1R, KRAS, MECOM, MMP1, MYC, NOTCH1, NRAS, PIK3CG, PTK2, RPTOR, SPTBN1, STAT2, TNKS and ZHX2, as possible candidates for roles in chemosensitivity for compound MOAs that target primarily, but not exclusively, kinases, nucleic acid synthesis, protein synthesis, apoptosis and tubulin. These results find exploitable instances of enhanced chemosensitivity of compound MOA's for selected defective genes. Collectively these findings will advance the interpretation of pre-clinical screening data as well as contribute towards the goals of cancer drug discovery, development decision making, and explanation of drug mechanisms., Competing Interests: No competing interests.

A novel data mining procedure is proposed for identifying potential pathway-gene biomarkers from preclinical drug sensitivity data for predicting clinical responses to erlotinib or sorafenib. The analysis applies linear ridge regression modeling to generate a small (N~1000) set of baseline gene expressions that jointly yield quality predictions of preclinical drug sensitivity data and clinical responses. Standard clustering of the pathway-gene combinations from gene set enrichment analysis of this initial gene set, according to their shared appearance in molecular function pathways, yields a reduced (N~300) set of potential pathway-gene biomarkers. A modified method for quantifying pathway fitness is used to determine smaller numbers of over and under expressed genes that correspond with favorable and unfavorable clinical responses. Detailed literature-based evidence is provided in support of the roles of these under and over expressed genes in compound efficacy. RandomForest analysis of potential pathway-gene biomarkers finds average treatment prediction errors of 10% and 22%, respectively, for patients receiving erlotinib or sorafenib that had a favorable clinical response. Higher errors were found for both compounds when predicting an unfavorable clinical response. Collectively these results suggest complementary roles for biomarker genes and biomarker pathways when predicting clinical responses from preclinical data.

Developing reliable biomarkers of tumor cell drug sensitivity and resistance can guide hypothesis-driven basic science research and influence pre-therapy clinical decisions. A popular strategy for developing biomarkers uses characterizations of human tumor samples against a range of cancer drug responses that correlate with genomic change; developed largely from the efforts of the Cancer Cell Line Encyclopedia (CCLE) and Sanger Cancer Genome Project (CGP). The purpose of this study is to provide an independent analysis of this data that aims to vet existing and add novel perspectives to biomarker discoveries and applications. Existing and alternative data mining and statistical methods will be used to a) evaluate drug responses of compounds with similar mechanism of action (MOA), b) examine measures of gene expression (GE), copy number (CN) and mutation status (MUT) biomarkers, combined with gene set enrichment analysis (GSEA), for hypothesizing biological processes important for drug response, c) conduct global comparisons of GE, CN and MUT as biomarkers across all drugs screened in the CGP dataset, and d) assess the positive predictive power of CGP-derived GE biomarkers as predictors of drug response in CCLE tumor cells. The perspectives derived from individual and global examinations of GEs, MUTs and CNs confirm existing and reveal unique and shared roles for these biomarkers in tumor cell drug sensitivity and resistance. Applications of CGP-derived genomic biomarkers to predict the drug response of CCLE tumor cells finds a highly significant ROC, with a positive predictive power of 0.78. The results of this study expand the available data mining and analysis methods for genomic biomarker development and provide additional support for using biomarkers to guide hypothesis-driven basic science research and pre-therapy clinical decisions.

Bioinformatic analysis of data from the NCI-60 cell cytotoxicity screen revealed a subset of extracts that showed selective cytotoxic activity toward human colon carcinoma cell lines. Bioassay-guided fractionation of a colon cancer selective extract from a Philippines collection of the marine sponge Corticium niger provided two new steroidal alkaloids, plakinamines N (1) and O (2), along with two known compounds of the plakinamine class (3, 4). The structures of these compounds were elucidated by interpretation of combined MS and NMR spectroscopic data. Plakinamines N (1), O (2), and J (4) were tested for antiproliferative activity in the NCI-60 screen, and they showed enhanced inhibitory effects against all of the colon cell lines with mean GI50 values of 11.5, 2.4, and 1.4 μM, respectively.

Background: Cancer stem cells (CSC) are thought to be responsible for tumor maintenance and heterogeneity. Bona fide CSC purified from tumor biopsies are limited in supply and this hampers study of CSC biology. Furthermore, purified stem-like CSC subpopulations from existing tumor lines are unstable in culture. Finding a means to overcome these technical challenges would be a useful goal. In a first effort towards this, we examined whether a chemical probe that promotes survival of murine embryonic stem cells without added exogenous factors can alter functional characteristics in extant tumor lines in a fashion consistent with a CSC phenotype., Methodology/principal Findings: The seven tumor lines of the NCI60 colon subpanel were exposed to SC-1 (pluripotin), a dual kinase and GTPase inhibitor that promotes self-renewal, and then examined for tumorigenicity under limiting dilution conditions and clonogenic activity in soft agar. A statistically significant increase in tumor formation following SC-1 treatment was observed (p<0.04). Cloning efficiencies and expression of putative CSC surface antigens (CD133 and CD44) were also increased. SC-1 treatment led to sphere formation in some colon tumor lines. Finally, SC-1 inhibited in vitro kinase activity of RSK2, and another RSK2 inhibitor increased colony formation implicating a role for this kinase in eliciting a CSC phenotype., Conclusions/significance: These findings validate a proof of concept study exposure of extant tumor lines to a small molecule may provide a tractable in vitro model for understanding CSC biology.

Gene expression data, collected from ASPS tumors of seven different patients and from one immortalized ASPS cell line (ASPS-1), was analyzed jointly with patient ASPL-TFE3 (t(X;17)(p11;q25)) fusion transcript data to identify disease-specific pathways and their component genes. Data analysis of the pooled patient and ASPS-1 gene expression data, using conventional clustering methods, revealed a relatively small set of pathways and genes characterizing the biology of ASPS. These results could be largely recapitulated using only the gene expression data collected from patient tumor samples. The concordance between expression measures derived from ASPS-1 and both pooled and individual patient tumor data provided a rationale for extending the analysis to include patient ASPL-TFE3 fusion transcript data. A novel linear model was exploited to link gene expressions to fusion transcript data and used to identify a small set of ASPS-specific pathways and their gene expression. Cellular pathways that appear aberrantly regulated in response to the t(X;17)(p11;q25) translocation include the cell cycle and cell adhesion. The identification of pathways and gene subsets characteristic of ASPS support current therapeutic strategies that target the FLT1 and MET, while also proposing additional targeting of genes found in pathways involved in the cell cycle (CHK1), cell adhesion (ARHGD1A), cell division (CDC6), control of meiosis (RAD51L3) and mitosis (BIRC5), and chemokine-related protein tyrosine kinase activity (CCL4).

Studies into the genetic origins of tumor cell chemoactivity pose significant challenges to bioinformatic mining efforts. Connections between measures of gene expression and chemoactivity have the potential to identify clinical biomarkers of compound response, cellular pathways important to efficacy and potential toxicities; all vital to anticancer drug development. An investigation has been conducted that jointly explores tumor-cell constitutive NCI60 gene expression profiles and small-molecule NCI60 growth inhibition chemoactivity profiles, viewed from novel applications of self-organizing maps (SOMs) and pathway-centric analyses of gene expressions, to identify subsets of over- and under-expressed pathway genes that discriminate chemo-sensitive and chemo-insensitive tumor cell types. Linear Discriminant Analysis (LDA) is used to quantify the accuracy of discriminating genes to predict tumor cell chemoactivity. LDA results find 15% higher prediction accuracies, using ∼30% fewer genes, for pathway-derived discriminating genes when compared to genes derived using conventional gene expression-chemoactivity correlations. The proposed pathway-centric data mining procedure was used to derive discriminating genes for ten well-known compounds. Discriminating genes were further evaluated using gene set enrichment analysis (GSEA) to reveal a cellular genetic landscape, comprised of small numbers of key over and under expressed on- and off-target pathway genes, as important for a compound's tumor cell chemoactivity. Literature-based validations are provided as support for chemo-important pathways derived from this procedure. Qualitatively similar results are found when using gene expression measurements derived from different microarray platforms. The data used in this analysis is available at http://pubchem.ncbi.nlm.nih.gov/andhttp://www.ncbi.nlm.nih.gov/projects/geo (GPL96, GSE32474).

A bicycle built for tubulin: The total synthesis of (+)-chamaecypanone C has been achieved by using a tandem retro-Diels-Alder/Diels-Alder cascade reaction (see scheme). Initial biological studies demonstrate that (+)-chamaecypanone C is an inhibitor of tubulin assembly and binds at the colchicine site.

Omics-based investigations offer potentially powerful readouts that might be useful for probing the underlying biology of normal and diseased states, identifying novel therapeutic targets and proposing relevant markers for designing treatment strategies. A vital component of these investigations involves a systematic analysis of gene expression and chemosensitivity data in the context of disease states and small molecule probes into the function of targets responsible for a disease phenotype. Systematic analysis of chemical and pharmacogenetics data offers a possible means to identify novel, small-molecule, potentially therapeutic, agents that affect the phenotype of a particular target. Elegantly simple in concept, the covariation of genetic and chemosensitivity readouts provide a hypothetical link for relating compounds through genomic expression profiles to underlying biology.

We present an analysis of current anticancer compounds that are in phase I, II, or III clinical trials and their structural analogues that have been screened in the National Cancer Institute (NCI) anticancer screening program. Bioactivity profiles, measured across the NCI 60 cell lines, were examined for a correspondence between the type of cancer proposed for clinical testing and selective sensitivity to appropriately matched tumor subpanels in the NCI screen. These results find strongest support for using the NCI anticancer screen to select analogue compounds with selective sensitivity to the leukemia, colon, central nervous system, melanoma, and ovarian panels, but not for renal, prostate, and breast panels. These results are extended to applications of two-dimensional structural features to further refine compound selections based on tumor panel sensitivity obtained from tumor screening results.

We provide a chemoinformatic examination of the NCI public human tumor xenograft data to explore relationships between small molecules, treatment modality, efficacy, and toxicity. Efficacy endpoints of tumor weight reduction (TW) and survival time increase (ST) compared to tumor bearing control mice were augmented by a toxicity measure, defined as the survival advantage of treated versus control animals (TX). These endpoints were used to define two independent therapeutic indices (TIs) as the ratio of efficacy (TW or ST) to toxicity (TX). Linear models predictive of xenograft endpoints were successfully constructed (0.67 < r(2) < or = 0.74)(observed&#95;versus&#95;predicted) using a model comprised of variables in treatment modality, chemoinformatic descriptors, and in vitro cell growth inhibition in the NCI 60-cell assay. Cross-validation analysis based on randomly chosen training subsets found these predictive correlations to be robust. Model-based sensitivity analysis found chemistry and growth inhibition to provide the best, and treatment modality the worst, indicators of xenograft endpoint. The poor predictive power derived from treatment alone appears to be of less importance to xenograft outcome for compounds having strongly similar chemical and biological features. ROC-based model validation found a 70% positive predictive value for distinguishing FDA approved oncology agents from available xenograft tested compounds. Additional chemoinformatic applications are provided that relate xenograft outcome to biological pathways and putative mechanism of compound action. These results find a strong relationship between xenograft efficacy and pathways comprised of genes having highly correlated mRNA expressions. Our analysis demonstrates that chemoinformatic studies utilizing a combination of xenograft data and in vitro preclinical testing offer an effective means to identify compound classes with superior efficacy and reduced toxicity.

Mitochondria are principal mediators of apoptosis and thus can be considered molecular targets for new chemotherapeutic agents in the treatment of cancer. Inhibitors of mitochondrial complex I of the electron transport chain have been shown to induce apoptosis and exhibit antitumor activity. In an effort to find novel complex I inhibitors which exhibited anticancer activity in the NCI's tumor cell line screen, we examined organized tumor cytotoxicity screening data available as SOM (self-organized maps) (http://www.spheroid.ncifcrf.gov) at the developmental therapeutics program (DTP) of the National Cancer Institute (NCI). Our analysis focused on an SOM cluster comprised of compounds which included a number of known mitochondrial complex I (NADH:CoQ oxidoreductase) inhibitors. From these clusters 10 compounds whose mechanism of action was unknown were tested for inhibition of complex I activity in bovine heart sub-mitochondrial particles (SMP) resulting in the discovery that 5 of the 10 compounds demonstrated significant inhibition with IC50's in the nM range for three of the five. Examination of screening profiles of the five inhibitors toward the NCI's tumor cell lines revealed that they were cytotoxic to the leukemia subpanel (particularly K562 cells). Oxygen consumption experiments with permeabilized K562 cells revealed that the five most active compounds inhibited complex I activity in these cells in the same rank order and similar potency as determined with bovine heart SMP. Our findings thus fortify the appeal of mitochondrial complex I as a possible anticancer molecular target and provide a data mining strategy for selecting candidate inhibitors for further testing.

The purpose of this study is to examine gene expression changes occurring in cancer from a pathway perspective by analyzing the level of pathway coherence in tumor tissues in comparison with their normal counterparts. Instability in pathway regulation patterns can be considered either as a result of or as a contributing factor to genetic instability and possibly cancer. Our analysis has identified pathways that show a significant change in their coherence level in tumor tissues, some of which are tumor type specific, indicating novel targets for cancer type-specific therapies. Pathways are found to have a general tendency to lose their gene expression coherence in tumor tissues when compared with normal tissues, especially for signaling pathways. The selective growth advantage of cancer cells over normal cells seems to originate from their preserved control over vital pathways to ensure survival and altered signaling, allowing excessive proliferation. We have additionally investigated the tissue-related instability of pathways, providing valuable clues to the cellular processes underlying the tumorigenesis and/or growth of specific cancer types. Pathways that contain known cancer genes (i.e., "cancer pathways") show significantly greater instability and are more likely to become incoherent in tumor tissues. Finally, we have proposed strategies to target instability (i.e., pathways that are prone to changes) by identifying compound groups that show selective activity against pathways with a detectable coherence change in cancer. These results can serve as guidelines for selecting novel agents that have the potential to specifically target a particular pathway that has relevance in cancer.

This paper examines two biological models of anticancer activity, cytotoxicity and hollow fiber (HF) activity, for chemotherapeutic agents evaluated as part of the National Cancer Institute's (NCI's) drug screening effort. Our analysis proposes strategies to globally assess compounds tested in the NCI's 60-cell (NCI60) in vitro anticancer screen in terms of structural features, biological activity, target specificity, and mechanism of action by data integration via our self-organizing maps of structural and biological response patterns. We have built statistical models to predict compound potency and HF activity based on physicochemical properties. Our results find that it is the combination of different structural properties that determines a compound's biological activity. A direct correlation is also found between compound potency and specificity, indicating that specific targeting, rather than promiscuous poisoning, gives rise to potency. Finally, we offer a strategy to exploit this relationship for future mining of novel anticancer candidates.

We have analyzed the level of gene coregulation, using gene expression patterns measured across the National Cancer Institute's 60 tumor cell panels (NCI(60)), in the context of predefined pathways or functional categories annotated by KEGG (Kyoto Encyclopedia of Genes and Genomes), BioCarta, and GO (Gene Ontology). Statistical methods were used to evaluate the level of gene expression coherence (coordinated expression) by comparing intra- and interpathway gene-gene correlations. Our results show that gene expression in pathways, or groups of functionally related genes, has a significantly higher level of coherence than that of a randomly selected set of genes. Transcriptional-level gene regulation appears to be on a "need to be" basis, such that pathways comprising genes encoding closely interacting proteins and pathways responsible for vital cellular processes or processes that are related to growth or proliferation, specifically in cancer cells, such as those engaged in genetic information processing, cell cycle, energy metabolism, and nucleotide metabolism, tend to be more modular (lower degree of gene sharing) and to have genes significantly more coherently expressed than most signaling and regular metabolic pathways. Hierarchical clustering of pathways based on their differential gene expression in the NCI(60) further revealed interesting interpathway communications or interactions indicative of a higher level of pathway regulation. The knowledge of the nature of gene expression regulation and biological pathways can be applied to understanding the mechanism by which small drug molecules interfere with biological systems.

2-(4-Amino-3-methylphenyl)-5-fluorobenzothiazole (5F-203) is a candidate antitumor agent empirically discovered with the aid of the National Cancer Institute (NCI) Anticancer Drug Screen. In an effort to determine whether basal expression of genes could be used to classify cell sensitivity to this agent, serial analysis of gene expression (SAGE) libraries were generated for three sensitive and two insensitive human tumor cell lines. When the SAGE tags expressed within these cell line libraries were compared and evaluated for differences, several genes seemed more highly expressed in 5F-203-sensitive cell lines than in the insensitive cell lines. Constitutive expressions of 15 genes identified by the analysis were then measured by quantitative reverse-transcription polymerase chain reaction in the 60 cell lines of the NCI Anticancer Drug Screen. This generated a pattern of relative basal gene expression across the cell lines and also confirmed the differential expression of SAGE-discovered genes within the initial set of five cell lines. Further analyses of these expression data in 60 cell lines suggested that a smaller subset of genes could be used to classify tumor cell sensitivity to 5F-203. In contrast, the same set of genes did not predict with equivalent precision sensitivity to unrelated active drugs, and another set of genes could not better classify the cell lines in terms of 5F-203 sensitivity. These results suggest that constitutive gene expression profiles, in which the genes are not necessarily known to be related to the mechanism of action of a given drug, may be viewed as a general tool to extend and improve the concept of a single predictive gene to groups of predictive genes.

Chemical variations of small compounds are commonly used to probe biological systems and potentially discover lead-like compounds with selective target activity. Molecular probes are either generated by synthesis or acquired through directed searches of commercially available compound libraries. The data generated when testing the probes in various biological systems constitutes a structure/activity analysis. The ability to detect variations and classify biological responses requires the analysis of a compound in multiple assays. While the concept of a structure/activity relationship is straightforward, its implementation can vary considerably depending on the biological system under study and the probe library selected for testing. The analysis presented here will focus on the accumulated compound library used to screen for growth inhibition across the National Cancer Institute's panel of 60 tumor cells. The considerable chemical and biological diversity inherent in these data offers an opportunity to establish a quantifiable connection between chemical structure and biological activity. We find that the connection between structure and biological response is not symmetric, with biological response better at predicting chemical structure than vice versa. Structurally and functionally similar compounds can have distinguishable biological responses reflecting different mechanisms of action.

The karyotypic features of cancer cells have not been a particular focus of anticancer drug targeting either as guidance for treatment or as specific drug targets themselves. Cancer cell lines typically have considerable, characteristic, and variable chromosomal aberrations. Here, we consider small-molecule screening data across the National Cancer Institute's 60 tumor cell line drug screening panel (NCI-60) analyzed for specific association with karyotypic variables (numerical and structural complexity and heterogeneity) determined for these same cell lines. This analysis is carried out with the aid of a self-organizing map allowing for a simultaneous assessment of all screened compounds, revealing an association between karyotypic variables and a unique part of the cytotoxic response space. Thirteen groups of compounds based on related specific chemical structural motifs are identified as possible leads for anticancer drug discovery. These compounds form distinct groups of molecules associated with relatively unexplored regions of the NCI-60 self-organizing map where anticancer agents currently standard in the clinic are not present. We suggest that compounds identified in this study may represent new classes of potential anticancer agents.

An integrated, bioinformatic analysis of three databases comprising tumor-cell-based small molecule screening data, gene expression measurements, and PDB (Protein Data Bank) ligand-target structures has been developed for probing mechanism of drug action (MOA). Clustering analysis of GI50 profiles for the NCI's database of compounds screened across a panel of tumor cells (NCI60) was used to select a subset of unique cytotoxic responses for about 4000 small molecules. Drug-gene-PDB relationships for this test set were examined by correlative analysis of cytotoxic response and differential gene expression profiles within the NCI60 and structural comparisons with known ligand-target crystallographic complexes. A survey of molecular features within these compounds finds thirteen conserved Compound Classes, each class exhibiting chemical features important for interactions with a variety of biological targets. Protein targets for an additional twelve Compound Classes could be directly assigned using drug-protein interactions observed in the crystallographic database. Results from the analysis of constitutive gene expressions established a clear connection between chemo-resistance and overexpression of gene families associated with the extracellular matrix, cytoskeletal organization, and xenobiotic metabolism. Conversely, chemo-sensitivity implicated overexpression of gene families involved in homeostatic functions of nucleic acid repair, aryl hydrocarbon metabolism, heat shock response, proteasome degradation and apoptosis. Correlations between chemo-responsiveness and differential gene expressions identified chemotypes with nonselective (i.e., many) molecular targets from those likely to have selective (i.e., few) molecular targets. Applications of data mining strategies that jointly utilize tumor cell screening, genomic, and structural data are presented for hypotheses generation and identifying novel anticancer candidates., (Copyright 2005 Wiley-Liss, Inc.)

Clustering analysis of tumor cell cytotoxicity profiles for the National Cancer Institute (NCI)'s open compound repository has been used to catalog over 1100 metal or metalloid containing compounds with potential anticancer activity. The molecular features and corresponding reactivity of these compounds have been analyzed in terms of properties of their metals, their associated organic components (ligands) and their capacity to inhibit tumor cell growth. Cytotoxic responses are influenced by both the identity of the metal and the properties of its coordination ligand, with clear associations between structural similarities and cytotoxicity. Assignments of mechanisms of action (MOAs) for these compounds could be segregated into four broad response classes according to preference for binding to biological sulfhydryl groups, chelation, generation of reactive oxygen species (ROS), and production of lipophilic ions. Correlations between specific cytotoxic responses and differential gene expression profiles within the NCI's tumor cell panel serve as a validation for candidate biological targets and putative MOA classes. In addition, specific sensitivity toward subsets of metal containing agents has been found for certain tumor cell panels. Taken together, our results expand the knowledge base available for evaluating, designing and developing new metal-based anticancer drugs that may provide the basis for target-specific therapeutics.

The elevated expression of the flavoprotein NAD(P)H:quinone acceptor oxidoreductase (NQO1) (EC 1.6.99.2) in many human solid tumors, along with its ability to activate quinone-based anticancer agents, makes it an excellent target for enzyme-directed drug development. Previous studies have shown a significant statistical correlation between NQO1 enzymatic activity and the cytotoxicity of certain antitumor quinones. RH1 [2,5-diaziridinyl-3-(hydroxymethyl)-6-methyl-1,4-benzoquinone], presently in late preclinical and entering early clinical development, has been previously considered to be an excellent substrate for activation by NQO1. In this study we investigate the cytotoxicity of RH1 in cell lines selected from the NCI's 60 tumor cell line panel, expressing varying levels of NQO1 activity. Exposure time- and concentration-dependent cytotoxicity was seen, apparently independent from levels of NQO1 activity in these cells. Furthermore, the NQO1 inhibitor dicoumarol had no impact on the sensitivity profiles of RH1 response. The HL-60 myeloid leukemia cells, which do not have detectable NQO1 activity, were further investigated. RH1 treatment of HL-60 cells generated high levels of free radicals, which was accompanied by robust redox cycling, oxygen consumption and induction of apoptosis. These results are in agreement with previous data suggesting that, in addition to its activation by NQO1, RH1-induced cytotoxicity might involve alternative pathways for activation of this compound. Furthermore, the high cytotoxicity of RH1 in the leukemia/lymphoma subpanel of the NCI in vitro cell line screen would suggest an empirical rationale for the utilization of this compound in the treatment of these malignancies.

Novel strategies are proposed to quantitatively analyze and relate biological pathways to drug responses using gene expression and small-molecule growth inhibition data (GI(50)) derived from the National Cancer Institute's 60 cancer cells (NCI(60)). We have annotated groups of drug GI(50) responses with pathways defined by the Kyoto Encyclopedia of Genes and Genomes (KEGG) and BioCarta, and functional categories defined by Gene Ontology (GO), through correlations between pathway gene expression patterns and drug GI(50) profiles. Drug-gene-pathway relationships may then be utilized to find drug targets or target-specific drugs. Significantly correlated pathways and the gene products involved represent interesting targets for further exploration, whereas drugs that are significantly correlated with only certain pathways are more likely to be target specific. Separate pathway clustering finds that pathways engaged in the same biological process tend to have similar drug correlation patterns. The biological and statistical significances of our method are established by comparison to known small-molecule inhibitor-gene target relationships reported in the literature and by standard randomization procedures. The results of our pathway, gene expression and drug-induced growth inhibition associations, can serve as a basis for proposing testable hypotheses about potential anticancer drugs, their targets, and mechanisms of action.

Tubulin with bound [5-3H]dolastatin 10 was exposed to ultraviolet light, and 8-10% of the bound drug cross-linked to the protein, most of it specifically. The primary cross-link was to the peptide spanning amino acid residues 2-31 of beta-tubulin, but the specific amino acid could not be identified. Indirect studies indicated that cross-link formation occurred between cysteine 12 and the thiazole moiety of dolastatin 10. An equipotent analog of dolastatin 10, lacking the thiazole ring, did not form an ultraviolet light-induced cross-link to beta-tubulin. Preillumination of tubulin with ultraviolet light, known to induce cross-link formation between cysteine 12 and exchangeable site nucleotide, inhibited the binding of [5-3H]dolastatin 10 and cross-link formation more potently than it inhibited the binding of colchicine or vinblastine to tubulin. Conversely, binding of dolastatin 10 to tubulin inhibited formation of the cross-link between cysteine 12 and the exchangeable site nucleotide. Dithiothreitol inhibited formation of the beta-tubulin/dolastatin 10 cross-link but not the beta-tubulin/exchangeable site nucleotide cross-link. Modeling studies revealed a highly favored binding site for dolastatin 10 at the + end of beta-tubulin in proximity to the exchangeable site GDP. Computational docking of an energy-minimized dolastatin 10 conformation at this site placed the thiazole ring of dolastatin 10 8-9 A from the sulfur atom of cysteine 12. Dolastatin 15 and cryptophycin 1 could also be docked into positions that overlapped more extensively with the docked dolastatin 10 than with each other. This result was consistent with the observed binding properties of these peptides.

Motivation: Data mining tools are proposed to establish mechanistic connections between chemotypes and specific cellular functions. Drawing on a previous study that classified the cellular response patterns of growth inhibition measurements log( GI(50)) from the National Cancer Institute's (NCI's) anticancer screen, we have examined additional data for mRNA expression, sets of known molecular targets and mutational status against these same tumor cell lines to relate chemosensitivity more precisely to biochemical pathways., Results: Our analysis finds that gene expression levels do not, in general, correlate with log(GI(50)) measurements, instead they reflect a generic toxic condition. Within the remaining set of non-generic conditions, examples were found where a correlation suggesting a biochemical basis for cellular cytotoxicity could be supported. These included reconfirmation of previously observed associations between mutant and wild-type status of p53, and chemosensitivity to alkylating agents, while extending these results to reveal associations with gamma-induced expressions of MDM2, WAF1 and GADD45, signals that were not apparent in measurements of basal mRNA expression levels for any of these genes. Additional examinations revealed that mRNA expression levels directly correlated with paclitaxel chemosensitivity to mitosis, while also identifying additional chemotypes as P-glycoprotein substrates. Our analysis revealed well-known direct associations between p16 mutant status and chemotypes implicated in cell cycle control, and extended these results to include expression levels for three additional tyrosine kinase proteins (TEK, transgelin and hCdc4). Links were also found that suggested associations between chemosensitivity and the endocrine, paracrine ligand-receptor loops, via expression of the adrenergic receptor, calcium second messenger pathways via expression levels of carbonic anhydrase and cellular communication pathways via fibrillin.

An unsupervised self-organizing map-based clustering strategy has been developed to classify tissue samples from an oligonucleotide microarray patient database. Our method is based on the likelihood that a test data vector may have a gene expression fingerprint that is shared by more than one tumor class and as such can identify datasets that cannot be unequivocally assigned to a single tumor class. Our self-organizing map analysis completely separated the tumor from the normal expression datasets. Within the 14 different tumor types, classification accuracies on the order of approximately 80% correct were achieved. Nearly perfect classifications were found for leukemia, central nervous system, melanoma, uterine, and lymphoma tumor types, with very poor classifications found for colorectal, ovarian, breast, and lung tumors. Classification results were further analyzed to identify sets of differentially expressed genes between tumor and normal gene expressions and among each tumor class. Within the total pool of 1139 genes most differentially expressed in this dataset, subsets were found that could be vetted according to previously published literature sources to be specific tumor markers. Attempts to classify gene expression datasets from other sources found a wide range of classification accuracies. Discussions about the utility of this method and the quality of data needed for accurate tumor classifications are provided.

We propose an integrated application of technologies, computation and statistical methods to design experiments for examination of cellular pathways that are necessary for cell survival and that are candidates for cancer therapy. Our design combines information derived from two very different data sets: tumor screening data from over 36,000 synthetic compounds screened against over 60 tumor cell lines, and replicate microarray gene expression measurements using one cell line and one compound. Data filtering, based on restricted cellular cytotoxicity profiles from chemically similar sets of compounds, has been used to select a class of benzothiazoles for subsequent microarray gene expression measurements in the most chemosensitive tumor cell line. The results confirmed observations that P450 metabolizing isoforms, CYP1A1 and CYP1B1, are overexpressed in MCF-7 tumor cells following treatment with benzothiazole. These results are consistent with the proposed inactivity of the CYP1A1-mediated metabolism of benzothiazole and the antitumor activity of the metabolically resistant halogenated forms.

6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase (HPPK) belongs to a class of catalytic enzymes involved in phosphoryl transfer and is a new target for the development of novel antimicrobial agents. In the present study, the fundamental consideration is to view the overall structure of HPPK as a network of interacting residues and to extract the most cooperative collective motions that define its global dynamics. A coarse-grained model, harmonically constrained according to HPPK's crystal structure is used. Four crystal structures of HPPK (one apo and three holo forms with different nucleotide and pterin analogs) are studied with the goal of providing insights about the function-dynamic correlation and ligand induced conformational changes. The dynamic differences are examined between HPPK's apo- and holo-forms, because they are involved in the catalytic reaction steps. Our results indicate that the palm-like structure of HPPK is nearly rigid, whereas the two flexible loops: L2 (residues 43-53) and L3 (residues 82-92) exhibit the most concerted motions for ligand recognition and presumably, catalysis. These two flexible loops are involved in the recognition of HPPKs nucleotide and pterin ligands, whereas the rigid palm region is associated with binding of these cognate ligands. Six domains of collective motions are identified, comprised of structurally close but not necessarily sequential residues. Two of these domains correspond to the flexible loops (L2 and L3), whereas the remaining domains correspond to the rigid part of the molecule., (Published 2002 Wiley-Liss, Inc.)

The crucial functions of HIV-1 nucleocapsid-p7 protein (NC-p7) at different stages of HIV replication are dependent on its nucleic acid binding properties. In this study, a search has been made to identify antagonists of the interaction between NC-p7 and d(TG)(4). A chemical library of approximately 2000 small molecules (the NCI Diversity Set) was screened, of the 26 active inhibitors that were identified, five contained a xanthenyl ring structure. Further analysis of 63 structurally related compounds led to the identification of 2,3,4,5-tetrachloro-6-(4('),5('),6(')-trihydroxy-3(')-oxo-3H-xanthen-9(')-yl)benzoic acid, which binds to NC-p7 stoichiometrically. This compound exerted a significant anti-HIV activity in vitro with an IC(50) of 16.6+/-4.3 microM (means+/-SD). Synthetic variants lacking the two hydroxyls at positions 4(') and 5(') in the xanthenyl ring system failed to bind NC-p7 and showed significantly less protection against HIV infection. Molecular modeling predicts that these hydroxyl groups would bind to the amide nitrogen of Gly(35) with other contacts at the carbonyl oxygens of Gly(40) and Lys(33).

The cytotoxic, cyclic depsipeptide (-)-doliculide was originally isolated by Ishiwata et al. (Ishiwata, H., Nemoto, T., Ojika, M., and Yamada, K. (1994) J. Org. Chem. 59, 4710-4711 and Ishiwata, H., Sone, H., Kigoshi, H., and Yamada, K. (1994) J. Org. Chem. 59, 4712-4713) from the sea hare Dolabella auricularia collected in Japanese waters, but the mechanism of action of the depsipeptide was not known. Using synthetic (-)-doliculide, we found that the compound arrests cells at the G(2)/M phase of the cell cycle by interfering with normal actin assembly. In cells, normal stress fibers disappeared and were replaced by multiple clumps of apparently aggregated F-actin. These effects of (-)-doliculide on cells were essentially identical to those obtained with jasplakinolide. Like jasplakinolide, (-)-doliculide caused the hyperassembly of purified actin into F-actin as measured both fluorometrically and by centrifugation. In addition, (-)-doliculide, like jasplakinolide, readily displaced a fluorescent phalloidin derivative from actin polymer. In these biochemical assays (-)-doliculide and jasplakinolide were quantitatively virtually identical in their behaviors. Similar effects have also been reported with a series of depsipeptides known as chondramides. Using recently developed, computer-driven shape descriptor analysis (Mansfield, M. L., Covell, D. G., and Jernigan, R. L. (2002) J. Chem. Inf. Comput. Sci. 42, 259-273), we compared (-)-doliculide with jasplakinolide, phalloidin, and chondramide C to gain insight into a possible pharmacophore that would explain the apparent binding of this diverse group of molecules at the same site on F-actin. We found that the segment of (-)-doliculide that best overlapped the other molecules encompassed its phenyl and isopropyl side chains and the portion of the macrocycle between these substituents.

Microtubules (MT), along with a variety of associated motor proteins, are involved in a range of cellular functions including vesicle movement, chromosome segregation, and cell motility. MTs are assemblies of heterodimeric proteins, alpha beta-tubulins, the structure of which has been determined by electron crystallography of zinc-induced, pacilitaxel-stabilized tubulin sheets. These data provide a basis for examining relationships between structural features and protein function. Here, we study the fluctuation dynamics of the tubulin dimer with the aim of elucidating its functional motions relevant to substrate binding, polymerization/depolymerization and MT assembly. A coarse-grained model, harmonically constrained according to the crystal structure, is used to explore the global dynamics of the dimer. Our results identify six regions of collective motion, comprised of structurally close but discontinuous sequence fragments, observed only in the dimeric form, dimerization being a prerequisite for domain identification. Boundaries between regions of collective motions appear to act as linkages, found primarily within secondary-structure elements that lack sequence conservation, but are located at minima in the fluctuation curve, at positions of hydrophobic residues. Residue fluctuations within these domains identify the most mobile regions as loops involved in recognition of the adjacent regions. The least mobile regions are associated with nucleotide binding sites where lethal mutations occur. The functional coupling of motions between and within regions identifies three global motions: torsional and wobbling movements, en bloc, between the alpha- and beta-tubulin monomers, and stretching longitudinally. Further analysis finds the antitumor drug pacilitaxel (TaxotereR) to reduce flexibility in the M loop of the beta-tubulin monomer; an effect that may contribute to tightening lateral interactions between protofilaments assembled into MTs. Our analysis provides insights into relationships between intramolecular tubulin movements of MT organization and function.

Increasing insight into the genetics and molecular biology of cancer has resulted in the identification of an increasing number of potential molecular targets for anti-cancer drug discovery and development. These targets can be approached through exploitation of emerging structural biology, "rational" drug design, screening of chemical libraries, or a combination of these methods. In this article we discuss the application of high-throughput screening to anti-cancer drug discovery, with special reference to approaches used at the U.S. National Cancer Institute.

The integrals V (n1, n2, n3) = integral dr x(n)1 y(n)2 z(n)3, where integral dr represents integration over the volume of a body, such as a molecule, where x, y, and z are Cartesian coordinates of a point in the interior of the body relative to an arbitrary reference frame, and where n1, n2, and n3 are integers greater than or equal to zero, constitute moments of the volume distribution of the body. Considering all such quantities for which 0 < or = n1 + n2 + n3 < or = 6 gives a set of 84 independent numbers which characterize the shape of the body and constitute a very useful set of shape descriptors. They also carry information about the absolute orientation and position of the body, and because their behavior under rotations and translations can be calculated quickly, they provide a fast, robust algorithm for the alignment of two similar molecules as well as a qualitative measure of their similarity. This paper reports the performance of the alignment algorithm on a learning set of about 80 different shapes. The algorithm is further tested against a set of small drug-like compounds that have been screened as anticancer agents. In both cases, excellent alignments of "shape-similar" molecules are obtained. Discussions are provided on many basic properties of these moments, e.g., their behavior under translations and rotations of the reference frame and their symmetry properties.

We have investigated three different microarray datasets of approximately 6 K gene expressions across the National Cancer Institute's panel of 60 tumor cell lines. Initial assessments of reproducibility for gene expressions within each dataset, as derived from sequence analysis of full-length sequences as well as expressed sequence tags (EST), found statistically significant results for no more than 36% of those cases where at least one replicate of a gene appears on the array. Filtering the data based only on pairwise comparisons among these three datasets creates a list of approximately 400 significant concordant expression patterns. The expression profiles of these smaller sets of genes were used to locate similar expression profiles of synthetic agents screened against these same 60 tumor cell lines. A correspondence was found between mRNA expression patterns and 50% growth inhibition response patterns of screened agents for 11 cases that were subsequently verifiable from ligand-target crystallographic data. Notable amongst these cases are genes encoding a variety of kinases, which were also found to be targets of small drug-like molecules within the database of protein structures. These 11 cases lend support to the premise that similarities between expression patterns and chemical responses for the National Cancer Institute's tumor panel can be related to known cases of molecular structure and putative cellular function. The details of the 11 verifiable cases and the concordant gene subsets are provided. Discussions about the prospects of using this approach as a data mining tool are included.

In an effort to enhance access to information available in the National Cancer Institute's (NCI) anticancer drug-screening database, a new suite of Internet accessible (http://spheroid. ncifcrf.gov) computational tools has been assembled for self-organizing map-based (SOM) cluster analysis and data visualization. A range of analysis questions were initially addressed to evaluate improvements in SOM cluster quality based on the data-conditioning procedures of Z-score normalization, capping, and treatment of missing data as well as completeness of drug cell-screening data. These studies established a foundation for SOM cluster analysis of the complete set of NCI's publicly available antitumor drug-screening data. This analysis identified relationships between chemotypes of screened agents and their effect on four major classes of cellular activities: mitosis, nucleic acid synthesis, membrane transport and integrity, and phosphatase- and kinase-mediated cell cycle regulation. Validations of these cellular activities, obtained from literature sources, found (i) strong evidence supporting within cluster memberships and shared cellular activity, (ii) indications of compound selectivity between various types of cellular activity, and (iii) strengths and weaknesses of the NCI's antitumor drug screen data for assigning compounds to these classes of cellular activity. Subsequent analyses of averaged responses within these tumor panel types find a strong dependence on chemotype for coherence among cellular response patterns. The advantages of a global analysis of the complete screening data set are discussed.

The dynamics of the GroEL-GroES complex is investigated with a coarse-grained model. This model is one in which single-residue points are connected to other such points, which are nearby, by identical springs, forming a network of interactions. The nature of the most important (slowest) normal modes reveals a wide variety of motions uniquely dependent upon the central cavity of the structure, including opposed torsional rotation of the two GroEL rings accompanied by the alternating compression and expansion of the GroES cap binding region, bending, shear, opposed radial breathing of the cis and trans rings, and stretching and contraction along the protein assembly's long axis. The intermediate domains of the subunits are bifunctional due to the presence of two hinges, which are alternatively activated or frozen by an ATP-dependent mechanism. ATP binding stabilizes a relatively open conformation (with respect to the central cavity) and hinders the motion of the hinge site connecting the intermediate and equatorial domains, while enhancing the flexibility of the second hinge that sets in motion the apical domains. The relative flexibilities of the hinges are reversed in the nucleotide-free form. Cooperative cross-correlations between subunits provide information about the mechanism of action of the protein. The mechanical motions driven by the different modes provide variable binding surfaces and variable sized cavities in the interior to enable accommodation of a broad range of protein substrates. These modes of motion could be used to manipulate the substrate's conformations.

Tubulin is the target for an ever increasing number of unusual peptides and depsipeptides that were originally isolated from a wide variety of organisms. Since tubulin is the major component of cellular microtubules, which maintain cell shape in interphase and form the mitotic spindle, most of these compounds are highly toxic to mammalian cells. These peptides and depsipeptides disrupt cellular microtubules and prevent formation of a functional spindle, resulting in the accumulation of cultured cells in the G2/M phase of the cell cycle through specific inhibition of mitosis. At the biochemical level, the compounds all inhibit the assembly of tubulin into polymer and, in the cases where it has been studied, strongly suppress microtubule dynamics at low concentrations. In most cases the peptides and depsipeptides inhibit the binding of vinblastine and vincristine to tubulin in a noncompetitive manner, inhibit tubulin-dependent GTP hydrolysis, and interfere with nucleotide turnover at the exchangeable GTP site on beta-tubulin. Most of the peptides and depsipeptides induce tubulin to form oligomers of aberrant morphology, including tubulin rings that vary in diameter depending on the (depsi) peptide under study. The purpose of this review is to give an overview of the cellular, biochemical, in vivo, and SAR aspects of this group of compounds. We also summarize initial efforts by computer modeling to decipher a pharmacophore among the diverse structures of these peptides and depsipeptides.

The chemical stability of 207 zinc fingers, derived from 92 experimental protein structures, is evaluated according to the protein packing and electrostatic screening of their zinc cores. These properties are used as measures of the protein protection of zinc cores, to predictively rank relative zinc finger reactivities and assess differences in function. On average, there is a substantial and concomitant increase in the screening of increasingly anionic core motifs, suggesting zinc fingers have evolved in a manner that promotes shielding of their potentially reactive core thiolates. In contrast, enzymatic zinc cores are functionally differentiated by negative electrostatic screening. Zinc finger cores are predominantly screened by networks of backbone:core NH-S hydrogen bonds that electronically stabilize core thiolates and enhance backbone packing. Stabilizing protein:core interactions can be mapped to conserved residues, including [Arg,Lys]:core salt-bridges in some protein families. Labile zinc fingers are identified by poorly screened cores, possibly indicating redox or metallothionein (MT) regulated function. Consistent with experiment, the cores of the C-terminal finger of the human immunodeficiency virus type 1 (HIV-1) nucleocapsid protein p7 (NCp7) and Escherichia coli Ada protein (Ada) "finger" are identified as reactive. The C-terminal zinc fingers of nuclear receptors are predicted to be the most labile in this study, particularly the human estrogen receptor (hER), which contains a triad of reactive thiolates. We propose that hER DNA binding is redox and MT regulated through the C-terminal finger and that weak electrophilic agents may inhibit hER-mediated transcription, implicated in breast cancer progression.

Protein C (PC), a 62 kDa multi-modular zymogen, is activated to an anticoagulant serine protease (activated PC or APC) by thrombin bound to thrombomodulin on the surface of endothelial cells. PC/APC interacts with many proteins and the characterisation of these interactions is not trivial. However, molecular modelling methods help to study these complex biological processes and provide basis for rational experimental design and interpretation of the results. PC/APC consists of a Gla domain followed by two EGF modules and a serine protease domain. In this report, we present two structural models for full-length APC and two equivalent models for full-length PC, based on the X-ray structures of Gla-domainless APC and of known serine protease zymogens. The overall elongated shape of the models is further cross-validated using size exclusion chromatography which allows evaluation of the Stokes radius (rs for PC = 33.15 A; rs for APC = 34.19 A), frictional ratio and axial ratio. We then propose potential binding sites at the surface of PC/APC using surface hydrophobicity as a determinant of the preferred sites of intermolecular recognition. Most of the predicted binding sites are consistent with previously reported experimental data, while some clusters highlight new regions that should be involved in protein-protein interactions.

2-Chloroacetyl-2-demethylthiocolchicine (2CTC) and 3-chloroacetyl-3-demethylthiocolchicine (3CTC) resemble colchicine in binding to tubulin and react covalently with beta-tubulin, forming adducts with cysteine residues 239 and 354. The adducts at Cys-239 are less stable than those at Cys-354 during formic acid digestion. Extrapolating to zero time, the Cys-239 to Cys-354 adduct ratio is 77:23 for 2CTC and 27:73 for 3CTC. Using energy minimization modeling to dock colchicinoids into the electron crystallographic model of beta-tubulin in protofilaments (Nogales, E. , Wolf, S. G., and Downing, K. H. (1998) Nature 391, 199-203), we found two potential binding sites. At one, entirely encompassed within beta-tubulin, the C2- and C3-oxygen atoms of 2CTC and 3CTC overlapped poorly with those of colchicine and thiocolchicine, but distances from the reactive carbon atoms of the analogs to the sulfur atoms of the cysteine residues were qualitatively consistent with reactivity. The other potential binding site was located at the alpha/beta interface. Here, the oxygen atoms of the analogs overlapped well with those of colchicine, but relative distances of the reactive carbons to the cysteine sulfur atoms did not correlate with the observed reactivity. A significant conformational change must occur in the colchicine binding site of tubulin in the transition from the unpolymerized to the polymerized state.

An analysis of the growth inhibitory potency of 122 anticancer agents available from the National Cancer Institute anticancer drug screen is presented. Methods of singular value decomposition (SVD) were applied to determine the matrix of distances between all compounds. These SVD-derived dissimilarity distances were used to cluster compounds that exhibit similar tumor growth inhibitory activity patterns against 60 human cancer cell lines. Cluster analysis divides the 122 standard agents into 25 statistically distinct groups. The first eight groups include structurally diverse compounds with reactive functionalities that act as DNA-damaging agents while the remaining 17 groups include compounds that inhibit nucleic acid biosynthesis and mitosis. Examination of the average activity patterns across the 60 tumor cell lines reveals unique 'fingerprints' associated with each group. A diverse set of structural features are observed for compounds within these groups, with frequent occurrences of strong within-group structural similarities. Clustering of cell types by their response to the 122 anticancer agents divides the 60 cell types into 21 groups. The strongest within-panel groupings were found for the renal, leukemia and ovarian cell panels. These results contribute to the basis for comparisons between log(GI(50)) screening patterns of the 122 anticancer agents and additional tested compounds.

Abstract By treating an enzyme as a coarse-grained uniform block of material, utilizing only the α-Carbon positions, the normal modes of motion can be obtained. For reverse transcriptase the slower of these motions are suggestive of being involved in the processing step, where the RNA or DNA strand is copied onto a new DNA strand at a polymerase site, and the RNA strand is subsequently cut up at the distant Ribonuclease H site. The slowest mode of motion involves hinge bending about a site midway between the polymerase and Ribonuclease H sites, suggesting that it can push or pull the RNA strand between these two sites. Pulling the nucleic acid strand would require tight binding to the RNase H site. The next slowest mode involves a hinge that opens and closes the protein like a clamp, which could facilitate the release of the nucleic acids for their step-wise progression. The third mode could rotate the substrate. An overall description of the step-wise processing step would involve close coordination among these steps. Results suggest that the smaller p51 subunit serves only as ballast to support the various modes of motion involving the different parts of the p66 subunit.

A previously published computational procedure was used to identify cooperative folding units within tryptophan repressor. The theoretical results predict the existence of distinct stable substructures in the protein chain for the monomer and the dimer. The predictions were compared with experimental data on structure and folding of the repressor and its proteolytic fragments and show excellent agreement for the dimeric form of the protein. The results suggest that the monomer, the structure of which is currently unknown, is likely to have a structure different from the one it has within the context of the highly intertwined dimer. Application of this method to the repressor monomer represents an extension of the computations into the realm of evaluating hypothetical structures such as those produced by threading.

In order to study the inferences of structure for mechanism, the collective motions of the retroviral reverse transcriptase HIV-1 RT (RT) are examined using the Gaussian network model (GNM) of proteins. This model is particularly suitable for elucidating the global dynamic characteristics of large proteins such as the presently investigated heterodimeric RT comprising a total of 982 residues. Local packing density and coordination order of amino acid residues is inspected by the GNM to determine the type and range of motions, both at the residue level and on a global scale, such as the correlated movements of entire subdomains. Of the two subunits, p66 and p51, forming the RT, only p66 has a DNA-binding cleft and a functional polymerase active site. This difference in the structure of the two subunits is shown here to be reflected in their dynamic characteristics: only p66 has the potential to undergo large-scale cooperative motions in the heterodimer, while p51 is essentially rigid. Taken together, the global motion of the RT heterodimer is comprised of movements of the p66 thumb subdomain perpendicular to those of the p66 fingers, accompanied by anticorrelated fluctuations of the RNase H domain and p51 thumb, thus providing information about the details of one processivity mechanism. A few clusters of residues, generally distant in sequence but close in space, are identified in the p66 palm and connection subdomains, which form the hinge-bending regions that control the highly concerted motion of the subdomains. These regions include the catalytically active site and the non-nucleoside inhibitor binding pocket of p66 polymerase, as well as sites whose mutations have been shown to impair enzyme activity. It is easily conceivable that this hinge region, indicated by GNM analysis to play a critical role in modulating the global motion, is locked into an inactive conformation upon binding of an inhibitor. Comparative analysis of the dynamic characteristics of the unliganded and liganded dimers indicates severe repression of the mobility of the p66 thumb in RT's global mode, upon binding of non-nucleoside inhibitors., (Copyright 1999 Academic Press.)

Nucleocapsid p7 protein (NCp7) zinc finger domains of the human immunodeficiency virus type 1 (HIV-1) are being developed as antiviral targets due to their key roles in viral replication and their mutationally nonpermissive nature. On the basis of our experience with symmetrical disulfide benzamides (DIBAs; Rice et al. Science 1995, 270, 1194-1197), we synthesized and evaluated variants of these dimers, including sets of 4,4'- and 3,3'-disubstituted diphenyl sulfones and their monomeric benzisothiazolone derivatives (BITA). BITAs generally exhibited diminished antiviral potency when compared to their disulfide precursors. Novel, monomeric structures were created by linking haloalkanoyl groups to the benzamide ring through -NH-C(=O)- (amide) or -S-C(=O)- (thiolester) bridges. Amide-linked compounds generally lacked antiviral activity, while haloalkanoyl thiolesters and non-halogen-bearing analogues frequently exhibited acceptable antiviral potency, thus establishing thiolester benzamides per se as a new anti-HIV chemotype. Pyridinioalkanoyl thiolesters (PATEs) exhibited superior anti-HIV-1 activity with minimal cellular toxicity and appreciable water solubility. PATEs were shown to preferentially target the NCp7 Zn finger when tested against other molecular targets, thus identifying thiolester benzamides, and PATEs in particular, as novel NCp7 Zn finger inhibitors for in vivo studies.

C4b-binding protein (C4BP) is a major regulatory molecule of the complement system. By forming a non covalent complex with the anticoagulant cofactor protein S (PS), it also plays an important role in blood coagulation. C4BP is composed of one beta-chain and seven alpha-chains that are essentially built from complement control protein (CCP)-modules. Our group has previously reported that the first (N-terminal) CCP module of the beta-chain (betaCCP1) contains the entire binding site for protein S. We now investigate further the binding of protein S to C4BP and show that the complex formation is essentially dependent on hydrophobic forces with minor contribution from electrostatic interactions. This result is in agreement with homology modeling experiments carried out in conjunction with inter-species sequence comparison and theoretical enumeration of potential binding sites. These methods pinpoint a solvent exposed hydrophobic cluster at the surface of the betaCCP1 module that is of crucial importance for the binding process.

The reaction of the human immunodeficiency virus type 1 (HIV-1) nucleocapsid protein p7 (NCp7) with a variety of electrophilic agents was investigated by experimental measurements of Trp37 fluorescence decay and compared with theoretical measures of reactivity based on density-functional theory in the context of the hard and soft acids and bases principle. Statistically significant correlations were found between rates of reaction and the ability of these agents to function as soft electrophiles. Notably, the molecular property that correlated strongest was the ratio of electronegativity to hardness, chi2/eta, a quantity related to the capacity of an electrophile to promote a soft (covalent) reaction. Electronic and steric determinants of the reaction were also probed by Fukui function and frontier-orbital overlap analysis in combination with protein-ligand docking methods. This analysis identified selective ligand docking regions within the conserved zinc finger domains that promoted reaction. The Cys49 thiolate was found overall to be the NCp7 site most susceptible to electrophilic attack.