SUMMARY Bcl-2 can be converted into a pro-apoptotic molecule by nuclear receptor Nur77. However, the development of Bcl-2 converters as anti-cancer therapeutics has not been explored. Here we report the identification of a Nur77-derived Bcl-2 converting peptide with 9 amino acids (NuBCP-9) and its enantiomer, which induce apoptosis of cancer cells in vitro and in animals. The apoptotic effect of NuBCPs and their activation of Bax are not inhibited but rather potentiated by Bcl-2. NuBCP-9 enantiomers bind to the Bcl-2 loop, which shares the characteristics of structurally adaptable regions with many cancer-associated and signaling proteins. NuBCP-9s act as molecular switches to dislodge the Bcl-2 BH4 domain, exposing its BH3 domain, which in turn blocks the activity of anti-apoptotic Bcl-XL.

The low-temperature electron microscope, which preserves aqueous structures as solid water at liquid nitrogen temperature, was used to image the alveolar lining layer, including surfactant and its aqueous subphase, of air-filled lungs frozen in anesthetized rats at 15-cmH2O transpulmonary pressure. Lining layer thickness was measured on cross fractures of walls of the outermost subpleural alveoli that could be solidified with metal mirror cryofixation at rates sufficient to limit ice crystal growth to 10 nm and prevent appreciable water movement. The thickness of the liquid layer averaged 0.14 micron over relatively flat portions of the alveolar walls, 0.89 micron at the alveolar wall junctions, and 0.09 micron over the protruding features (9 rats, 20 walls, 16 junctions, and 146 areas), for an area-weighted average thickness of 0.2 micron. The alveolar lining layer appears continuous, submerging epithelial cell microvilli and intercellular junctional ridges; varies from a few nanometers to several micrometers in thickness, and serves to smooth the alveolar air-liquid interface in lungs inflated to zone 1 or 2 conditions.

Natural products from plants, animals, marine life, fungi, bacteria, and other organisms are an important resource for modern drug discovery. Their biological relevance and structural diversity make natural products good starting points for drug design. Natural product-based drug discovery can benefit greatly from computational approaches, which are a valuable precursor or supplementary method to in vitro testing. We present an overview of 25 virtual and 31 physical natural product libraries that are useful for applications in cheminformatics, in particular virtual screening. The overview includes detailed information about each library, the extent of its structural information, and the overlap between different sources of natural products. In terms of chemical structures, there is a large overlap between freely available and commercial virtual natural product libraries. Of particular interest for drug discovery is that at least ten percent of known natural products are readily purchasable and many more natural products and derivatives are available through on-demand sourcing, extraction and synthesis services. Many of the readily purchasable natural products are of small size and hence of relevance to fragment-based drug discovery. There are also an increasing number of macrocyclic natural products and derivatives becoming available for screening.

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Natural products remain one of the most productive sources of chemical inspiration for the development of new drugs. The structures of more than 250 000 natural products are available from public databases. At least 10% of these compounds are readily obtainable for experimental testing from commercial vendors and public research institutions. While the physicochemical properties of known natural products have been thoroughly studied and compared to those of drugs and other types of small molecules, the information available on the content, coverage, and relevance of individual virtual and physical natural product libraries is clearly limited. The aim of this study was the development of a detailed understanding of the coverage of chemical space by known and readily obtainable natural products and by individual natural product databases. For this purpose, we compiled comprehensive data sets of known and readily obtainable natural products from 18 virtual databases (including the Dictionary of Natural Products), nine physical libraries, and the Protein Data Bank (PDB). We also developed and employed an algorithm ("SugarBuster") for the removal of sugars and sugar-like moieties, which are generally not in the focus of interest for drug discovery, from natural products. In addition, we devised a rule-based approach for the automated classification of natural products into natural product classes (alkaloids, steroids, flavonoids, etc.). Among the most important results of this study is the finding that the readily obtainable natural products are highly diverse and populate regions of chemical space that are of high relevance to drug discovery. In some cases, substantial differences in the coverage of natural product classes and chemical space by the individual databases are observed. More than 2000 natural products are identified for which at least one X-ray crystal structure of the compound in complex with a biomacromolecule is available from the PDB.

This review seeks to provide a timely survey of the scope and limitations of cheminformatics methods in natural product-based drug discovery. Following an overview of data resources of chemical, biological and structural information on natural products, we discuss, among other aspects, in silico methods for (i) data curation and natural products dereplication, (ii) analysis, visualization, navigation and comparison of the chemical space, (iii) quantification of natural product-likeness, (iv) prediction of the bioactivities (virtual screening, target prediction), ADME and safety profiles (toxicity) of natural products, (v) natural productsinspired de novo design and (vi) prediction of natural products prone to cause interference with biological assays. Among the many methods discussed are rule-based, similarity-based, shape-based, pharmacophore-based and network-based approaches, docking and machine learning methods.

We have recently identified a series of compounds which efficiently inhibit Anthrax lethal factor (LF) metallo-protease. Here we present further structure activity relationship and CoMFA (Comparative Molecular Field Analysis) studies on newly derived inhibitors. The obtained 3D QSAR model was subsequently compared with the X-ray structure of the complex between LF and a representative compound. Our studies form the basis for the rational design of additional compounds with improved activity and selectivity.Anthrax is an infectious disease caused by the bacterium Bacillus anthracis. 1 This rod shaped bacterium infects humans through the respiratory system, skin, or digestive tract. Dependent upon the entry route into the human body, Anthrax can be highly lethal. Although cutaneous Anthrax is rarely lethal, inhalation Anthrax is dangerous and usually fatal. 2 Upon inhalation, the Anthrax spores adhere to the alveolar macrophages and germinate. Bacteria migrate to the lymph node, in which they rapidly multiply and excrete a tripartite exotoxin comprised of protective antigen (PA, 83 kDa), lethal factor (LF, Zn 2+ -metalloproteinase, 90 kDa) and calmodulin-activated edema factor adenylate cyclase (EF, 89 kDa). 3,4 The combined actions of these proteins constitute the Anthrax toxins (AT) which induce cell death. Unless properly and promptly treated, inhalation anthrax will lead to the death of the host organism. 5 Initially, PA binds to an AT receptor on the host cell surface, where it is cleaved by a furinlike protease to produce a 20 kDa N-terminal fragment (PA 20 ) and a 63 kDa C-terminal fragment (PA 63 ). 6 PA 63 , which remains bound to the membrane, oligomerizes into a heptameric prepore capable of binding LF and EF. 7 Upon binding of LF and EF, the complex undergoes receptor mediated endocytosis and the PA 63 conformational change allows the two enzymatic moieties LF and EF to translocate into the cell cytosol. Once in the cytosol, LF is then able to cleave several members of the MAPKK family near the Nterminus. [8][9][10] This cleavage prevents interaction with, and phosphorylation of, downstream MAPK, thereby inhibiting one or more signaling pathways through a mechanism not yet understood. 11 * To Whom Correspondence should be addressed. With the long term goal of developing novel potential treatments for Anthrax disease, we previously identified several small molecule inhibitors that inhibit Anthrax LF protease activity with IC 50 's in sub-micromolar range. 12 Cell based and peptide cleavage assays were subsequently used to confirm the potency of the iterate leads. The most potent compounds were subsequently tested in mice models of the diseases showing a protection against Bacillus anthracis spores, when used in combination with the antibiotic ciproflaxin. 12 Initial structure activity relationship (SAR) data suggested that the presence of multiple substitutions on the phenyl ring significantly increases the inhibitory activity. 12 Furthermore, details of the 3D structure of the complex between ...

Assay interference caused by small molecules continues to pose a significant challenge for early drug discovery. A number of rule-based and similarity-based approaches have been derived that allow the flagging of potentially "badly behaving compounds", "bad actors" or "nuisance compounds". These compounds are typically aggregators, reactive compounds and/or pan-assay interference compounds (PAINS), and many of them are frequent hitters. Hit Dexter is a recently introduced machine learning approach that predicts frequent hitters independent of the underlying physicochemical mechanisms (including also the binding of compounds based on "privileged scaffolds" to multiple binding sites). Here we report on the development of a second generation of machine learning models which now cover both primary screening assays and confirmatory dose-response assays. Protein sequence clustering was newly introduced to minimize the overrepresentation of structurally and functionally related proteins. The models correctly classified compounds of large independent test sets as (highly) promiscuous or nonpromiscuous with Matthews correlation coefficient (MCC) values of up to 0.64 and area under the receiver operating characteristic curve (AUC) values of up to 0.96. The models were also utilized to characterize sets of compounds with specific biological and physicochemical properties, such as dark chemical matter, aggregators, compounds from a high-throughput screening library, drug-like compounds, approved drugs, potential PAINS and natural products.Among the most interesting outcomes is that the new Hit Dexter models predict the presence of large fractions of (highly) promiscuous compounds among approved drugs. Importantly, predictions of the individual Hit Dexter models are generally in good agreement and consistent with those of Badapple, an established statistical model for the prediction of frequent hitters. The new Hit Dexter 2.0 web service, available at http://hitdexter2.zbh.uni-hamburg.de, not only provides user-friendly access to all machine learning models presented in this work but also to similarity-based methods for the prediction of aggregators and dark chemical matter as well as a comprehensive collection of available rule sets for flagging frequent hitters and compounds including undesired substructures.