Reactions of (-)-sparteine with alkali metal HMDS complexes : conventional meets the unconventional

Conventional (-)-sparteine adducts of lithium and sodium 1,1,1,3,3,3-hexamethyldisilazide (HMDS) were prepared and characterised, along with an unexpected and unconventional hydroxyl-incorporated sodium sodiate, [(-)-sparteine·Na(-HMDS)Na·(-)-sparteine]+[Na4(-HMDS)4(OH)]--the complex anion of which is the first inverse crown ether anion

Identification of a Novel Pseudo-Natural Product Type IV IDO1 Inhibitor Chemotype

Natural product (NP)-inspired design principles provide invaluable guidance for bioactive compound discovery. Pseudo-natural products (PNPs) are de novo combinations of NP fragments to target biologically relevant chemical space not covered by NPs. We describe the design and synthesis of apoxidoles, a novel pseudo-NP class, whereby indole- and tetrahydropyridine fragments are linked in monopodal connectivity not found in nature. Apoxidoles are efficiently accessible by an enantioselective [4+2] annulation reaction. Biological evaluation revealed that apoxidoles define a new potent type IV inhibitor chemotype of indoleamine 2,3-dioxygenase 1 (IDO1), a heme-containing enzyme considered a target for the treatment of neurodegeneration, autoimmunity and cancer. Apoxidoles target apo-IDO1, prevent heme binding and induce unique amino acid positioning as revealed by crystal structure analysis. Novel type IV apo-IDO1 inhibitors are in high demand, and apoxidoles may provide new opportunities for chemical biology and medicinal chemistry research

Exploring the solid state and solution structural chemistry of the utility amide potassium hexamethyldisilazide (KHMDS)

The structural chemistry of eleven donor complexes of the important Brønsted base potassium 1,1,1,3,3,3-hexamethyldisilazide (KHMDS) has been studied. Depending on the donor, each complex adopted one of four general structural motifs. Specifically, in this study the donors employed were toluene (to give polymeric 1 and dimeric 2), THF (dimeric 3), N,N,N',N'-tetramethylethylenediamine (TMEDA) (dimeric 4), (R,R)-N,N,N',N'-tetramethyl-1,2-diaminocyclohexane [(R,R)-TMCDA] (dimeric 5), 12-crown-4 (dimeric 6), N,N,N',N'-tetramethyldiaminoethyl ether (TMDAE) (tetranuclear dimeric 8 and monomeric 10), N,N,N',N',N''-pentamethyldiethylentriamine (PMDETA) (tetranuclear dimeric 7), tris[2-dimethyl(amino)ethyl]amine (Me6TREN) (tetranuclear dimeric 9) and tris{2-(2-methoxyethoxy)ethyl}amine (TMEEA) (monomeric 11). The complexes were also studied in solution by 1H and 13C NMR spectroscopy as well as DOSY NMR spectroscopy

The highly potent AhR agonist picoberin modulates Hh-dependent osteoblast differentiation

Identification and analysis of small molecule bioactivity in target-agnostic cellular assays and monitoring changes in phenotype followed by identification of the biological target are a powerful approach for the identification of novel bioactive chemical matter in particular when the monitored phenotype is disease-related and physiologically relevant. Profiling methods that enable the unbiased analysis of compound-perturbed states can suggest mechanisms of action or even targets for bioactive small molecules and may yield novel insights into biology. Here we report the enantioselective synthesis of natural-product-inspired 8-oxotetrahydroprotoberberines and the identification of Picoberin, a low picomolar inhibitor of Hedgehog (Hh)-induced osteoblast differentiation. Global transcriptome and proteome profiling revealed the aryl hydrocarbon receptor (AhR) as the molecular target of this compound and identified a cross talk between Hh and AhR signaling during osteoblast differentiation

The pseudo‐natural product rhonin targets RHOGDI

For the discovery of novel chemical matter generally endowed with bioactivity, strategies may be particularly efficient that combine previous insight about biological relevance, e.g., natural product (NP) structure, with methods that enable efficient coverage of chemical space, such as fragment-based design. We describe the de novo combination of different 5-membered NP-derived N-heteroatom fragments to structurally unprecedented “pseudo-natural products” in an efficient complexity-generating and enantioselective one-pot synthesis sequence. The pseudo-NPs inherit characteristic elements of NP structure but occupy areas of chemical space not covered by NP-derived chemotypes, and may have novel biological targets. Investigation of the pseudo-NPs in unbiased phenotypic assays and target identification led to the discovery of the first small-molecule ligand of the RHO GDP-dissociation inhibitor 1 (RHOGDI1), termed Rhonin. Rhonin inhibits the binding of the RHOGDI1 chaperone to GDP-bound RHO GTPases and alters the subcellular localization of RHO GTPases

The pseudo‐natural product rhonin targets RHOGDI

For the discovery of novel chemical matter generally endowed with bioactivity, strategies may be particularly efficient that combine previous insight about biological relevance, e.g., natural product (NP) structure, with methods that enable efficient coverage of chemical space, such as fragment-based design. We describe the de novo combination of different 5-membered NP-derived N-heteroatom fragments to structurally unprecedented “pseudo-natural products” in an efficient complexity-generating and enantioselective one-pot synthesis sequence. The pseudo-NPs inherit characteristic elements of NP structure but occupy areas of chemical space not covered by NP-derived chemotypes, and may have novel biological targets. Investigation of the pseudo-NPs in unbiased phenotypic assays and target identification led to the discovery of the first small-molecule ligand of the RHO GDP-dissociation inhibitor 1 (RHOGDI1), termed Rhonin. Rhonin inhibits the binding of the RHOGDI1 chaperone to GDP-bound RHO GTPases and alters the subcellular localization of RHO GTPases

Robust minimax probability machine regression. submitted, www.cs. colorado.edu/~strohman/tech.pdf

We formulate regression as maximizing the minimum probability (Ω) that the regression model is within ±ɛ of all future observations (i.e. outputs) of the true regression function. Our framework starts by posing regression as a binary classification problem, such that a solution to this single classification problem directly solves the original regression problem. Minimax probability machine classification (Lanckriet et al., 2002a) is used to solve the binary classification problem, resulting in a direct bound on the minimum probability Ω that the regression model is within ±ɛ accurate. This minimax probability machine regression (MPMR) model assumes only that the mean and covariance matrix of the distribution which generated the regression data are known; no further assumptions on conditional distributions are required. Theory is formulated for determining when estimates of mean and covariance are accurate, thus implying robust estimates of the probability bound Ω. Conditions under which the MPMR regression surface is identical to a standard least squares regression surface are given, allowing direct generalization bounds to be easily calculated for any least squares regression model (which was previously possible only under very specific, often unrealistic, distributional assumptions). We further generalize these theoretical bounds to any superposition of basis functions regression model. Experimental evidence is given supporting these theoretical results