Cold denaturation of encapsulated ubiquitin

Theoretical considerations suggest that protein cold denaturation can potentially provide a means to explore the cooperative substructure of proteins. Protein cold denaturation is generally predicted to occur well below the freezing point of water. Here NMR spectroscopy of ubiquitin encapsulated in reverse micelles dissolved in low viscosity alkanes is used to follow cold-induced unfolding to temperatures below -25 degrees C. Comparison of cold-induced structural transitions in a variety of reverse micelle-buffer systems indicate that protein-surfactant interactions are negligible and allow the direct observation of the multistate cold-induced unfolding of the protein.     

Biochemical suppression of small-molecule inhibitors: a strategy to identify inhibitor targets and signaling pathway components

Identification of small-molecule targets remains an important challenge for chemical genetics. We report an approach for target identification and protein discovery based on functional suppression of chemical inhibition in vitro. We discovered pirl1, an inhibitor of actin assembly, in a screen conducted with cytoplasmic extracts. Pirl1 was used to partially inhibit actin assembly in the same assay, and concentrated biochemical fractions of cytoplasmic extracts were added to find activities that suppressed pirl1 inhibition. Two activities were detected, separately purified, and identified as Arp2/3 complex and Cdc42/RhoGDI complex, both known regulators of actin assembly. We show that pirl1 directly inhibits activation of Cdc42/RhoGDI, but that Arp2/3 complex represents a downstream suppressor. This work introduces a general method for using low-micromolar chemical inhibitors to identify both inhibitor targets and other components of a signaling pathway.     

Using Copula distributions to support more accurate imaging-based diagnostic classifiers for neuropsychiatric disorders

Many investigators have tried to apply machine learning techniques to magnetic resonance images (MRIs) of the brain in order to diagnose neuropsychiatric disorders. Usually the number of brain imaging measures (such as measures of cortical thickness and measures of local surface morphology) derived from the MRIs (i.e., their dimensionality) has been large (e.g. > 10) relative to the number of participants who provide the MRI data (< 100). Sparse data in a high dimensional space increase the variability of the classification rules that machine learning algorithms generate, thereby limiting the validity, reproducibility, and generalizability of those classifiers. The accuracy and stability of the classifiers can improve significantly if the multivariate distributions of the imaging measures can be estimated accurately. To accurately estimate the multivariate distributions using sparse data, we propose to estimate first the univariate distributions of imaging data and then combine them using a Copula to generate more accurate estimates of their multivariate distributions. We then sample the estimated Copula distributions to generate dense sets of imaging measures and use those measures to train classifiers. We hypothesize that the dense sets of brain imaging measures will generate classifiers that are stable to variations in brain imaging measures, thereby improving the reproducibility, validity, and generalizability of diagnostic classification algorithms in imaging datasets from clinical populations. In our experiments, we used both computer-generated and real-world brain imaging datasets to assess the accuracy of multivariate Copula distributions in estimating the corresponding multivariate distributions of real-world imaging data. Our experiments showed that diagnostic classifiers generated using imaging measures sampled from the Copula were significantly more accurate and more reproducible than were the classifiers generated using either the real-world imaging measures or their multivariate Gaussian distributions. Thus, our findings demonstrate that estimated multivariate Copula distributions can generate dense sets of brain imaging measures that can in turn be used to train classifiers, and those classifiers are significantly more accurate and more reproducible than are those generated using real-world imaging measures alone.     

On The Validation of LES Applied to Internal Combustion Engine Flows: Part 1: Comprehensive Experimental Database

Improved understanding of in-cylinder flows requires knowledge from well-resolved experimental velocimetry measurements and flow simulation modeling. Engine simulations using large eddy simulations (LES) are making large progress and the need for well documented velocimetry measurements for model validation is high. This work presents velocimetry measurements from PIV, high-speed PIV, stereoscopic PIV, and tomographic PIV to extensively describe the in-cylinder flow field in a motored optical engine operating at 800 RPM. These measurements also establish a comprehensive database designed for LES model development and validation. Details of the engine, engine accessory components, and well-controlled boundary conditions and engine operation are presented. The first two statistical moments of the flow field are computed and show excellent agreement among the PIV database. Analysis of statistical moments based on limited sample size is presented and is important for modeling validation purposes. High-speed PIV resolved the instantaneous flow field throughout entire engine cycles (i.e. 719 consecutive crank-angles), while tomographic PIV images are further used to investigate the 3D flow field and identify regions of strong vortical structures identified by the Q-criterion. Principle velocity gradient components are computed and emphasize the need to resolve similar spatial scales between experimental and modeling efforts for suitable model validation.     

Mechanistic aspects of hydrosilylation catalyzed by (ArN=)Mo(H)(Cl)(PMe3)3

The reaction of (ArN=)MoCl(2)(PMe(3))(3) (Ar = 2,6-diisopropylphenyl) with L-Selectride gives the hydrido-chloride complex (ArN=)Mo(H)(Cl)(PMe(3))(3) (2). Complex 2 was found to catalyze the hydrosilylation of carbonyls and nitriles as well as the dehydrogenative silylation of alcohols and water. Compound 2 does not show any productive reaction with PhSiH(3); however, a slow H/D exchange and formation of (ArN=)Mo(D)(Cl)(PMe(3))(3) (2(D)) was observed upon addition of PhSiD(3). Reactivity of 2 toward organic substrates was studied. Stoichiometric reactions of 2 with benzaldehyde and cyclohexanone start with dissociation of the trans-to-hydride PMe(3) ligand followed by coordination and insertion of carbonyls into the Mo-H bond to form alkoxy derivatives (ArN=)Mo(Cl)(OR)(PMe(2))L(2) (3: R = OCH(2)Ph, L(2) = 2 PMe(3); 5: R = OCH(2)Ph, L(2) = η(2)-PhC(O)H; 6: R = OCy, L(2) = 2 PMe(3)). The latter species reacts with PhSiH(3) to furnish the corresponding silyl ethers and to recover the hydride 2. An analogous mechanism was suggested for the dehydrogenative ethanolysis with PhSiH(3), with the key intermediate being the ethoxy complex (ArN=)Mo(Cl)(OEt)(PMe(3))(3) (7). In the case of hydrosilylation of acetophenone, a D-labeling experiment, i.e., a reaction of 2 with acetophenone and PhSiD(3) in the 1:1:1 ratio, suggests an alternative mechanism that does not involve the intermediacy of an alkoxy complex. In this particular case, the reaction presumably proceeds via Lewis acid catalysis. Similar to the case of benzaldehyde, treatment of 2 with styrene gives trans-(ArN=)Mo(H)(η(2)-CH(2)═CHPh)(PMe(3))(2) (8). Complex 8 slowly decomposes via the release of ethylbenzene, indicating only a slow insertion of styrene ligand into the Mo-H bond of 8.     

New mechanisms of 5-nitrofuran toxicity

Phenotypic screens and the target identification that follows can lead to surprising new connections between small molecules and targets. In this issue of Chemistry & Biology, Zhou and colleagues seek to understand the pigmentation of fish skin and end up uncovering an interesting clinical hypothesis about the trypanocidal compound nifurtimox.     

Stimulation of natural killer cells with small molecule inhibitors of CD38 for the treatment of neuroblastoma

High-risk neuroblastoma (NB) accounts for 15% of all pediatric cancer deaths. Refractory disease for high-risk NB patients is attributed to chemotherapy resistance and immunotherapy failure. The poor prognosis for high-risk NB patients demonstrates an unmet medical need for the development of new, more efficacious therapeutics. CD38 is an immunomodulating protein that is expressed constitutively on natural killer (NK) cells and other immune cells in the tumor microenvironment (TME). Furthermore, CD38 over expression is implicated in propagating an immunosuppressive milieu within the TME. Through virtual and physical screening, we have identified drug-like small molecule inhibitors of CD38 with low micromolar IC₅₀ values. We have begun to explore structure activity relationships for CD38 inhibition through derivatization of our most effective hit molecule to develop a new compound with lead-like physicochemical properties and improved potency. We have demonstrated that our derivatized inhibitor, compound 2, elicits immunomodulatory effects in NK cells by increasing cell viability by 190 ± 36% in multiple donors and by significantly increasing interferon gamma. Additionally, we have illustrated that NK cells exhibited enhanced cytotoxicity toward NB cells (14% reduction of NB cells over 90 minutes) when given a combination treatment of our inhibitor and the immunocytokine ch14.18-IL2. Herein we describe the synthesis and biological evaluation of small molecule CD38 inhibitors and demonstrate their potential utility as a novel approach to NB immunotherapy. These compounds represent the first examples of small molecules that stimulate immune function for the treatment of cancer.

Small molecule inhibitors of CD38 promote increases in interferon gamma and stimulate natural killer cell proliferation for the treatment of neuroblastoma.     

The analogue of Weyl's conformal curvature tensor in a Michal functional geometry

The purpose of this note is to obtain a set of functionals conformally invariant in a Michal functional geometry. The author is indebted to prof.A. D. Michal for calling his attention to this problem and for helpful suggestions and criticisms in the preparation of this paper.