Tunable Probes with Direct Fluorescence Signals for the Constitutive and Immunoproteasome

Electrophiles are commonly used for the inhibition of proteases. Notably, inhibitors of the proteasome, a central determinant of cellular survival and a target of several FDA‐approved drugs, are mainly characterized by the reactivity of their electrophilic head groups. We aimed to tune the inhibitory strength of peptidic sulfonate esters by varying the leaving groups. Indeed, proteasome inhibition correlated well with the pK_a of the leaving group. The use of fluorophores as leaving groups enabled us to design probes that release a stoichiometric fluorescence signal upon reaction, thereby directly linking proteasome inactivation to the readout. This principle could be applicable to other sulfonyl fluoride based inhibitors and allows the design of sensitive probes for enzymatic studies.     

Towards Photochromic Azobenzene-Based Inhibitors for Tryptophan Synthase

Light regulation of drug molecules has gained growing interest in biochemical and pharmacological research in recent years. In addition, a serious need for novel molecular targets of antibiotics has emerged presently. Herein, the development of a photocontrollable, azobenzene-based antibiotic precursor towards tryptophan synthase (TS), an essential metabolic multienzyme complex in bacteria, is presented. The compound exhibited moderately strong inhibition of TS in its E configuration and five times lower inhibition strength in its Z configuration. A combination of biochemical, crystallographic, and computational analyses was used to characterize the inhibition mode of this compound. Remarkably, binding of the inhibitor to a hitherto-unconsidered cavity results in an unproductive conformation of TS leading to noncompetitive inhibition of tryptophan production. In conclusion, we created a promising lead compound for combatting bacterial diseases, which targets an essential metabolic enzyme, and whose inhibition strength can be controlled with light.     

Discriminant Analysis of Selected Organics Evaluated as Corrosion Inhibitors in 0.5 M HCL

Abstract

Discriminant analysis can be used to (1) find criteria to separate observations into groups and (2) to optimally assign a new observation to its correct group. It is rather exploratory in nature especially when causal relationships are not known. Twenty four selected organics were tested as corrosion inhibitors and both physical and structural features were encoded as criteria for discrimination. In addition, a narrower set of twelve nitrogen containing compounds was also studied. In both cases the carbon chain length was an important uncoded criterion. The physical factors such as boiling point, molecular weight, pKa, and molecular area were not as important as some of the structural parameters.     

An improved ensemble learning machine for biological activity prediction of tyrosine kinase inhibitors

Boosting is one of the most important strategies in ensemble learning because of its ability to improve the stability and performance of weak learners. It is nonparametric, multivariate, fast and interpretable but is not robust against outliers. To enhance its prediction accuracy as well as immunize it against outliers, a modified version of a boosting algorithm (AdaBoost R2) was developed and called AdaBoost R3. In the sampling step, extremum samples were added to the boosting set. In the robustness step, a modified Huber loss function was applied to overcome the outlier problem. In the output step, a deterministic threshold was used to guarantee that bad predictions do not participate in the final output. The performance of the modified algorithm was investigated with two anticancer data sets of tyrosine kinase inhibitors, and the mechanism of inhibition was studied using the relative weighted variable importance procedure. Investigating the effect of base learner's strength reveals that boosting is only successful using the classification and regression tree method (a weak to moderate learner) and does not have a significant effect using the radial basis functions partial least square method (a strong base learners). Copyright © 2015 John Wiley & Sons, Ltd.     

CONFORMATIONAL PREFERENCES ABOUT Cpy-S BONDS IN DI-2-PYRIDYL DISULFIDE

Abstract

The total energy, dipole moment and electron densities for each possible rotational conformation about the C_py-S bonds of di-2-pyridyl disulfide were evaluated by using the semi-quantitative CNDO/2 method. The conformations in which the pyridine rings are coplanar with the valency plane of the bonded sulfur atom (cis-cis, cis-trans and trans-trans) were predicted to be the most favored ones.

Results of the theoretical study, when compared to some experimental determinations such as dipole moment and variable temperature pmr spectra, provided evidence that easy interconversion between these conformations can occur.     

CoMFA and CoMSIA studies on C-aryl glucoside SGLT2 inhibitors as potential anti-diabetic agents

SGLT2 has become a target of therapeutic interest in diabetes research. CoMFA and CoMSIA studies were performed on C-aryl glucoside SGLT2 inhibitors (180 analogues) as potential anti-diabetic agents. Three different alignment strategies were used for the compounds. The best CoMFA and CoMSIA models were obtained by means of Distill rigid body alignment of training and test sets, and found statistically significant with cross-validated coefficients (q ²) of 0.602 and 0.618, respectively, and conventional coefficients (r ²) of 0.905 and 0.902, respectively. Both models were validated by a test set of 36 compounds giving satisfactory predicted correlation coefficients (r ² _pred) of 0.622 and 0.584 for CoMFA and CoMSIA models, respectively. A comparison was made with earlier 3D QSAR study on SGLT2 inhibitors, which shows that our 3D QSAR models are better than earlier models to predict good inhibitory activity. CoMFA and CoMSIA models generated in this work can provide useful information to design new compounds and helped in prediction of activity prior to synthesis.