Site-selective chlorination of pyrrolic heterocycles by flavin dependent enzyme PrnC.

Halogenation of pyrrole requires strong electrophilic reagents and often leads to undesired polyhalogenated products. Biocatalytic halogenation is a highly attractive approach given its chemoselectivity and benign reaction conditions. While there are several reports of enzymatic phenol and indole halogenation in organic synthesis, corresponding reports on enzymatic pyrrole halogenation have been lacking. Here we describe the in vitro functional and structural characterization of PrnC, a flavin-dependent halogenase that can act on free-standing pyrroles. Computational modeling and site mutagenesis studies identified three key residues in the catalytic pocket. A moderate resolution map using single-particle cryogenic electron microscopy reveals PrnC to be a dimer. This native PrnC can halogenate a library of structurally diverse pyrrolic heterocycles in a site-selective manner and be applied in the chemoenzymatic synthesis of a chlorinated analog of the agrochemical fungicide Fludioxonil.

Direct arene trifluoromethylation enabled by promiscuous activity of fungal laccase.

Laccase from Trametes versicolor was found to oxidize non-phenolic arenes and enable the trifluoromethylation of arenes in the presence of in situ generated CF3 radicals at a catalyst loading as low as 0.0034%. The biocatalytic trifluoromethylation proceeded under mild conditions and could increase the yield by up to 12 fold, compared to the control.

Further Characterization of Fungal Halogenase RadH and Its Homologs.

RadH is one of the flavin-dependent halogenases that has previously exhibited promising catalytic activity towards hydroxycoumarin, hydroxyisoquinoline, and phenolic derivatives. Here, we evaluated new functional homologs of RadH and expanded its specificities for the halogenation of non-tryptophan-derived, heterocyclic scaffolds. Our investigation revealed that RadH could effectively halogenate hydroxyquinoline and hydroxybenzothiophene. Assay optimization studies revealed the need to balance the various co-factor concentrations and where a GDHi co-factor recycling system most significantly improves the conversion and efficiency of the reaction. A crystal structure of RadH was also obtained with a resolution of 2.4 Å, and docking studies were conducted to pinpoint the binding and catalytic sites for substrates.

Classification of mammographic breast density and its correlation with BI-RADS in elder women using machine learning approach.

INTRODUCTION: Mammographic breast density (MBD) is a known risk factor for breast cancer and older women have higher incidence rates of breast cancer occurrence. The Breast Imaging Reporting and Data System (BI-RADS) is a commonly used MBD classification tool for mammogram reporting. However, they have limitations since there are reading inconsistencies between different radiologists with the visual assessment of breast density. METHODS: Digitised film-screen mammographic images were extracted from the Digital Database for Screening Mammography (DDSM). A machine learning project was developed using commercially available software with several predictive models applied to classify different amount of MBD on mammograms into different density groups. The effectiveness of different predictive models used in classifying the mammograms were tested by receiver operator characteristics (ROC) curve with comparison to the gold standard of BI-RADS classification. RESULTS: Three predictive models, Decision Tree (Tree), Support Vector Model (SVM) and k-Nearest Neighbour (kNN) showed high AUC values of 0.801, 0.805 and 0.810 respectively. High AUC values for the three predictive models indicates that the accuracy of the model is approaching that of the BI-RADS method. DISCUSSION: Our machine learning project showed to have capabilities to be potentially used in the clinical settings to help categorise mammograms into extremely dense breasts (BI-RADS Group A) from entirely fatty breasts (BI-RADS Group D). CONCLUSION: Findings from the present study suggest that the machine learning method is potentially useful to quantify the amount of MBD in mammograms.

Spatial Distribution and Quantification of Mammographic Breast Density, and Its Correlation with BI-RADS Using an Image Segmentation Method.

(1) Background: Mammographic breast density (MBD) and older age are classical breast cancer risk factors. Normally, MBDs are not evenly distributed in the breast, with different women having different spatial distribution and clustering patterns. The presence of MBDs makes tumors and other lesions challenging to be identified in mammograms. The objectives of this study were: (i) to quantify the amount of MBDs-in the whole (overall), different sub-regions, and different zones of the breast using an image segmentation method; (ii) to investigate the spatial distribution patterns of MBD in different sub-regions of the breast. (2) Methods: The image segmentation method was used to quantify the overall amount of MBDs in the whole breast (overall percentage density (PD)), in 48 sub-regions (regional PDs), and three different zones (zonal PDs) of the whole breast, and the results of the amount of MBDs in 48 sub-regional PDs were further analyzed to determine its spatial distribution pattern in the breast using Moran's I values (spatial autocorrelation). (3) Results: The overall PD showed a negative correlation with age (p = 0.008); the younger women tended to have denser breasts (higher overall PD in breasts). We also found a higher proportion (p < 0.001) of positive autocorrelation pattern in the less dense breast group than in the denser breast group, suggesting that MBDs in the less dense breasts tend to be clustered together. Moreover, we also observed that MBDs in the mature women (<65 years old) tended to be clustered in the middle zone, while in older women (>64 years old) they tended to be clustered in both the posterior and middle zones. (4) Conclusions: There is an inverse relationship between the amount of MBD (overall PD in the breast) and age, and a different clustering pattern of MBDs between the older and mature women.

Toward Resolving the Resveratrol Conundrum: Synthesis and in Vivo Pharmacokinetic Evaluation of BCP-Resveratrol.

Over the last few decades, resveratrol has gained significance due to its impressive array of biological activities; however, its true potential as a drug has been severely constrained by its poor bioavailability. Indeed, several studies have implicated this bioavailability trait as a major road-block to resveratrol's potential clinical applications. To mitigate this pharmacokinetic issue, we envisioned a tactical bioisosteric modification of resveratrol to bicyclo[1.1.1]pentane (BCP) resveratrol. Relying on the beneficial bioisosteric potential demonstrated by the BCP-scaffold, we hypothesized that BCP-resveratrol would have an inherently better in vivo PK profile as compared to its natural counterpart. To validate such a hypothesis, it was necessary to secure a synthetic access to this novel structure. Herein we describe the first synthesis of BCP-resveratrol and disclose its PK properties.

Radical fluorination powered expedient synthesis of 3-fluorobicyclo[1.1.1]pentan-1-amine.

Exploration of novel chemical space, a modern trend in medicinal chemistry, is heavily reliant on synthetic access to new and interesting building blocks. In this direction, the following work describes an expedient synthesis of one such moiety, 3-fluorobicyclo[1.1.1]pentan-1-amine, by employing radical fluorination.

3-Deazaneplanocin A and neplanocin A analogues and their effects on apoptotic cell death.

3-Deazaneplanocin A (DzNep) is a potential epigenetic drug for the treatment of various cancers. DzNep has been reported to deplete histone methylations, including oncogenic EZH2 complex, giving rise to epigenetic modifications that reactivate many silenced tumor suppressors in cancer cells. Despite its promise as an anticancer drug, little is known about the structure-activity relationships of DzNep in the context of epigenetic modifications and apoptosis induction. In this study, a number of analogues of DzNep were examined for DzNep-like ability to induce synergistic apoptosis in cancer cells in combination with trichostatin A, a known histone deacetylase (HDAC) inhibitor. The structure-activity relationship data thus obtained provide valuable information on the structural requirements for biological activity. The studies identified three compounds that show similar activities to DzNep. Two of these compounds show good pharmacokinetics and safety profiles. Attempts to correlate the observed synergistic apoptotic activities with measured S-adenosylhomocysteine hydrolase (SAHH) inhibitory activities suggest that the apoptotic activity of DzNep might not be directly due to its inhibition of SAHH.

A new route to bicyclo[1.1.1]pentan-1-amine from 1-azido-3-iodobicyclo[1.1.1]pentane.

From a medicinal chemistry perspective, bicyclo[1.1.1]pentan-1-amine (1) has served as a unique and important moiety. Synthetically, however, this compound has received little attention, and only one scalable route to this amine has been demonstrated. Reduction of an easily available and potentially versatile intermediate, 1-azido-3-iodobicyclo[1.1.1]pentane (2), can offer both a flexible and scalable alternative to this target. Herein, we describe our scrutiny of this reportedly elusive transformation and report our ensuing success with this endeavor.

Cell-based proteome profiling using an affinity-based probe (AfBP) derived from 3-deazaneplanocin A (DzNep).

3-Deazaneplanocin A (DzNep), a global histone methylation inhibitor, has attracted significant interest in epigenetic research in recent years. The molecular mechanism of action and the cellular off-targets of DzNep, however, are still not well-understood. Our aim was to develop novel DzNep-derived small-molecule probes suitable to be used in live mammalian cells for identification of potential cellular targets of DzNep under physiologically relevant settings. In the current study, we have successfully designed, synthesized, and tested one such probe, called DZ-1. DZ-1 is a 'clickable' affinity-based probe (AfBP) derived from DzNep with minimal structural modifications. The probe was found to be highly cell-permeable, and possessed similar anti-apoptotic activities as DzNep in MCF-7 mammalian cells. Two additional control probes were made as negative labeling/pull-down probes in order to minimize false identification of background proteins due to unavoidable, intrinsic nonspecific photo-crosslinking reactions. All three probes were subsequently used for in-situ proteome profiling in live mammalian cells, followed by large-scale pull-down/LC-MS/MS analysis for identification of potential cellular protein targets that might interact with DzNep in native cellular environments. Our LC-MS/MS results revealed some highly enriched proteins that had not been reported as potential DzNep targets. These proteins might constitute unknown cellular off-targets of DzNep. Though further validation experiments are needed in order to unequivocally confirm these off-targets, our findings shed new light on the future use of DzNep as a validated chemical probe for epigenetic research and as a potential drug candidate for cancer therapy.