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The second edition of this popular title presents the most exciting approaches for engineering biologically active nanostructures. A particular stress is placed on purely artificial assemblies created to deliver a specialist biological function or mechanism. Such synthetic designs emulate naturally occurring nanoscale forms of viruses and matrices, pores and channels, but their functional properties are not necessarily those normally observed in Nature. Therefore, it is said that the forms may be same, but functions they adopt are new. The volume is structured around prominent topics of biological nanodesign with an integrated perspective of the impact nanoscale engineering has on the emerging field of synthetic biology. Nanostructured materials assembled from DNA, proteins and lipids and advances they offer for medicine are discussed. Written by a world recognised expert, this book provides an authorative guide to those working in design and development of nanomaterial research in industry and academia, from postgraduate researchers upwards.

Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and compound libraries have grown, the ability to screen hundreds of millions, and even billions, of compounds has become feasible for modest-sized computer clusters. This allows the rapid and cost-effective exploration and categorization of vast chemical space into a subset enriched with potential hits for a given target. To accomplish this goal at speed, approximations are used that result in undersampling of possible configurations and inaccurate predictions of absolute binding energies. Accordingly, it is important to establish controls, as are common in other fields, to enhance the likelihood of success in spite of these challenges. Here we outline best practices and control docking calculations that help evaluate docking parameters for a given target prior to undertaking a large-scale prospective screen, with exemplification in one particular target, the melatonin receptor, where following this procedure led to direct docking hits with activities in the subnanomolar range. Additional controls are suggested to ensure specific activity for experimentally validated hit compounds. These guidelines should be useful regardless of the docking software used. Docking software described in the outlined protocol (DOCK3.7) is made freely available for academic research to explore new hits for a range of targets. Structure-based docking screens of compound libraries are common in early drug and probe discovery. This protocol outlines best practices and control calculations to evaluate docking parameters prior to undertaking a large-scale prospective screen.

Histone H3 lysine 14 is propionylated and butyrylated in vivo in a metabolic-state-dependent manner and these modifications promote high levels of transcription. Histones are highly covalently modified, but the functions of many of these modifications remain unknown. In particular, it is unclear how histone marks are coupled to cellular metabolism and how this coupling affects chromatin architecture. We identified histone H3 Lys14 (H3K14) as a site of propionylation and butyrylation in vivo and carried out the first systematic characterization of histone propionylation. We found that H3K14pr and H3K14bu are deposited by histone acetyltransferases, are preferentially enriched at promoters of active genes and are recognized by acylation-state-specific reader proteins. In agreement with these findings, propionyl-CoA was able to stimulate transcription in an in vitro transcription system. Notably, genome-wide H3 acylation profiles were redefined following changes to the metabolic state, and deletion of the metabolic enzyme propionyl-CoA carboxylase altered global histone propionylation levels. We propose that histone propionylation, acetylation and butyrylation may act in combination to promote high transcriptional output and to couple cellular metabolism with chromatin structure and function.

A key resource for toxicologists across a broad spectrum of fields, this book offers a comprehensive analysis of molecular modelling approaches and strategies applied to risk assessment for pharmaceutical and environmental chemicals. * Provides a perspective of what is currently achievable with computational toxicology and a view to future developments * Helps readers overcome questions of data sources, curation, treatment, and how to model / interpret critical endpoints that support 21st century hazard assessment * Assembles cutting-edge concepts and leading authors into a unique and powerful single-source reference * Includes in-depth looks at QSAR models, physicochemical drug properties, structure-based drug targeting, chemical mixture assessments, and environmental modeling * Features coverage about consumer product safety assessment and chemical defense along with chapters on open source toxicology and big data

The NLRP3 inflammasome, which has been linked to human inflammatory diseases, is activated by diverse stimuli. How these stimuli activate NLRP3 is unknown. Here we show that different NLRP3 stimuli lead to disassembly of the trans -Golgi network (TGN). NLRP3 is recruited to the dispersed TGN (dTGN) through ionic bonding between its conserved polybasic region and negatively charged phosphatidylinositol-4-phosphate (PtdIns4P) on the dTGN. The dTGN then serves as a scaffold for NLRP3 aggregation into multiple puncta, leading to polymerization of the adaptor protein ASC, thereby activating the downstream signalling cascade. Disruption of the interaction between NLRP3 and PtdIns4P on the dTGN blocked NLRP3 aggregation and downstream signalling. These results indicate that recruitment of NLRP3 to dTGN is an early and common cellular event that leads to NLRP3 aggregation and activation in response to diverse stimuli. Recruitment of NLRP3 to the dispersed trans -Golgi network via binding to PtdIns4P is required for activation of the NLRP3 inflammasome by diverse stimuli.

The Freundlich isotherm parameters K and 1/n are typically regarded as empirical constants. However, the underlying theoretical basis for the widespread applicability of the Freundlich isotherm in describing adsorption processes for diverse organic compounds remains unclear. In this study, we successfully elucidated the reason by developing two optimal quantitative structure-activity relationship (QSAR) models: one correlating K with quantum chemical parameters and another linking 1/n to these parameters. The modeling results demonstrated that both K and 1/n exhibit strong correlations with specific quantum chemical descriptors, indicating that the empirical Freundlich isotherm's applicability is fundamentally linked to the molecular structural characteristics of organic compounds. Key quantum parameters influencing K were identified as [summation]q(O + N), q(CH+).sub.max, ELUMO, Fukui(-).sub.max, and Wiberg(C-C).sub.min, suggesting that charge distribution, carbon bond energy, and active site energy are the primary factors governing adsorption efficiency on activated carbon. The QSAR model for 1/n yielded similarly novel and consistent insights, showing that the value of 1/n also correlated with molecular structural characteristics. Both models were rigorously validated and confirmed to be stable, robust, and accurate through standard statistical evaluations. These QSAR models can now be employed to identify whether an organic compound would conform to the Freundlich Isotherm and predict the adsorption efficiency of this compound by activated carbon based on their quantum chemical parameters. As to the practical implications, this study provides a convenient reference method for assessing the applicability of activated carbon adsorption in treating emerging organic pollutants in drinking water plants and a theoretical foundation for developing intelligent management systems in water treatment facilities.

In this study, hybrid analogs of benzimidazole containing a thiazole moiety (1–17) were afforded and then tested for their ability to inhibit α-amylase and α-glucosidase when compared to acarbose as a standard drug. The recently available analogs showed a wide variety of inhibitory potentials that ranged between 1.31 ± 0.05 and 38.60 ± 0.70 µM (against α-amylase) and between 2.71 ± 0.10 and 42.31 ± 0.70 µM (against α-glucosidase) under the positive control of acarbose (IC50 = 10.30 ± 0.20 µM against α-amylase) (IC50 = 9.80 ± 0.20 µM against α-glucosidase). A structure–activity relationship (SAR) study was carried out for all analogs based on substitution patterns around both rings B and C respectively. It was concluded from the SAR study that analogs bearing either substituent(s) of smaller size (−F and Cl) or substituent(s) capable of forming hydrogen bonding (−OH) with the catalytic residues of targeted enzymes enhanced the inhibitory potentials. Therefore, analogs 2 (bearing meta-fluoro substitution), 3 (having para-fluoro substitution) and 4 (with ortho-fluoro group) showed enhanced potency when evaluated against standard acarbose drug with IC50 values of 4.10 ± 0.10, 1.30 ± 0.05 and 1.90 ± 0.10 (against α-amylase) and 5.60 ± 0.10, 2.70 ± 0.10 and 2.90 ± 0.10 µM (against α-glucosidase), correspondingly. On the other hand, analogs bearing substituent(s) of either a bulky nature (−Br) or that are incapable of forming hydrogen bonds (−CH3) were found to lower the inhibitory potentials. In order to investigate the binding sites for synthetic analogs and how they interact with the active areas of both targeted enzymes, molecular docking studies were also conducted on the potent analogs. The results showed that these analogs adopted many important interactions with the active areas of enzymes. The precise structure of the newly synthesized compounds was confirmed using several spectroscopic techniques as NMR and HREI-MS.

The development of innovative antifungal agents is essential. Some fungicidal agents are no longer effective due to resistance development, various side effects, and high toxicity. Therefore, the synthesis and development of some new antifungal agents are necessary. 1,2,4-Triazole is one of the most essential pharmacophore systems between five-membered heterocycles. The structure-activity relationship (SAR) of this nitrogen-containing heterocyclic compound showed potential antifungal activity. The 1,2,4-triazole core is present as the nucleus in a variety of antifungal drug categories. The most potent and broad activity of triazoles have confirmed them as pharmacologically significant moieties. The goal of this review is to highlight recent developments in the synthesis and SAR study of 1,2,4-triazole as a potential fungicidal compound. In this study, we provide the results of a biological activity evaluation using various structures and figures. Literature investigation showed that 1, 2, 4-triazole derivatives reveal the extensive span of antifungal activity. This review will assist researchers in the development of new potential antifungal drug candidates with high effectiveness and selectivity.

Of the thousands of novel compounds that a drug discovery project team invents and that bind to the therapeutic target, typically only a fraction of these have sufficient ADME/Tox properties to become a drug product. Understanding ADME/Tox is critical for all drug researchers, owing to its increasing importance in advancing high quality candidates to clinical studies and the processes of drug discovery. If the properties are weak, the candidate will have a high risk of failure or be less desirable as a drug product. This book is a tool and resource for scientists engaged in, or preparing for, the selection and optimization process. The authors describe how properties affect in vivo pharmacological activity and impact in vitro assays. Individual drug-like properties are discussed from a practical point of view, such as solubility, permeability and metabolic stability, with regard to fundamental understanding, applications of property data in drug discovery and examples of structural modifications that have achieved improved property performance. The authors also review various methods for the screening (high throughput), diagnosis (medium throughput) and in-depth (low throughput) analysis of drug properties. * Serves as an essential working handbook aimed at scientists and students in medicinal chemistry* Provides practical, step-by-step guidance on property fundamentals, effects, structure-property relationships, and structure modification strategies * Discusses improvements in pharmacokinetics from a practical chemist's standpoint

The increase of publicly available bioactivity data in recent years has fueled and catalyzed research in chemogenomics, data mining, and modeling approaches. As a direct result, over the past few years a multitude of different methods have been reported and evaluated, such as target fishing, nearest neighbor similarity-based methods, and Quantitative Structure Activity Relationship (QSAR)-based protocols. However, such studies are typically conducted on different datasets, using different validation strategies, and different metrics. In this study, different methods were compared using one single standardized dataset obtained from ChEMBL, which is made available to the public, using standardized metrics (BEDROC and Matthews Correlation Coefficient). Specifically, the performance of Naïve Bayes, Random Forests, Support Vector Machines, Logistic Regression, and Deep Neural Networks was assessed using QSAR and proteochemometric (PCM) methods. All methods were validated using both a random split validation and a temporal validation, with the latter being a more realistic benchmark of expected prospective execution. Deep Neural Networks are the top performing classifiers, highlighting the added value of Deep Neural Networks over other more conventional methods. Moreover, the best method (‘DNN_PCM’) performed significantly better at almost one standard deviation higher than the mean performance. Furthermore, Multi-task and PCM implementations were shown to improve performance over single task Deep Neural Networks. Conversely, target prediction performed almost two standard deviations under the mean performance. Random Forests, Support Vector Machines, and Logistic Regression performed around mean performance. Finally, using an ensemble of DNNs, alongside additional tuning, enhanced the relative performance by another 27% (compared with unoptimized ‘DNN_PCM’). Here, a standardized set to test and evaluate different machine learning algorithms in the context of multi-task learning is offered by providing the data and the protocols. Graphical Abstract .