Synthesis and exploration of novel curcumin analogues as anti-malarial agents

Curcumin, a major yellow pigment and active component of turmeric, has been shown to possess anti-inflammatory and anti-cancer activities. Recent studies have indicated that curcumin inhibits chloroquine-sensitive (CQ-S) and chloroquine-resistant (CQ-R) Plasmodium falciparum growth in culture with an IC50 of not, vert, similar3.25 μM (MIC = 13.2 μM) and IC50 4.21 μM (MIC = 14.4 μM), respectively. In order to expand their potential as anti-malarials a series of novel curcumin derivatives were synthesized and evaluated for their ability to inhibit P. falciparum growth in culture. Several curcumin analogues examined show more effective inhibition of P. falciparumgrowth than curcumin. The most potent curcumin compounds 3, 6, and 11 were inhibitory for CQ-S P. falciparum at IC50 of 0.48, 0.87, 0.92 μM and CQ-R P. falciparum at IC50 of 0.45 μM, 0.89, 0.75 μM, respectively. Pyrazole analogue of curcumin (3) exhibited sevenfold higher anti-malarial potency against CQ-S and ninefold higher anti-malarial potency against CQ-R. Curcumin analogues described here represent a novel class of highly selective P. falciparum inhibitors and promising candidates for the design of novel anti-malarial agents.

Synthesis and exploration of novel curcumin analogues as anti-malarial agents

Curcumin, a major yellow pigment and active component of turmeric, has been shown to possess anti-inflammatory and anti-cancer activities. Recent studies have indicated that curcumin inhibits chloroquine-sensitive (CQ-S) and chloroquine-resistant (CQ-R) Plasmodium falciparum growth in culture with an IC50 of similar to 3.25 mu M (MIC = 13.2 mu M) and IC50 4.21 mu M (MIC = 14.4 mu M), respectively. In order to expand their potential as anti-malarials a series of novel curcumin derivatives were synthesized and evaluated for their ability to inhibit P. falciparum growth in culture. Several curcumin analogues examined show more effective inhibition of P. falciparum growth than curcumin. The most potent curcumin compounds 3, 6, and 11 were inhibitory for CQ-S P. falciparum at IC50 of 0.48, 0.87, 0.92 mu M and CQ-R P. falcipartan at IC50 of 0.45 mu M, 0.89, 0.75 mu M, respectively. Pyrazole analogue of curcumin (3) exhibited sevenfold higher anti-malarial potency against CQ-S and ninefold higher anti-malarial potency against CQ-R. Curcumin analogues described here represent a novel class of highly selective P. falcipartan inhibitors and promising candidates for the design of novel anti-malarial agents. (C) 2007 Elsevier Ltd. All rights reserved.

Construction of Bis-pyrazole Based Co(II) Metal-Organic Frameworks and Exploration of Their Chirality and Magnetic Properties

In continuation of our interest in pyrazole based multifunctional metal-organic frameworks (MOFs), we report herein the construction of a series of Co(II) MOFs using a bis-pyrazole ligand and various benzene polycarboxylic acids. Employment of different acids has resulted in different architectures ranging from a two-dimensional grid network, porous nanochannels with interesting double helical features such as supramolecular chicken wire, to three-dimensional diamondoid networks. One of the distinguishing features of the network is their larger dimensions which can be directly linked to a relatively larger size of the ligand molecule. Conformational flexibility of the ligand also plays a decisive role in determining both the dimensionality and topology of the final structure. Furthermore, chirality associated with helical networks and magnetic properties of two MOFs have also been investigated.

Mining Unit Tests for Discovery and Migration of Math APIs

Today's programming languages are supported by powerful third-party APIs. For a given application domain, it is common to have many competing APIs that provide similar functionality. Programmer productivity therefore depends heavily on the programmer's ability to discover suitable APIs both during an initial coding phase, as well as during software maintenance. The aim of this work is to support the discovery and migration of math APIs. Math APIs are at the heart of many application domains ranging from machine learning to scientific computations. Our approach, called MATHFINDER, combines executable specifications of mathematical computations with unit tests (operational specifications) of API methods. Given a math expression, MATHFINDER synthesizes pseudo-code comprised of API methods to compute the expression by mining unit tests of the API methods. We present a sequential version of our unit test mining algorithm and also design a more scalable data-parallel version. We perform extensive evaluation of MATHFINDER (1) for API discovery, where math algorithms are to be implemented from scratch and (2) for API migration, where client programs utilizing a math API are to be migrated to another API. We evaluated the precision and recall of MATHFINDER on a diverse collection of math expressions, culled from algorithms used in a wide range of application areas such as control systems and structural dynamics. In a user study to evaluate the productivity gains obtained by using MATHFINDER for API discovery, the programmers who used MATHFINDER finished their programming tasks twice as fast as their counterparts who used the usual techniques like web and code search, IDE code completion, and manual inspection of library documentation. For the problem of API migration, as a case study, we used MATHFINDER to migrate Weka, a popular machine learning library. Overall, our evaluation shows that MATHFINDER is easy to use, provides highly precise results across several math APIs and application domains even with a small number of unit tests per method, and scales to large collections of unit tests.

Computational systems approach for drug target discovery

Importance of the field: The shift in focus from ligand based design approaches to target based discovery over the last two to three decades has been a major milestone in drug discovery research. Currently, it is witnessing another major paradigm shift by leaning towards the holistic systems based approaches rather the reductionist single molecule based methods. The effect of this new trend is likely to be felt strongly in terms of new strategies for therapeutic intervention, new targets individually and in combinations, and design of specific and safer drugs. Computational modeling and simulation form important constituents of new-age biology because they are essential to comprehend the large-scale data generated by high-throughput experiments and to generate hypotheses, which are typically iterated with experimental validation. Areas covered in this review: This review focuses on the repertoire of systems-level computational approaches currently available for target identification. The review starts with a discussion on levels of abstraction of biological systems and describes different modeling methodologies that are available for this purpose. The review then focuses on how such modeling and simulations can be applied for drug target discovery. Finally, it discusses methods for studying other important issues such as understanding targetability, identifying target combinations and predicting drug resistance, and considering them during the target identification stage itself. What the reader will gain: The reader will get an account of the various approaches for target discovery and the need for systems approaches, followed by an overview of the different modeling and simulation approaches that have been developed. An idea of the promise and limitations of the various approaches and perspectives for future development will also be obtained. Take home message: Systems thinking has now come of age enabling a `bird's eye view' of the biological systems under study, at the same time allowing us to `zoom in', where necessary, for a detailed description of individual components. A number of different methods available for computational modeling and simulation of biological systems can be used effectively for drug target discovery.

Lectin Microarrays for Glycomic Analysis

Glycomics is the study of comprehensive structural elucidation and characterization of all glycoforms found in nature and their dynamic spatiotemporal changes that are associated with biological processes. Glycocalyx of mammalian cells actively participate in cell-cell, cell-matrix, and cell-pathogen interactions, which impact embryogenesis, growth and development, homeostasis, infection and immunity, signaling, malignancy, and metabolic disorders. Relative to genomics and proteomics, glycomics is just growing out of infancy with great potential in biomedicine for biomarker discovery, diagnosis, and treatment. However, the immense diversity and complexity of glycan structures and their multiple modes of interactions with proteins pose great challenges for development of analytical tools for delineating structure function relationships and understanding glycocode. Several tools are being developed for glycan profiling based on chromatography,m mass spectrometry, glycan microarrays, and glyco-informatics. Lectins, which have long been used in glyco-immunology, printed on a microarray provide a versatile platform for rapid high throughput analysis of glycoforms of biological samples. Herein, we summarize technological advances in lectin microarrays and critically review their impact on glycomics analysis. Challenges remain in terms of expansion to include nonplant derived lectins, standardization for routine clinical use, development of recombinant lectins, and exploration of plant kingdom for discovery of novel lectins.

Network approaches to drug discovery

Introduction: Advances in genomics technologies are providing a very large amount of data on genome-wide gene expression profiles, protein molecules and their interactions with other macromolecules and metabolites. Molecular interaction networks provide a useful way to capture this complex data and comprehend it. Networks are beginning to be used in drug discovery, in many steps of the modern discovery pipeline, with large-scale molecular networks being particularly useful for the understanding of the molecular basis of the disease. Areas covered: The authors discuss network approaches used for drug target discovery and lead identification in the drug discovery pipeline. By reconstructing networks of targets, drugs and drug candidates as well as gene expression profiles under normal and disease conditions, the paper illustrates how it is possible to find relationships between different diseases, find biomarkers, explore drug repurposing and study emergence of drug resistance. Furthermore, the authors also look at networks which address particular important aspects such as off-target effects, combination-targets, mechanism of drug action and drug safety. Expert opinion: The network approach represents another paradigm shift in drug discovery science. A network approach provides a fresh perspective of understanding important proteins in the context of their cellular environments, providing a rational basis for deriving useful strategies in drug design. Besides drug target identification and inferring mechanism of action, networks enable us to address new ideas that could prove to be extremely useful for new drug discovery, such as drug repositioning, drug synergy, polypharmacology and personalized medicine.

A game theoretic approach for feature clustering and its application to feature selection

In this paper, we develop a game theoretic approach for clustering features in a learning problem. Feature clustering can serve as an important preprocessing step in many problems such as feature selection, dimensionality reduction, etc. In this approach, we view features as rational players of a coalitional game where they form coalitions (or clusters) among themselves in order to maximize their individual payoffs. We show how Nash Stable Partition (NSP), a well known concept in the coalitional game theory, provides a natural way of clustering features. Through this approach, one can obtain some desirable properties of the clusters by choosing appropriate payoff functions. For a small number of features, the NSP based clustering can be found by solving an integer linear program (ILP). However, for large number of features, the ILP based approach does not scale well and hence we propose a hierarchical approach. Interestingly, a key result that we prove on the equivalence between a k-size NSP of a coalitional game and minimum k-cut of an appropriately constructed graph comes in handy for large scale problems. In this paper, we use feature selection problem (in a classification setting) as a running example to illustrate our approach. We conduct experiments to illustrate the efficacy of our approach.

Biodegradation of sodium isopropyl xanthate by Paenibacillus polymyxa and Pseudomonas putida

Biodegradation of sodium isopropyl xanthate using two types of bacterial strains, Paenibacillus polymyxa and Pseudomonas putida, is demonstrated. At concentrations higher than 50 mg/L, the presence of xanthate in the growth medium resulted in bacterial toxicity, retarding growth kinetics. Adaptation through serial subculturing in the presence of higher xanthate concentrations resulted in the development of xanthate-tolerant bacterial strains. Stress proteins secreted by bacterial cells grown in the presence of xanthate were isolated. Bacterial cells could utilize xanthate as a growth substrate, degrading xanthate species in the process. Acidic metabolic products generated by bacterial metabolism promoted efficient xanthate decomposition. Probable mechanisms for the biodegradation of isopropyl xanthate are illustrated.

Synthesis, DNA binding, docking and photocleavage studies of quinolinyl chalcones

A series of simple quinoline-chalcone conjugates have been synthesized by Claisen-Schmidt condensation reactions of substituted acetophenones with 2-chloro-3-formyl-quinoline and evaluated for their nucleolytic activity. The structures of the synthesized quinoline-chalcone conjugates were confirmed by IR, H-1 NMR, C-13 NMR and mass spectral analyses. Most of the prepared compounds showed significant DNA binding and photocleavage activities. The incorporation of an electron-donating group into ring A caused a moderate increase in the DNA binding and photocleavage activities. Compounds 3c and 3d exhibited promising DNA photocleavage against pUC 19 DNA with 85% inhibition at 100 mu M concentration. A structure-activity relationship analysis of these compounds was performed; compounds 3c and 3d are potential candidates for future drug discovery and development.

Sulfate Chemistry for High-Voltage Insertion Materials: Synthetic, Structural and Electrochemical Insights

Rechargeable batteries based on Li and Na ions have been growing leaps and bounds since their inception in the 1970s. They enjoy significant attention from both the fundamental science point of view and practical applications ranging from portable electronics to hybrid vehicles and grid storage. The steady demand for building better batteries calls for discovery, optimisation and implementation of novel positive insertion (cathode) materials. In this quest, chemists have tried to unravel many future cathode materials by taking into consideration their eco-friendly synthesis, material/process economy, high energy density, safety, easy handling and sustainability. Interestingly, sulfate-based cathodes offer a good combination of sustainable syntheses and high energy density owing to their high-voltage operation, stemming from electronegative SO42- units. This review delivers a sneak peak at the recent advances in the discovery and development of sulfate-containing cathode materials by focusing on their synthesis, crystal structure and electrochemical performance. Several family of cathodes are independently discussed. They are 1) fluorosulfates AMSO(4)F], 2) bihydrated fluorosulfates AMSO(4)F2H(2)O], 3) hydroxysulfate AMSO(4)OH], 4) bisulfates A(2)M(SO4)(2)], 5) hydrated bisulfates A(2)M(SO4)(2)nH(2)O], 6) oxysulfates Fe-2(SO4)(2)O] and 7) polysulfates A(2)M(2)(SO4)(3)]. A comparative study of these sulfate-based cathodes has been provided to offer an outlook on the future development of high-voltage polyanionic cathode materials for next-generation batteries.

Recognizing drug targets using evolutionary information: implications for repurposing FDA-approved drugs against Mycobacterium tuberculosis H37Rv

Drug repurposing to explore target space has been gaining pace over the past decade with the upsurge in the use of systematic approaches for computational drug discovery. Such a cost and time-saving approach gains immense importance for pathogens of special interest, such as Mycobacterium tuberculosis H37Rv. We report a comprehensive approach to repurpose drugs, based on the exploration of evolutionary relationships inferred from the comparative sequence and structural analyses between targets of FDA-approved drugs and the proteins of M. tuberculosis. This approach has facilitated the identification of several polypharmacological drugs that could potentially target unexploited M. tuberculosis proteins. A total of 130 FDA-approved drugs, originally intended against other diseases, could be repurposed against 78 potential targets in M. tuberculosis. Additionally, we have also made an attempt to augment the chemical space by recognizing compounds structurally similar to FDA-approved drugs. For three of the attractive cases we have investigated the probable binding modes of the drugs in their corresponding M. tuberculosis targets by means of structural modelling. Such prospective targets and small molecules could be prioritized for experimental endeavours, and could significantly influence drug-discovery and drug-development programmes for tuberculosis.

Phytomedicinal importance of Saraca asoca (Ashoka): an exciting past, an emerging present and a promising future

Since the dawn of civilization, natural resources have remained the mainstay of various remedial approaches of humans vis-a-vis a large number of illnesses. Saraca asoca (Roxb.) de Wilde (Saraca indica L.) belonging to the family Caesalpiniaceae has been regarded as a universal panacea in old Indian Ayurvedic texts and has especially been used to manage gynaecological complications and infections besides treating haemmorhagic dysentery, uterine pain, bacterial infections, skin problems, tumours, worm infestations, cardiac and circulatory problems. Almost all parts of the plant are considered pharmacologically valuable. Extensive folkloric practices and ethnobotanical applications of this plant have even lead to the availability of several commercial S. asoca formulations recommended for different indications though adulteration of these remains a pressing concern. Though a wealth of knowledge on this plant is available in both the classical and modern literature, extensive research on its phytomedicinal worth using state-of-the-art tools and methodologies is lacking. Recent reports on bioprospecting of S. asoca endophytic fungi for industrial bioproducts and useful pharmacologically relevant metabolites provide a silver lining to uncover single molecular bio-effectors from its endophytes. Here, we describe socio-ethnobotanical usage, present the current pharmacological status and discuss potential bottlenecks in harnessing the proclaimed phytomedicinal worth of this prescribed Ayurvedic medicinal plant. Finally, we also look into the possible future of the drug discovery and pharmaceutical R&D efforts directed at exploring its pharma legacy.

Membrane modifying linear polypeptide antibiotics

Peptides Possessing antibiotic activity isolated from microbial sources have been the subject of intensive structural and biological investigation over the past two decades. Perhaps, the discovery and widespread use of penicillin, a molecule biosynthetically derived from a tripeptide precursor, as a strong antibacterial agent, has provided the necessary impetus for the detailed study of microbial peptides. While many of these peptides have not been used clinically, They show unique metal binding properties and often possess the ability to modify the electrical properties or ion permeabilities of artificial lipid membranes. Hence, these peptides have been used extensively to study transmembrane ion transport processes in model and natural systems like mitochondria, chloroplasts and plasma membranes.

Single top quark events at Fermilab

According to a press release dated 9 March 2009, the two experiments CDF (Collider Detector at Fermilab) and DZero have announced the discovery of ‘single top quark’ events, which represent a spectacular discovery and confirmation of the standard model of elementary particle physics. The results of their findings are now available as preprints which have been submitted for publication in Physical Review Letters1,2.