651,818 research outputs found

    Cheminformatics Models for Inhibitors of Schistosoma mansoni

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    Schistosomiasis is a neglected tropical disease caused by a parasite Schistosoma mansoni and affects over 200 million annually. There is an urgent need to discover novel therapeutic options to control the disease with the recent emergence of drug resistance. The multifunctional protein, thioredoxin glutathione reductase (TGR), an essential enzyme for the survival of the pathogen in the redox environment has been actively explored as a potential drug target. The recent availability of small-molecule screening datasets against this target provides a unique opportunity to learn molecular properties and apply computational models for discovery of activities in large molecular libraries. Such a prioritisation approach could have the potential to reduce the cost of failures in lead discovery. A supervised learning approach was employed to develop a cost sensitive classification model to evaluate the biological activity of the molecules. Random forest was identified to be the best classifier among all the classifiers with an accuracy of around 80 percent. Independent analysis using a maximally occurring substructure analysis revealed 10 highly enriched scaffolds in the actives dataset and their docking against was also performed. We show that a combined approach of machine learning and other cheminformatics approaches such as substructure comparison and molecular docking is efficient to prioritise molecules from large molecular datasets

    Streptococcal dTDP-L-rhamnose biosynthesis enzymes:functional characterization and lead compound identification

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    Biosynthesis of the nucleotide sugar precursor dTDP-L-rhamnose is critical for the viability and virulence of many human pathogenic bacteria, including Streptococcus pyogenes (Group A Streptococcus; GAS), Streptococcus mutans and Mycobacterium tuberculosis. Streptococcal pathogens require dTDP-L-rhamnose for the production of structurally similar rhamnose polysaccharides in their cell wall. Via heterologous expression in S. mutans, we confirmed that GAS RmlB and RmlC are critical for dTDP-L-rhamnose biosynthesis through their action as dTDP-glucose-4,6-dehydratase and dTDP-4-keto-6-deoxyglucose-3,5-epimerase enzymes respectively. Complementation with GAS RmlB and RmlC containing specific point mutations corroborated the conservation of previous identified catalytic residues. Bio-layer interferometry was used to identify and confirm inhibitory lead compounds that bind to GAS dTDP-rhamnose biosynthesis enzymes RmlB, RmlC and GacA. One of the identified compounds, Ri03, inhibited growth of GAS, other rhamnose-dependent streptococcal pathogens as well as M. tuberculosis with an IC 50 of 120–410 µM. Importantly, we confirmed that Ri03 inhibited dTDP-L-rhamnose formation in a concentration-dependent manner through a biochemical assay with recombinant rhamnose biosynthesis enzymes. We therefore conclude that inhibitors of dTDP-L-rhamnose biosynthesis, such as Ri03, affect streptococcal and mycobacterial viability and can serve as lead compounds for the development of a new class of antibiotics that targets dTDP-rhamnose biosynthesis in pathogenic bacteria

    Using transcriptomics to guide lead optimization in drug discovery projects : lessons learned from the QSTAR project

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    The pharmaceutical industry is faced with steadily declining R&D efficiency which results in fewer drugs reaching the market despite increased investment. A major cause for this low efficiency is the failure of drug candidates in late-stage development owing to safety issues or previously undiscovered side-effects. We analyzed to what extent gene expression data can help to de-risk drug development in early phases by detecting the biological effects of compounds across disease areas, targets and scaffolds. For eight drug discovery projects within a global pharmaceutical company, gene expression data were informative and able to support go/no-go decisions. Our studies show that gene expression profiling can detect adverse effects of compounds, and is a valuable tool in early-stage drug discovery decision making

    Protein Functional Families to characterise drug-target interactions.

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    The quest for “magic bullets” has been the driving force in drug discovery during the last two decades. However, the increasing rate of drug failure over this period has occurred concurrently with the assumption that a drug is a selective ligand for a single target. It now seems likely that polypharmacology is the rule rather than the exception [1]. Our previous research shows that protein domains are a good proxy for drug targets, and that drug polypharmacology emerges as a consequence of the multi-domain composition of proteins [2]. In this study, we investigate further the idea that the domain is the druggable entity within a protein target. We have identified a specific class of domains (CATH Functional Families) as the best currently available for identifying drug-target interactions. We show how this opens a new direction in target identification with potential application in drug repurposing.1. Hopkins, AL. (2008) Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol; 4: 682 2. Moya-García AA & Ranea JAG (2013) Insights into polypharmacology from drug-domain associations. Bioinformatics 29: 1934–1937)Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Universidad de Granad

    Whole Genome Sequencing Variant Call Files for Trypanosoma brucei clones resistant to nifurtimox or fexinidazole

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    Genomic DNA prepared from parental and drug resistant clones from T.brucei was sequenced on an Illumina HiSeq 2000 sequencer. The Illumina data were aligned against the T.brucei TREU927 reference genome and variants were called using SAMtools v0.1.19 and BCF tools. The files are in variant call format listing all 190064 genomic positions at which any one of the six drug-resistant and the parental parasite lines had a variant call

    Fragments and hot spots in drug discovery

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    R01 GM064700 - NIGMS NIH HHSPublished versio
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