Current Issue : April - June Volume : 2019 Issue Number : 2 Articles : 5 Articles
The interaction between dehydroeburicoic acid (DeEA), a triterpene purified\nfrom medicinal fungi and the major transport protein, human serum albumin\n(HSA), were systematically studied by fluorescence spectroscopy, synchronous\nfluorescence spectroscopy, three-dimensional fluorescence spectroscopy\nand molecular docking approach under simulated physiological conditions.\nThe intrinsic fluorescence of HSA was quenched through the combination of\nstatic and dynamic quenching mechanism. DeEA cannot be stored and carried\nby HSA in the body at higher temperature. The hydrogen bonding, hydrophobic\nforce and van der Waals force were major acting forces. The site II\nwas the major binding site. The energy transfer could occur with high probability\nand the binding distance was 3.29 nm. The binding process slightly\nchanged the conformation and microenvironment of HSA. The DeEA molecule\nentered the hydrophobic cleft of HSA and formed the hydrogen bonding\nwith Glu-492 and Lys-545....
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Virtual screening (VS) has emerged in drug discovery as a powerful computational\napproach to screen large libraries of small molecules for new hits with desired properties\nthat can then be tested experimentally. Similar to other computational approaches, VS\nintention is not to replace in vitro or in vivo assays, but to speed up the discovery\nprocess, to reduce the number of candidates to be tested experimentally, and to\nrationalize their choice. Moreover, VS has become very popular in pharmaceutical\ncompanies and academic organizations due to its time-, cost-, resources-, and laborsaving.\nAmong the VS approaches, quantitative structureâ??activity relationship (QSAR)\nanalysis is the most powerful method due to its high and fast throughput and\ngood hit rate. As the first preliminary step of a QSAR model development, relevant\nchemogenomics data are collected from databases and the literature. Then, chemical\ndescriptors are calculated on different levels of representation of molecular structure,\nranging from 1D to nD, and then correlated with the biological property using machine\nlearning techniques. Once developed and validated, QSAR models are applied to\npredict the biological property of novel compounds. Although the experimental testing\nof computational hits is not an inherent part of QSAR methodology, it is highly desired\nand should be performed as an ultimate validation of developed models. In this minireview,\nwe summarize and critically analyze the recent trends of QSAR-based VS\nin drug discovery and demonstrate successful applications in identifying perspective\ncompounds with desired properties. Moreover, we provide some recommendations\nabout the best practices for QSAR-based VS along with the future perspectives of this\napproach....
Background: Motif analysis methods have long been central for studying biological function of nucleotide\nsequences. Functional genomics experiments extend their potential. They typically generate sequence lists ranked\nby an experimentally acquired functional property such as gene expression or protein binding affinity. Current motif\ndiscovery tools suffer from limitations in searching large motif spaces, and thus more complex motifs may not be\nincluded. There is thus a need for motif analysis methods that are tailored for analyzing specific complex motifs motivated\nby biological questions and hypotheses rather than acting as a screen based motif finding tool.\nMethods: We present Regmex (REGular expression Motif EXplorer), which offers several methods to identify overrepresented\nmotifs in ranked lists of sequences. Regmex uses regular expressions to define motifs or families of motifs\nand embedded Markov models to calculate exact p-values for motif observations in sequences. Biases in motif distributions\nacross ranked sequence lists are evaluated using random walks, Brownian bridges, or modified rank based\nstatistics. A modular setup and fast analytic p value evaluations make Regmex applicable to diverse and potentially\nlarge-scale motif analysis problems.\nResults: We demonstrate use cases of combined motifs on simulated data and on expression data from micro RNA\ntransfection experiments. We confirm previously obtained results and demonstrate the usability of Regmex to test a\nspecific hypothesis about the relative location of microRNA seed sites and U-rich motifs. We further compare the tool\nwith an existing motif discovery tool and show increased sensitivity.\nConclusions: Regmex is a useful and flexible tool to analyze motif hypotheses that relates to large data sets in functional\ngenomics. The method is available as an R package (https ://githu b.com/muhli gs/regme x)....
A series of thiosemicarbazide derivatives was designed and synthesized by reaction\nof carboxylic acid hydrazide with isothiocyanates. The molecular structures of the investigated\nthiosemicarbazides were confirmed and characterized by spectroscopic analysis. The conformational\npreference of carbonylthiosemicarbazide chain and intra- and intermolecular interactions in the\ncrystalline state were characterized using X-ray analysis. The antituberculosis activity of the target\ncompounds were tested in vitro against four Mycobacterium strains: M. H37Ra, M. phlei, M. smegmatis,\nM. timereck. The most active compounds were those with 2-pyridine ring. They exhibited lower\nminimal inhibitory concentration (MIC) values in the range...................
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