Pharmazie
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Nep1 (Emg1) is a highly conserved nucleolar protein with an essential function in ribosome biogenesis. A mutation in the human Nep1 homolog causes Bowen–Conradi syndrome—a severe developmental disorder. Structures of Nep1 revealed a dimer with a fold similar to the SPOUT-class of RNA-methyltransferases suggesting that Nep1 acts as a methyltransferase in ribosome biogenesis. The target for this putative methyltransferase activity has not been identified yet. We characterized the RNA-binding specificity of Methanocaldococcus jannaschii Nep1 by fluorescence- and NMR-spectroscopy as well as by yeast three-hybrid screening. Nep1 binds with high affinity to short RNA oligonucleotides corresponding to nt 910–921 of M. jannaschii 16S rRNA through a highly conserved basic surface cleft along the dimer interface. Nep1 only methylates RNAs containing a pseudouridine at a position corresponding to a previously identified hypermodified N1-methyl-N3-(3-amino-3-carboxypropyl) pseudouridine (m1acp3-Psi) in eukaryotic 18S rRNAs. Analysis of the methylated nucleoside by MALDI-mass spectrometry, HPLC and NMR shows that the methyl group is transferred to the N1 of the pseudouridine. Thus, Nep1 is the first identified example of an N1-specific pseudouridine methyltransferase. This enzymatic activity is also conserved in human Nep1 suggesting that Nep1 is the methyltransferase in the biosynthesis of m1acp3-Psi in eukaryotic 18S rRNAs.
Poster presentation at 5th German Conference on Cheminformatics: 23. CIC-Workshop Goslar, Germany. 8-10 November 2009 We demonstrate the theoretical and practical application of modern kernel-based machine learning methods to ligand-based virtual screening by successful prospective screening for novel agonists of the peroxisome proliferator-activated receptor gamma (PPARgamma) [1]. PPARgamma is a nuclear receptor involved in lipid and glucose metabolism, and related to type-2 diabetes and dyslipidemia. Applied methods included a graph kernel designed for molecular similarity analysis [2], kernel principle component analysis [3], multiple kernel learning [4], and, Gaussian process regression [5]. In the machine learning approach to ligand-based virtual screening, one uses the similarity principle [6] to identify potentially active compounds based on their similarity to known reference ligands. Kernel-based machine learning [7] uses the "kernel trick", a systematic approach to the derivation of non-linear versions of linear algorithms like separating hyperplanes and regression. Prerequisites for kernel learning are similarity measures with the mathematical property of positive semidefiniteness (kernels). The iterative similarity optimal assignment graph kernel (ISOAK) [2] is defined directly on the annotated structure graph, and was designed specifically for the comparison of small molecules. In our virtual screening study, its use improved results, e.g., in principle component analysis-based visualization and Gaussian process regression. Following a thorough retrospective validation using a data set of 176 published PPARgamma agonists [8], we screened a vendor library for novel agonists. Subsequent testing of 15 compounds in a cell-based transactivation assay [9] yielded four active compounds. The most interesting hit, a natural product derivative with cyclobutane scaffold, is a full selective PPARgamma agonist (EC50 = 10 ± 0.2 microM, inactive on PPARalpha and PPARbeta/delta at 10 microM). We demonstrate how the interplay of several modern kernel-based machine learning approaches can successfully improve ligand-based virtual screening results.