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Purification and characterization of organic solvent and detergent tolerant lipase from thermotolerant Bacillus sp. RN2.

Pornpimon Kanjanavas | Sintawee Khuchareontaworn | Paisarn Khawsak | Arda Pakpitcharoen | Khajeenart Pothivejkul | Somchai Santiwatanakul | Kenji Matsui | Tadahiko Kajiwara | Kosum Chansiri
International journal of molecular sciences | 2010

The aim of this study was to characterize the organic solvent and detergent tolerant properties of recombinant lipase isolated from thermotolerant Bacillus sp. RN2 (Lip-SBRN2). The isolation of the lipase-coding gene was achieved by the use of inverse and direct PCR. The complete DNA sequencing of the gene revealed that the lip-SBRN2 gene contains 576 nucleotides which corresponded to 192 deduced amino acids. The purified enzyme was homogeneous with the estimated molecular mass of 19 kDa as determined by SDS-PAGE and gel filtration. The Lip-SBRN2 was stable in a pH range of 9-11 and temperature range of 45-60 °C. The enzyme was a non metallo-monomeric protein and was active against pNP-caprylate (C8) and pNP-laurate (C12) and coconut oil. The Lip-SBRN2 exhibited a high level of activity in the presence of 108% benzene, 102.4% diethylether and 112% SDS. It is anticipated that the organic solvent and detergent tolerant enzyme secreted by Bacillus sp. RN2 will be applicable as catalysts for reaction in the presence of organic solvents and detergents.

Pubmed ID: 21152301

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Non-profit academic organization for research and services in bioinformatics. Provides freely available data from life science experiments, performs basic research in computational biology, and offers user training programme, manages databases of biological data including nucleic acid, protein sequences, and macromolecular structures. Part of EMBL.

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SignalP (tool)

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Web application for prediction of the presence and location of signal peptide cleavage sites in amino acid sequences from different organisms. The method incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of several artificial neural networks.

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