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Your search keyword '"Reinders, Marcel J T"' showing total 10 results
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10 results on '"Reinders, Marcel J T"'

4. Analysis of Tumor Heterogeneity and Cancer Gene Networks Using Deep Sequencing of MMTV-Induced Mouse Mammary Tumors

Author

Klijn, Christiaan, primary, Koudijs, Marco J., additional, Kool, Jaap, additional, ten Hoeve, Jelle, additional, Boer, Mandy, additional, de Moes, Joost, additional, Akhtar, Waseem, additional, van Miltenburg, Martine, additional, Vendel-Zwaagstra, Annabel, additional, Reinders, Marcel J. T., additional, Adams, David J., additional, van Lohuizen, Maarten, additional, Hilkens, John, additional, Wessels, Lodewyk F. A., additional, and Jonkers, Jos, additional

Published
2013
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10. Exploring Sequence Characteristics Related to High-Level Production of Secreted Proteins in Aspergillus niger.

Author

den Berg, Bastiaan A. van, Reinders, Marcel J. T., Hulsman, Marc, Liang Wu, Pel, Herman J., Roubos, Johannes A., and de Ridder, Dick

Subjects
*AMINO acid sequence, *FUNGI, *ENZYMES, *PROTEIN engineering, *FUNGAL genes, *DNA
Abstract

Protein sequence features are explored in relation to the production of over-expressed extracellular proteins by fungi. Knowledge on features influencing protein production and secretion could be employed to improve enzyme production levels in industrial bioprocesses via protein engineering. A large set, over 600 homologous and nearly 2,000 heterologous fungal genes, were overexpressed in Aspergillus niger using a standardized expression cassette and scored for high versus no production. Subsequently, sequence-based machine learning techniques were applied for identifying relevant DNA and protein sequence features. The amino-acid composition of the protein sequence was found to be most predictive and interpretation revealed that, for both homologous and heterologous gene expression, the same features are important: tyrosine and asparagine composition was found to have a positive correlation with high-level production, whereas for unsuccessful production, contributions were found for methionine and lysine composition. The predictor is available online at http://bioinformatics.tudelft.nl/hipsec. Subsequent work aims at validating these findings by protein engineering as a method for increasing expression levels per gene copy. [ABSTRACT FROM AUTHOR]

Published
2012
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