Exploring sequence characteristics related to high-level production of secreted proteins in Aspergillus niger
| Autor: | Johannes Andries Roubos, Liang Wu, Bastiaan A. van den Berg, Marcel J. T. Reinders, Herman Jan Pel, Marc Hulsman, Dick de Ridder |
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| Jazyk: | angličtina |
| Rok vydání: | 2012 |
| Předmět: |
Signal peptide
Applied Microbiology Genes Fungal Molecular Sequence Data Gene Expression Heterologous lcsh:Medicine Biochemistry Fungal Proteins Industrial Microbiology Protein sequencing Artificial Intelligence Molecular Cell Biology Gene expression Aspergillosis Amino Acid Sequence lcsh:Science Biology Gene Genetics Extracellular Matrix Proteins Multidisciplinary biology Gene Expression Profiling Aspergillus niger lcsh:R Fungal Diseases Computational Biology Proteins Protein engineering biology.organism_classification Recombinant Proteins Enzymes Infectious Diseases OA-Fund TU Delft Medicine Electrophoresis Polyacrylamide Gel Membranes and Sorting lcsh:Q Expression cassette Genetic Engineering Sequence Analysis Research Article Biotechnology |
| Zdroj: | PLoS ONE, Vol 7, Iss 10, p e45869 (2012) PLoS ONE PLoS ONE, 7 (10), 2012 |
| ISSN: | 1932-6203 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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