Exploring Sequence Characteristics Related to High-Level Production of Secreted Proteins in Aspergillus niger |
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| Authors: | Bastiaan A van den Berg Marcel J T Reinders Marc Hulsman Liang Wu Herman J Pel Johannes A Roubos Dick de Ridder |
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| Institution: | 1Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands;2Netherlands Bioinformatics Centre, Nijmegen, The Netherlands;3DSM Biotechnology Center, Delft, The Netherlands;4Kluyver Centre for Genomics of Industrial Fermentation, Delft, The Netherlands;Universidade de Sao Paulo, Brazil |
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| 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. |
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