Exploring sequence characteristics related to high-level production of secreted proteins in Aspergillus niger.
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.
Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands.
Bastiaan A
BA
van den Berg
Marcel J T
MJ
Reinders
Marc
M
Hulsman
Liang
L
Wu
Herman J
HJ
Pel
Johannes A
JA
Roubos
Dick
D
de Ridder
D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D001234:Aspergillus niger / Q000201:enzymology / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D004591:Electrophoresis, Polyacrylamide Gel; D004798:Enzymes / Q000096:biosynthesis*; D005656:Fungal Proteins / Q000235:genetics*; D020869:Gene Expression Profiling; D005800:Genes, Fungal / Q000235:genetics*; D005818:Genetic Engineering / Q000379:methods; D007218:Industrial Microbiology / Q000379:methods*; D008969:Molecular Sequence Data
D004798:Enzymes; D005656:Fungal Proteins