Journal article
Machine learning in computational biology to accelerate high-throughput protein expression
University of California at San Diego1
KTH Royal Institute of Technology2
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark3
High Throughput Molecular Bioscience, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark4
Big Data 2 Knowledge, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark5
Network Reconstruction in Silico Biology, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark6
Technical University of Denmark7
Motivation: The Human Protein Atlas (HPA) enables the simultaneous characterization of thousands of proteins across various tissues to pinpoint their spatial location in the human body. This has been achieved through transcriptomics and high-throughput immunohistochemistry-based approaches, where over 40 000 unique human protein fragments have been expressed in E. coli.
These datasets enable quantitative tracking of entire cellular proteomes and present new avenues for understanding molecular-level properties influencing expression and solubility. Results: Combining computational biology and machine learning identifies protein properties that hinder the HPA high-throughput antibody production pipeline.
We predict protein expression and solubility with accuracies of 70% and 80%, respectively, based on a subset of key properties (aromaticity, hydropathy and isoelectric point). We guide the selection of protein fragments based on these characteristics to optimize high-throughput experimentation. Availability and implementation: We present the machine learning workflow as a series of IPython notebooks hosted on GitHub (https://github.com/SBRG/Protein_ML).
The workflow can be used as a template for analysis of further expression and solubility datasets.
Language: | English |
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Publisher: | Oxford University Press |
Year: | 2017 |
Pages: | 2487-2495 |
ISSN: | 13674811 , 13674803 and 14602059 |
Types: | Journal article |
DOI: | 10.1093/bioinformatics/btx207 |
ORCIDs: | Palsson, Bernhard |