Machine Learning in Computational Biology to Accelerate High-Throughput Protein Expression.

TitleMachine Learning in Computational Biology to Accelerate High-Throughput Protein Expression.
Publication TypeJournal Article
Year of Publication2017
AuthorsSastry A, Monk J, Tegel H, Uhlen M, Palsson BO, Rockberg J, Brunk E
JournalBioinformatics
PubMed Date04/2017
ISSN1367-4811
Abstract

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 molecularlevel 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: 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.
Contact: ebrunk@ucsd.edu and johanr@biotech.kth.se.
Supplementary information: Supplementary data are available at Bioinformatics online.

Alternate JournalBioinformatics
PubMed ID28398465
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