Machine learning in computational biology to accelerate high-throughput protein expression

Anand Sastry, Jonathan M. Monk, Hanna Tegel, Mathias Uhlén, Bernhard Palsson, Johan Rockberg, Elizabeth Brunk

Research output: Contribution to journalJournal articleResearchpeer-review

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 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.
Original languageEnglish
JournalBioinformatics
Volume33
Issue number16
Pages (from-to)2487-2495
ISSN1367-4803
DOIs
Publication statusPublished - 2017

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