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Machine learning in computational biology to accelerate high-throughput protein expression
KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. Technical University of Denmark - DTU.
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2017 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 33, no 16, p. 2487-2495Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Oxford University Press, 2017. Vol. 33, no 16, p. 2487-2495
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:kth:diva-212595DOI: 10.1093/bioinformatics/btx207ISI: 000407139800008Scopus ID: 2-s2.0-85044518554OAI: oai:DiVA.org:kth-212595DiVA, id: diva2:1135907
Funder
Knut and Alice Wallenberg FoundationNovo Nordisk, NNF10CC1016517
Note

QC 20170824

Available from: 2017-08-24 Created: 2017-08-24 Last updated: 2018-01-13Bibliographically approved

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