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

Journal: Bioinformatics (Oxford, England)
Published Date: Aug 15, 2017

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.

Authors

  • Anand Sastry
    Department of Bioengineering, University of California, San Diego, CA, USA.
  • Jonathan Monk
    Department of Bioengineering, University of California, San Diego, CA, USA.
  • Hanna Tegel
    KTH - Royal Institute of Technology, Department of Proteomics and Nanobiotechnology, SE-106 91 Stockholm, Sweden.
  • Mathias Uhlen
    KTH - Royal Institute of Technology, Department of Proteomics and Nanobiotechnology, SE-106 91 Stockholm, Sweden.
  • Bernhard O Palsson
    Department of Bioengineering, University of California, San Diego, CA, USA.
  • Johan Rockberg
    KTH - Royal Institute of Technology, Department of Proteomics and Nanobiotechnology, SE-106 91 Stockholm, Sweden.
  • Elizabeth Brunk
    Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.

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