title: Machine learning in computational biology to accelerate high-throughput protein expression.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  BACKGROUND
  ebrunk@ucsd.edu or johanr@biotech.kth.se.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Bioengineering, University of California, San Diego, CA, USA.
authors.forename: Anand
authors.initials: A
authors.lastname: Sastry
authors.affiliation: Department of Bioengineering, University of California, San Diego, CA, USA.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Monk
authors.affiliation: KTH - Royal Institute of Technology, Department of Proteomics and Nanobiotechnology, SE-106 91 Stockholm, Sweden.
authors.forename: Hanna
authors.initials: H
authors.lastname: Tegel
authors.affiliation: KTH - Royal Institute of Technology, Department of Proteomics and Nanobiotechnology, SE-106 91 Stockholm, Sweden.|The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark.
authors.forename: Mathias
authors.initials: M
authors.lastname: Uhlen
authors.affiliation: Department of Bioengineering, University of California, San Diego, CA, USA.|The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark.
authors.forename: Bernhard O
authors.initials: BO
authors.lastname: Palsson
authors.affiliation: KTH - Royal Institute of Technology, Department of Proteomics and Nanobiotechnology, SE-106 91 Stockholm, Sweden.
authors.forename: Johan
authors.initials: J
authors.lastname: Rockberg
authors.affiliation: Department of Bioengineering, University of California, San Diego, CA, USA.|The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Brunk
mesh_terms: D019295:Computational Biology / Q000379:methods*; D004926:Escherichia coli / Q000235:genetics; D005786:Gene Expression Regulation; D006801:Humans; D000069550:Machine Learning; D009928:Organ Specificity; D020543:Proteome / Q000737:chemistry / Q000235:genetics* / Q000378:metabolism; D012995:Solubility
chemical_list: D020543:Proteome