MINLP Models for the Synthesis of Optimal Peptide Tags and Downstream Protein Processing

Authors

  • Evangelos Simeonidis,

    1. Centre for Process Systems Engineering, Department of Chemical Engineering, UCL (University College London), London WC1E 7JE, U.K.
    Search for more papers by this author
  • Jose M. Pinto,

    1. Department of Chemical and Biological Sciences and Engineering, Polytechnic University, Brooklyn, New York 11201
    Search for more papers by this author
  • M. Elena Lienqueo,

    1. Department of Chemical Engineering, University of São Paulo, São Paulo, SP 05508–900, Brazil
    Search for more papers by this author
  • Sophia Tsoka,

    1. Centre for Biochemical Engineering and Biotechnology, Department of Chemical Engineering, Millennium Institute for Advanced Studies in Cell Biology and Biotechnology, University of Chile, Beauchef 861, Santiago, Chile
    Search for more papers by this author
  • Lazaros G. Papageorgiou

    Corresponding author
    1. Centre for Process Systems Engineering, Department of Chemical Engineering, UCL (University College London), London WC1E 7JE, U.K.
    2. Computational Genomics Group, Research Program, The European Bioinformatics Institute, EMBL Cambridge Outstation, Cambridge CB10 1SD, U.K.
    • Centre for Process Systems Engineering, Department of Chemical Engineering, UCL (University College London), London WC1E 7JE, U.K. Tel: +44 20 7679 2563. Fax: +44 20 7383 2348
    Search for more papers by this author

Abstract

The development of systematic methods for the synthesis of downstream protein processing operations has seen growing interest in recent years, as purification is often the most complex and costly stage in biochemical production plants. The objective of the work presented here is to develop mathematical models based on mixed integer optimization techniques, which integrate the selection of optimal peptide purification tags into an established framework for the synthesis of protein purification processes. Peptide tags are comparatively short sequences of amino acids fused onto the protein product, capable of reducing the required purification steps. The methodology is illustrated through its application on two example protein mixtures involving up to 13 contaminants and a set of 11 candidate chromatographic steps. The results are indicative of the benefits resulting by the appropriate use of peptide tags in purification processes and provide a guideline for both optimal tag design and downstream process synthesis.

Ancillary