MINLP models for the synthesis of optimal peptide tags and downstream protein processing

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.