Optimization algorithms for functional deimmunization of therapeutic proteins
1 Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA
2 Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA
BMC Bioinformatics 2010, 11:180 doi:10.1186/1471-2105-11-180Published: 9 April 2010
To develop protein therapeutics from exogenous sources, it is necessary to mitigate the risks of eliciting an anti-biotherapeutic immune response. A key aspect of the response is the recognition and surface display by antigen-presenting cells of epitopes, short peptide fragments derived from the foreign protein. Thus, developing minimal-epitope variants represents a powerful approach to deimmunizing protein therapeutics. Critically, mutations selected to reduce immunogenicity must not interfere with the protein's therapeutic activity.
This paper develops methods to improve the likelihood of simultaneously reducing the anti-biotherapeutic immune response while maintaining therapeutic activity. A dynamic programming approach identifies optimal and near-optimal sets of conservative point mutations to minimize the occurrence of predicted T-cell epitopes in a target protein. In contrast with existing methods, those described here integrate analysis of immunogenicity and stability/activity, are broadly applicable to any protein class, guarantee global optimality, and provide sufficient flexibility for users to limit the total number of mutations and target MHC alleles of interest. The input is simply the primary amino acid sequence of the therapeutic candidate, although crystal structures and protein family sequence alignments may also be input when available. The output is a scored list of sets of point mutations predicted to reduce the protein's immunogenicity while maintaining structure and function. We demonstrate the effectiveness of our approach in a number of case study applications, showing that, in general, our best variants are predicted to be better than those produced by previous deimmunization efforts in terms of either immunogenicity or stability, or both factors.
By developing global optimization algorithms leveraging well-established immunogenicity and stability prediction techniques, we provide the protein engineer with a mechanism for exploring the favorable sequence space near a targeted protein therapeutic. Our mechanism not only helps identify designs more likely to be effective, but also provides insights into the interrelated implications of design choices.