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J Bioinform Comput Biol. 2011 Apr;9(2):207-29.

Optimization of therapeutic proteins to delete T-cell epitopes while maintaining beneficial residue interactions.

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Department of Computer Science, Dartmouth College, Sudikoff Laboratory, Hanover, NH 03755, USA.


Exogenous enzymes, signaling peptides, and other classes of nonhuman proteins represent a potentially massive but largely untapped pool of biotherapeutic agents. Adapting a foreign protein for therapeutic use poses numerous design challenges. We focus here on one significant problem: modifying the protein to mitigate the immune response mounted against "non-self" proteins, while not adversely affecting the protein's stability or therapeutic activity. In order to propose such variants suitable for experimental evaluation, this paper develops a computational method to select sets of mutations predicted to delete immunogenic T-cell epitopes, as evaluated by a 9-mer potential, while simultaneously maintaining important residues and residue interactions, as evaluated by one- and two-body potentials. While this design problem is NP-hard, we develop an integer programming approach that works very well in practice. We demonstrate the effectiveness of our approach by developing plans for biotherapeutic proteins that, in previous studies, have been partially deimmunized via extensive experimental characterization and modification of limited segments. In contrast, our global optimization technique considers an entire protein and accounts for all residues, residue interactions, and epitopes in proposing candidates worth subjecting to experimental evaluation.

[Indexed for MEDLINE]

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