In collaboration with Karl Griswold (engineering, Dartmouth), we are developing and experimentally applying a suite of general methods integrating computation and experiment in order to produce immunotolerant variants of therapeutic proteins, in which immunogenicity is reduced while therapeutic activity is maintained. The majority of therapeutic proteins elicit an anti-biotherapeutic immune response (aBIR) in human patients, driving the search for strategies to detect, assess, and ameliorate potentially deleterious immune responses. Our approach promises not only to improve the state of the art in humanization of antibodies, but also to open new doors to humanization of enzymes.

In our DP2 (Dynamic Programming for Deimmunizing Proteins) approach, we establish as our primary optimization objective reduction of immunogenicity, according to predicted T-cell epitopes within the sequence. In order to also address the complementary/competing concern of maintaining stability and activity, we identify for each residue position those mutations that are deemed acceptable according to sequence and/or structure-based analyses. A dynamic programming approach then finds globally optimal and near-optimal sets of these acceptable mutations that minimize the occurrence of predicted epitopes.

In our IP2 (Integer Programming for Immunogenic Proteins) approach, we extend the objective to account for residue interactions, e.g., based on pairwise covariation analysis from a set of homologs. Thus we seek 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. An integer programming approach finds sets of mutations optimizing a specified trade-off between these objectives.