title: Mapping the Pareto optimal design space for a functionally deimmunized biotherapeutic candidate.
abstract: The immunogenicity of biotherapeutics can bottleneck development pipelines and poses a barrier to widespread clinical application. As a result, there is a growing need for improved deimmunization technologies. We have recently described algorithms that simultaneously optimize proteins for both reduced T cell epitope content and high-level function. In silico analysis of this dual objective design space reveals that there is no single global optimum with respect to protein deimmunization. Instead, mutagenic epitope deletion yields a spectrum of designs that exhibit tradeoffs between immunogenic potential and molecular function. The leading edge of this design space is the Pareto frontier, i.e. the undominated variants for which no other single design exhibits better performance in both criteria. Here, the Pareto frontier of a therapeutic enzyme has been designed, constructed, and evaluated experimentally. Various measures of protein performance were found to map a functional sequence space that correlated well with computational predictions. These results represent the first systematic and rigorous assessment of the functional penalty that must be paid for pursuing progressively more deimmunized biotherapeutic candidates. Given this capacity to rapidly assess and design for tradeoffs between protein immunogenicity and functionality, these algorithms may prove useful in augmenting, accelerating, and de-risking experimental deimmunization efforts.
authors.affiliation: Thayer School of Engineering, Dartmouth, Hanover, New Hampshire, United States of America.
authors.forename: Regina S
authors.initials: RS
authors.lastname: Salvat
authors.affiliation: Department of Computer Science, Dartmouth, Hanover, New Hampshire, United States of America.
authors.forename: Andrew S
authors.initials: AS
authors.lastname: Parker
authors.affiliation: Department of Computer Science, Dartmouth, Hanover, New Hampshire, United States of America.
authors.forename: Yoonjoo
authors.initials: Y
authors.lastname: Choi
authors.affiliation: Department of Computer Science, Dartmouth, Hanover, New Hampshire, United States of America.
authors.forename: Chris
authors.initials: C
authors.lastname: Bailey-Kellogg
authors.affiliation: Thayer School of Engineering, Dartmouth, Hanover, New Hampshire, United States of America; Program in Molecular and Cellular Biology, Dartmouth, Hanover, New Hampshire, United States of America.
authors.forename: Karl E
authors.initials: KE
authors.lastname: Griswold
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D018984:Epitopes, T-Lymphocyte / Q000737:chemistry / Q000235:genetics / Q000276:immunology* / Q000378:metabolism; D006801:Humans; D015233:Models, Statistical; D011485:Protein Binding; D015202:Protein Engineering / Q000379:methods*; D011994:Recombinant Proteins / Q000737:chemistry / Q000235:genetics / Q000276:immunology* / Q000378:metabolism
chemical_list: D018984:Epitopes, T-Lymphocyte; D011994:Recombinant Proteins