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Mapping the Pareto optimal design space for a functionally deimmunized biotherapeutic candidate.

Salvat RS, Parker AS, Choi Y, Bailey-Kellogg C, Griswold KE - PLoS Comput. Biol. (2015)

Bottom Line: As a result, there is a growing need for improved deimmunization technologies.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.

View Article: PubMed Central - PubMed

Affiliation: Thayer School of Engineering, Dartmouth, Hanover, New Hampshire, United States of America.

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.

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Pareto frontier of the P99βL deimmunized design space.The computed Sseq design parameter is plotted vs. the computed Sepi design parameter for 19 unique enzyme plans. Sseq derives from a statistical sequence potential, and is analogous to an energy function such that lower values are better. Sepi is the total predicted epitope count for each protein. Pareto optimal designs, i.e. those for which no other single design has both better epitope and sequence scores, are indicated with blue circular markers. In orange are three 4-mutation designs that are Pareto optimal at their specific mutational load but are outperformed by designs at higher mutational loads. Wild type P99βL is indicated with a red square. Mutational loads are indicated adjacent to their cognate markers. For three representative proteins, the epitope content has been mapped onto the P99βL peptide backbone (PDB ID 1XX2A). Dense regions of overlapping epitopes are shown as thick red tubes, and lower densities are indicated with incrementally thinner tubes colored in a gradient red-orange-yellow-green-blue. Epitope free regions are thin grey tubes. 3-dimensional epitope maps are shown for wild type P99βL, design 4O, and design 8Z.
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pcbi-1003988-g002: Pareto frontier of the P99βL deimmunized design space.The computed Sseq design parameter is plotted vs. the computed Sepi design parameter for 19 unique enzyme plans. Sseq derives from a statistical sequence potential, and is analogous to an energy function such that lower values are better. Sepi is the total predicted epitope count for each protein. Pareto optimal designs, i.e. those for which no other single design has both better epitope and sequence scores, are indicated with blue circular markers. In orange are three 4-mutation designs that are Pareto optimal at their specific mutational load but are outperformed by designs at higher mutational loads. Wild type P99βL is indicated with a red square. Mutational loads are indicated adjacent to their cognate markers. For three representative proteins, the epitope content has been mapped onto the P99βL peptide backbone (PDB ID 1XX2A). Dense regions of overlapping epitopes are shown as thick red tubes, and lower densities are indicated with incrementally thinner tubes colored in a gradient red-orange-yellow-green-blue. Epitope free regions are thin grey tubes. 3-dimensional epitope maps are shown for wild type P99βL, design 4O, and design 8Z.

Mentions: The resulting output was a panel of 18 P99βL designs that exhibited a range of mutational loads and extents of epitope disruption. A plot of Sseq vs. Sepi for the 18 protein plans enabled visualization of the objective functions' competing nature (Fig. 2). The overarching goal was reduction of P99βL epitope score via mutagenic deletion of predicted epitopes; however each deimmunizing mutation incurs an Sseq penalty. Any increase above the wild type Sseq reflects a putative risk of reduced protein stability and/or function, and therefore mutagenic deimmunization must carefully balance the opposing objective functions. The Pareto frontier analysis (Fig. 2) highlights the relative tradeoffs between predictions of epitope content and biological activity, but the practical relationship between these mathematical functions is an unknown quantity. Thus, experimental analysis is ultimately required to understand the magnitude of biological activity that is sacrificed per unit immunogenicity.


Mapping the Pareto optimal design space for a functionally deimmunized biotherapeutic candidate.

Salvat RS, Parker AS, Choi Y, Bailey-Kellogg C, Griswold KE - PLoS Comput. Biol. (2015)

Pareto frontier of the P99βL deimmunized design space.The computed Sseq design parameter is plotted vs. the computed Sepi design parameter for 19 unique enzyme plans. Sseq derives from a statistical sequence potential, and is analogous to an energy function such that lower values are better. Sepi is the total predicted epitope count for each protein. Pareto optimal designs, i.e. those for which no other single design has both better epitope and sequence scores, are indicated with blue circular markers. In orange are three 4-mutation designs that are Pareto optimal at their specific mutational load but are outperformed by designs at higher mutational loads. Wild type P99βL is indicated with a red square. Mutational loads are indicated adjacent to their cognate markers. For three representative proteins, the epitope content has been mapped onto the P99βL peptide backbone (PDB ID 1XX2A). Dense regions of overlapping epitopes are shown as thick red tubes, and lower densities are indicated with incrementally thinner tubes colored in a gradient red-orange-yellow-green-blue. Epitope free regions are thin grey tubes. 3-dimensional epitope maps are shown for wild type P99βL, design 4O, and design 8Z.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4288714&req=5

pcbi-1003988-g002: Pareto frontier of the P99βL deimmunized design space.The computed Sseq design parameter is plotted vs. the computed Sepi design parameter for 19 unique enzyme plans. Sseq derives from a statistical sequence potential, and is analogous to an energy function such that lower values are better. Sepi is the total predicted epitope count for each protein. Pareto optimal designs, i.e. those for which no other single design has both better epitope and sequence scores, are indicated with blue circular markers. In orange are three 4-mutation designs that are Pareto optimal at their specific mutational load but are outperformed by designs at higher mutational loads. Wild type P99βL is indicated with a red square. Mutational loads are indicated adjacent to their cognate markers. For three representative proteins, the epitope content has been mapped onto the P99βL peptide backbone (PDB ID 1XX2A). Dense regions of overlapping epitopes are shown as thick red tubes, and lower densities are indicated with incrementally thinner tubes colored in a gradient red-orange-yellow-green-blue. Epitope free regions are thin grey tubes. 3-dimensional epitope maps are shown for wild type P99βL, design 4O, and design 8Z.
Mentions: The resulting output was a panel of 18 P99βL designs that exhibited a range of mutational loads and extents of epitope disruption. A plot of Sseq vs. Sepi for the 18 protein plans enabled visualization of the objective functions' competing nature (Fig. 2). The overarching goal was reduction of P99βL epitope score via mutagenic deletion of predicted epitopes; however each deimmunizing mutation incurs an Sseq penalty. Any increase above the wild type Sseq reflects a putative risk of reduced protein stability and/or function, and therefore mutagenic deimmunization must carefully balance the opposing objective functions. The Pareto frontier analysis (Fig. 2) highlights the relative tradeoffs between predictions of epitope content and biological activity, but the practical relationship between these mathematical functions is an unknown quantity. Thus, experimental analysis is ultimately required to understand the magnitude of biological activity that is sacrificed per unit immunogenicity.

Bottom Line: As a result, there is a growing need for improved deimmunization technologies.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.

View Article: PubMed Central - PubMed

Affiliation: Thayer School of Engineering, Dartmouth, Hanover, New Hampshire, United States of America.

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.

Show MeSH