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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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Detailed view of T cell epitopes targeted for disruption.The four MHC II alleles of interest are shown on the y-axis, and peptide sub-sequences of P99βL are shown on the x-axis. Deimmunizing mutations are specified above each graphic, and sites of mutation are indicated by asterisks on the x-axis. The precise positions of predicted T cell epitopes in wild type P99βL are indicated by solid black lines. Predicted epitopes in the specified engineered sequence are indicated with hatched orange lines. Overlapping black and orange lines are predicted epitopes not deleted by the specified mutation or mutations.
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pcbi-1003988-g003: Detailed view of T cell epitopes targeted for disruption.The four MHC II alleles of interest are shown on the y-axis, and peptide sub-sequences of P99βL are shown on the x-axis. Deimmunizing mutations are specified above each graphic, and sites of mutation are indicated by asterisks on the x-axis. The precise positions of predicted T cell epitopes in wild type P99βL are indicated by solid black lines. Predicted epitopes in the specified engineered sequence are indicated with hatched orange lines. Overlapping black and orange lines are predicted epitopes not deleted by the specified mutation or mutations.

Mentions: Incrementally enhanced deimmunization, moving from right to left on the Pareto curve (Fig. 2), was realized by three complementary mechanisms. First, increasing mutational loads allowed for simultaneous disruption of multiple, distributed epitope clusters. Compare, for example, design 1I, which targets a single epitope with one mutation, to design 8Z, which targets seven distinct immunogenic regions with eight mutations (Table 1). Second, in some instances accrued mutations were combined in close proximity to better target one particularly immunogenic region. For example, designs 4M through 7S as well as plan 8U encoded the R105S mutation, which was predicted to disrupt three of seven epitopes in a dense cluster centered on position 105 (Fig. 3). The more ambitious designs 8V through 8Z deleted six of these same seven epitopes with the combined G103D+R105S double mutation. The mutational combinations M235Q+V243L and Q333D+I334L were likewise predicted to yield enhanced epitope deletion relative to their single mutation counterparts (Fig. 3). In parallel to escalating mutational loads, a third mechanism for improved epitope deletion was the use of increasingly aggressive individual mutations. In particular, mutation N14R was associated with three designs possessing moderate sequence scores (4N, 5R, and 8V; Sseq range of 17.1 to 41.4; Table 1), but it deleted only three of six epitopes in the dense cluster centered on residue 14 (Fig. 3). Mutation A13E, employed by six designs having a Sseq range of 25.3 to 107.6, disrupted five of the six epitopes in this cluster. Finally, A13D deleted all six predicted epitopes, but this aggressive substitution contributed to particularly poor overall sequence scores (Sseq = 98.8 and 144.8 for designs 4P and 8Z, respectively). In aggregate, incremental increases in mutational load and mutational stringency produced a systematic series of deimmunized designs ranging from the wild type Sepi = 60 to that of variant 8Z (Sepi = 32), in which almost half of the predicted epitopes were targeted for disruption.


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)

Detailed view of T cell epitopes targeted for disruption.The four MHC II alleles of interest are shown on the y-axis, and peptide sub-sequences of P99βL are shown on the x-axis. Deimmunizing mutations are specified above each graphic, and sites of mutation are indicated by asterisks on the x-axis. The precise positions of predicted T cell epitopes in wild type P99βL are indicated by solid black lines. Predicted epitopes in the specified engineered sequence are indicated with hatched orange lines. Overlapping black and orange lines are predicted epitopes not deleted by the specified mutation or mutations.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003988-g003: Detailed view of T cell epitopes targeted for disruption.The four MHC II alleles of interest are shown on the y-axis, and peptide sub-sequences of P99βL are shown on the x-axis. Deimmunizing mutations are specified above each graphic, and sites of mutation are indicated by asterisks on the x-axis. The precise positions of predicted T cell epitopes in wild type P99βL are indicated by solid black lines. Predicted epitopes in the specified engineered sequence are indicated with hatched orange lines. Overlapping black and orange lines are predicted epitopes not deleted by the specified mutation or mutations.
Mentions: Incrementally enhanced deimmunization, moving from right to left on the Pareto curve (Fig. 2), was realized by three complementary mechanisms. First, increasing mutational loads allowed for simultaneous disruption of multiple, distributed epitope clusters. Compare, for example, design 1I, which targets a single epitope with one mutation, to design 8Z, which targets seven distinct immunogenic regions with eight mutations (Table 1). Second, in some instances accrued mutations were combined in close proximity to better target one particularly immunogenic region. For example, designs 4M through 7S as well as plan 8U encoded the R105S mutation, which was predicted to disrupt three of seven epitopes in a dense cluster centered on position 105 (Fig. 3). The more ambitious designs 8V through 8Z deleted six of these same seven epitopes with the combined G103D+R105S double mutation. The mutational combinations M235Q+V243L and Q333D+I334L were likewise predicted to yield enhanced epitope deletion relative to their single mutation counterparts (Fig. 3). In parallel to escalating mutational loads, a third mechanism for improved epitope deletion was the use of increasingly aggressive individual mutations. In particular, mutation N14R was associated with three designs possessing moderate sequence scores (4N, 5R, and 8V; Sseq range of 17.1 to 41.4; Table 1), but it deleted only three of six epitopes in the dense cluster centered on residue 14 (Fig. 3). Mutation A13E, employed by six designs having a Sseq range of 25.3 to 107.6, disrupted five of the six epitopes in this cluster. Finally, A13D deleted all six predicted epitopes, but this aggressive substitution contributed to particularly poor overall sequence scores (Sseq = 98.8 and 144.8 for designs 4P and 8Z, respectively). In aggregate, incremental increases in mutational load and mutational stringency produced a systematic series of deimmunized designs ranging from the wild type Sepi = 60 to that of variant 8Z (Sepi = 32), in which almost half of the predicted epitopes were targeted for disruption.

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