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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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Epitope map of P99βL.The P99βL sequence was analyzed using ProPred set to a 5% threshold. Every nonamer peptide was classified as a binder or non-binder of alleles DRB1*0101, 0401, 0701, and 1501. The number of alleles that bind each nonamer peptide (y-axis) is indicated by a bar at the starting position of the peptide (x-axis). The positions of engineered mutations from the deimmunized enzymes are indicated with blue arrows and residue numbers.
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pcbi-1003988-g001: Epitope map of P99βL.The P99βL sequence was analyzed using ProPred set to a 5% threshold. Every nonamer peptide was classified as a binder or non-binder of alleles DRB1*0101, 0401, 0701, and 1501. The number of alleles that bind each nonamer peptide (y-axis) is indicated by a bar at the starting position of the peptide (x-axis). The positions of engineered mutations from the deimmunized enzymes are indicated with blue arrows and residue numbers.

Mentions: Whereas previous computationally-driven deimmunization of P99βL had targeted eight common MHC II alleles [25], [31], here we optimized against only four alleles (DRB1*0101, 0401, 0701, and 1501) for which MHC II-peptide binding experiments had been fully optimized [33]. Analysis with the ProPred epitope prediction tool [20] revealed that putative immunogenic peptides were broadly distributed throughout the sequence, with several discrete regions exhibiting numerous overlapping and promiscuous epitopes, e.g. proximal to residues 14, 105, 210, 235, and 334 (Fig. 1). While our prior P99βL deimmunization efforts had focused on validation of our protein optimization algorithms, the objective of the current study was a systematic analysis of the sequence-function-immunoreactivity tradeoffs that are inherent to the deimmunization process.


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)

Epitope map of P99βL.The P99βL sequence was analyzed using ProPred set to a 5% threshold. Every nonamer peptide was classified as a binder or non-binder of alleles DRB1*0101, 0401, 0701, and 1501. The number of alleles that bind each nonamer peptide (y-axis) is indicated by a bar at the starting position of the peptide (x-axis). The positions of engineered mutations from the deimmunized enzymes are indicated with blue arrows and residue numbers.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003988-g001: Epitope map of P99βL.The P99βL sequence was analyzed using ProPred set to a 5% threshold. Every nonamer peptide was classified as a binder or non-binder of alleles DRB1*0101, 0401, 0701, and 1501. The number of alleles that bind each nonamer peptide (y-axis) is indicated by a bar at the starting position of the peptide (x-axis). The positions of engineered mutations from the deimmunized enzymes are indicated with blue arrows and residue numbers.
Mentions: Whereas previous computationally-driven deimmunization of P99βL had targeted eight common MHC II alleles [25], [31], here we optimized against only four alleles (DRB1*0101, 0401, 0701, and 1501) for which MHC II-peptide binding experiments had been fully optimized [33]. Analysis with the ProPred epitope prediction tool [20] revealed that putative immunogenic peptides were broadly distributed throughout the sequence, with several discrete regions exhibiting numerous overlapping and promiscuous epitopes, e.g. proximal to residues 14, 105, 210, 235, and 334 (Fig. 1). While our prior P99βL deimmunization efforts had focused on validation of our protein optimization algorithms, the objective of the current study was a systematic analysis of the sequence-function-immunoreactivity tradeoffs that are inherent to the deimmunization process.

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