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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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Correlations between computational design parameters and experimentally measured performance metrics.A Sseq vs. Tm. B Sseq vs. Km. C Sseq vs. kcat. D Sseq vs. kcat/Km. E Global Quantitative Immunoreactivity vs. Sepi. (former as defined in equation 4). Pareto optimal enzymes are shown as blue circular markers, sub-optimal 4-mutation variants are shown as orange circular markers, and wild type P99βL is shown as a red square. Linear regressions are shown along with R2 values, and an F test was used to determine statistical significance for non-zero slopes (P values are provided).
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pcbi-1003988-g004: Correlations between computational design parameters and experimentally measured performance metrics.A Sseq vs. Tm. B Sseq vs. Km. C Sseq vs. kcat. D Sseq vs. kcat/Km. E Global Quantitative Immunoreactivity vs. Sepi. (former as defined in equation 4). Pareto optimal enzymes are shown as blue circular markers, sub-optimal 4-mutation variants are shown as orange circular markers, and wild type P99βL is shown as a red square. Linear regressions are shown along with R2 values, and an F test was used to determine statistical significance for non-zero slopes (P values are provided).

Mentions: Finally, it should be noted that the sequence potential was intended, in part, to quantify the likelihood that mutations or combinations of mutations would maintain P99βL structural integrity. A plot of Sseq vs. apparent Tm yielded the expected inverse relationship, and a linear regression showed that the correlation was highly significant (non-zero slope, P = 0.0019) (Fig. 4A). While the sequence potential was not an accurate predictor of individual Tm values (linear R2 = 0.44), from a global perspective it did effectively capture this aspect of experimental performance.


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)

Correlations between computational design parameters and experimentally measured performance metrics.A Sseq vs. Tm. B Sseq vs. Km. C Sseq vs. kcat. D Sseq vs. kcat/Km. E Global Quantitative Immunoreactivity vs. Sepi. (former as defined in equation 4). Pareto optimal enzymes are shown as blue circular markers, sub-optimal 4-mutation variants are shown as orange circular markers, and wild type P99βL is shown as a red square. Linear regressions are shown along with R2 values, and an F test was used to determine statistical significance for non-zero slopes (P values are provided).
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003988-g004: Correlations between computational design parameters and experimentally measured performance metrics.A Sseq vs. Tm. B Sseq vs. Km. C Sseq vs. kcat. D Sseq vs. kcat/Km. E Global Quantitative Immunoreactivity vs. Sepi. (former as defined in equation 4). Pareto optimal enzymes are shown as blue circular markers, sub-optimal 4-mutation variants are shown as orange circular markers, and wild type P99βL is shown as a red square. Linear regressions are shown along with R2 values, and an F test was used to determine statistical significance for non-zero slopes (P values are provided).
Mentions: Finally, it should be noted that the sequence potential was intended, in part, to quantify the likelihood that mutations or combinations of mutations would maintain P99βL structural integrity. A plot of Sseq vs. apparent Tm yielded the expected inverse relationship, and a linear regression showed that the correlation was highly significant (non-zero slope, P = 0.0019) (Fig. 4A). While the sequence potential was not an accurate predictor of individual Tm values (linear R2 = 0.44), from a global perspective it did effectively capture this aspect of experimental performance.

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
Related in: MedlinePlus