Optimization of Combinatorial Mutagenesis

Parker, Andrew S.; Griswold, Karl E.; Bailey‐Kellogg, Chris

doi:10.1007/978-3-642-20036-6_29

Cited by 10 publications

(22 citation statements)

References 40 publications

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“…It significantly extends structurebased protein design by accounting for the complementary goal of immunogenicity. It likewise significantly extends our previous work on Pareto optimization for protein engineering in general (Zheng et al, 2009, He et al, 2012 and for deimmunization in particular, which assessed effects on structure and function only according to a sequence potential (Parker et al, 2010;Parker et al, 2011a, Parker et al, 2011b. Inspired by an approach for optimization of stability and specificity of interacting proteins (Grigoryan et al, 2009), we employ a sweep algorithm that minimizes the energy of the design target at decreasing predicted epitope scores.…”

Section: Introductionmentioning

confidence: 87%

Structure-Guided Deimmunization of Therapeutic Proteins

Parker

Griswold

Bailey‐Kellogg

2012

Lecture Notes in Computer Science

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Therapeutic proteins continue to yield revolutionary new treatments for a growing spectrum of human disease, but the development of these powerful drugs requires solving a unique set of challenges. For instance, it is increasingly apparent that mitigating potential antitherapeutic immune responses, driven by molecular recognition of a therapeutic protein's peptide fragments, may be best accomplished early in the drug development process. One may eliminate immunogenic peptide fragments by mutating the cognate amino acid sequences, but deimmunizing mutations are constrained by the need for a folded, stable, and functional protein structure. These two concerns may be competing, as the mutations that are best at reducing immunogenicity often involve amino acids that are substantially different physicochemically. We develop a novel approach, called EpiSweep, that simultaneously optimizes both concerns. Our algorithm identifies sets of mutations making such Pareto optimal trade-offs between structure and immunogenicity, embodied by a molecular mechanics energy function and a T-cell epitope predictor, respectively. EpiSweep integrates structure-based protein design, sequence-based protein deimmunization, and algorithms for finding the Pareto frontier of a design space. While structure-based protein design is NPhard, we employ integer programming techniques that are efficient in practice. Furthermore, EpiSweep only invokes the optimizer once per identified Pareto optimal design. We show that EpiSweep designs of regions of the therapeutics erythropoietin and staphylokinase are predicted to outperform previous experimental efforts. We also demonstrate EpiSweep's capacity for deimmunization of the entire proteins, case analyses involving dozens of predicted epitopes, and tens of thousands of unique side-chain interactions. Ultimately, Epi-Sweep is a powerful protein design tool that guides the protein engineer toward the most promising immunotolerant biotherapeutic candidates.

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Section: Introductionmentioning

confidence: 87%

Structure-Guided Deimmunization of Therapeutic Proteins

Parker

Griswold

Bailey‐Kellogg

2012

Lecture Notes in Computer Science

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“…The epitope score counts the number of peptide-allele hits at a standard 5% threshold. Libraries are designed by simultaneously selecting residue positions and degenerate oligonucleotides from a set of allowed positions and position-specific mutational choices derived from the sequence analysis (excluding mutations to/from proline and cysteine) (53).…”

Section: Methodsmentioning

confidence: 99%

Computationally optimized deimmunization libraries yield highly mutated enzymes with low immunogenicity and enhanced activity

Salvat

Verma

Parker

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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Therapeutic proteins of wide-ranging function hold great promise for treating disease, but immune surveillance of these macromolecules can drive an antidrug immune response that compromises efficacy and even undermines safety. To eliminate widespread T-cell epitopes in any biotherapeutic and thereby mitigate this key source of detrimental immune recognition, we developed a Pareto optimal deimmunization library design algorithm that optimizes protein libraries to account for the simultaneous effects of combinations of mutations on both molecular function and epitope content. Active variants identified by high-throughput screening are thus inherently likely to be deimmunized. Functional screening of an optimized 10-site library (1,536 variants) of P99 β-lactamase (P99βL), a component of ADEPT cancer therapies, revealed that the population possessed high overall fitness, and comprehensive analysis of peptide-MHC II immunoreactivity showed the population possessed lower average immunogenic potential than the wild-type enzyme. Although similar functional screening of an optimized 30-site library (2.15 × 10 9 variants) revealed reduced population-wide fitness, numerous individual variants were found to have activity and stability better than the wild type despite bearing 13 or more deimmunizing mutations per enzyme. The immunogenic potential of one highly active and stable 14-mutation variant was assessed further using ex vivo cellular immunoassays, and the variant was found to silence T-cell activation in seven of the eight blood donors who responded strongly to wild-type P99βL. In summary, our multiobjective library-design process readily identified large and mutually compatible sets of epitope-deleting mutations and produced highly active but aggressively deimmunized constructs in only one round of library screening.deimmunization | computational protein design | immunogenicity | T-cell epitope | combinatorial library P rotein engineering techniques have enabled targeted modification of biotherapeutics to mitigate the T-cell-mediated immune response that otherwise can undermine clinical applications (1). These approaches mutate specific amino acids in a therapeutic candidate so as to disrupt MHC II binding of constituent peptides processed from the protein following uptake by antigen-presenting cells (APCs). The amino acid substitutions prevent APCs from displaying peptide epitopes to cognate CD4 +

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“…As noted by Parker and colleagues (27), it follows from the NPhardness of the protein design problem (28) that the design of degenerate sequences varying non-independently at multiple positions is NP-hard. This finding holds whether designing a single template or multiple degenerate templates.…”

Section: • Instancementioning

confidence: 99%

“…A number of groups have developed computational methods to maximize the number of desired target sequences created using DCs under a user-specified library size. Unfortunately, this problem is NP-hard (27,28), meaning that a fast (polynomial time) algorithmic solution is extremely unlikely to exist. Instead, researchers have had to rely on a variety of heuristics or relaxations of the problem to develop algorithms that efficiently design DC libraries.…”

Section: Introductionmentioning

confidence: 99%

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DeCoDe: degenerate codon design for complete protein-coding DNA libraries

Shimko

Fordyce

Orenstein

2019

Preprint

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Motivation: High-throughput protein screening is a critical technique for dissecting and designing protein function. Libraries for these assays can be created through a number of means, including targeted or random mutagenesis of a template protein sequence or direct DNA synthesis. However, mutagenic library construction methods often yield vastly more non-functional than functional variants and, despite advances in large-scale DNA synthesis, individual synthesis of each desired DNA template is often prohibitively expensive. Consequently, many protein screening libraries rely on the use of degenerate codons (DCs), mixtures of DNA bases incorporated at specific positions during DNA synthesis, to generate highly diverse protein variant pools from only a few low-cost synthesis reactions. However, selecting DCs for sets of sequences that covary at multiple positions dramatically increases the difficulty of designing a DC library and leads to the creation of many undesired variants that can quickly outstrip screening capacity. Results:We introduce a novel algorithm for total DC library optimization, DeCoDe, based on integer linear programming. DeCoDe significantly outperforms state-of-the-art DC optimization algorithms and scales well to more than a hundred proteins sharing complex patterns of covariation (e.g. the lab-derived avGFP lineage). Moreover, DeCoDe is, to our knowledge, the first DC design algorithm with the capability to encode mixed-length protein libraries. We anticipate DeCoDe to be broadly useful for a variety of library generation problems, ranging from protein engineering attempts that leverage mutual information to the reconstruction of ancestral protein states.

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Optimization of Combinatorial Mutagenesis

Cited by 10 publications

References 40 publications

Structure-Guided Deimmunization of Therapeutic Proteins

Structure-Guided Deimmunization of Therapeutic Proteins

Computationally optimized deimmunization libraries yield highly mutated enzymes with low immunogenicity and enhanced activity

DeCoDe: degenerate codon design for complete protein-coding DNA libraries

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