ArticleProteins2012
A divide-and-conquer approach to determine the Pareto frontier for optimization of protein engineering experiments.
Article in Proteins, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 37 citations in OpenAlex.
- Resistor: An algorithm for predicting resistance mutations via Pareto optimization over multistate protein design and mutational signatures.Cell systems · 2022Article
- Deimmunization of protein therapeutics - Recent advances in experimental and computational epitope prediction and deletion.Computational and structural biotechnology journal · 2021Review
- DisruPPI: structure-based computational redesign algorithm for protein binding disruption.Bioinformatics (Oxford, England) · 2018Article
- A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters.Nature chemical biology · 2018Article
- Article
- Searching for the Pareto frontier in multi-objective protein design.Biophysical reviews · 2017Review
- Computationally optimized deimmunization libraries yield highly mutated enzymes with low immunogenicity and enhanced activity.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- EpiSweep: Computationally Driven Reengineering of Therapeutic Proteins to Reduce Immunogenicity While Maintaining Function.Methods in molecular biology (Clifton, N.J.) · 2017Article
- Computationally driven antibody engineering enables simultaneous humanization and thermostabilization.Protein engineering, design & selection : PEDS · 2016Article
- Design and engineering of deimmunized biotherapeutics.Current opinion in structural biology · 2016Review
- Protein deimmunization via structure-based design enables efficient epitope deletion at high mutational loads.Biotechnology and bioengineering · 2015Article
- Depletion of T cell epitopes in lysostaphin mitigates anti-drug antibody response and enhances antibacterial efficacy in vivo.Chemistry & biology · 2015Article
- Structure-based design of combinatorial mutagenesis libraries.Protein science : a publication of the Protein Society · 2015Article
- Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently.Chemical Society reviews · 2015Review
- Article
- Structure-based redesign of lysostaphin yields potent antistaphylococcal enzymes that evade immune cell surveillance.Molecular therapy. Methods & clinical development · 2015Article
- Mapping the Pareto optimal design space for a functionally deimmunized biotherapeutic candidate.PLoS computational biology · 2015Article
- Structure-based redesign of proteins for minimal T-cell epitope content.Journal of computational chemistry · 2013Article
- Structure-guided deimmunization of therapeutic proteins.Journal of computational biology : a journal of computational molecular cell biology · 2013Article
- Algorithms for optimizing cross-overs in DNA shuffling.BMC bioinformatics · 2012Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
In developing improved protein variants by site-directed mutagenesis or recombination, there are often competing objectives that must be considered in designing an experiment (selecting mutations or breakpoints): stability versus novelty, affinity versus specificity, activity versus immunogenicity, and so forth. Pareto optimal experimental designs make the best trade-offs between competing objectives. Such designs are not "dominated"; that is, no other design is better than a Pareto optimal design for one objective without being worse for another objective. Our goal is to produce all the Pareto optimal designs (the Pareto frontier), to characterize the trade-offs and suggest designs most worth considering, but to avoid explicitly considering the large number of dominated designs. To do so, we develop a divide-and-conquer algorithm, Protein Engineering Pareto FRontier (PEPFR), that hierarchically subdivides the objective space, using appropriate dynamic programming or integer programming methods to optimize designs in different regions. This divide-and-conquer approach is efficient in that the number of divisions (and thus calls to the optimizer) is directly proportional to the number of Pareto optimal designs. We demonstrate PEPFR with three protein engineering case studies: site-directed recombination for stability and diversity via dynamic programming, site-directed mutagenesis of interacting proteins for affinity and specificity via integer programming, and site-directed mutagenesis of a therapeutic protein for activity and immunogenicity via integer programming. We show that PEPFR is able to effectively produce all the Pareto optimal designs, discovering many more designs than previous methods. The characterization of the Pareto frontier provides additional insights into the local stability of design choices as well as global trends leading to trade-offs between competing criteria.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.