Evidence map›Paper›PMID 21923411›Full record

ArticleJournal of computational biology : a journal of computational molecular cell biology2011

Optimization of combinatorial mutagenesis.

Andrew S Parker, Karl E Griswold, Chris Bailey-Kellogg

Abstract read
In one paragraph

Article in Journal of computational biology : a journal of computational molecular cell biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Review
  6. Article
  7. 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 · 2017
    Article
  8. Article
  9. Article
  10. Algorithms for protein design.Current opinion in structural biology · 2016
    Review
  11. Structure-based design of combinatorial mutagenesis libraries.Protein science : a publication of the Protein Society · 2015
    Article
  12. Article
  13. Structure-guided deimmunization of therapeutic proteins.Journal of computational biology : a journal of computational molecular cell biology · 2013
    Article
  14. Article
  15. Review
  16. Pareto Optimization of Combinatorial Mutagenesis Libraries.IEEE/ACM transactions on computational biology and bioinformatics
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Andrew S ParkerDepartment of Computer Science, Dartmouth College, Hanover, New Hampshire, USA.
Karl E Griswold
Chris Bailey-Kellogg

Funding

Functional Deimmunization of Therapeutic ProteinsR01GM098977 · NIGMS · DARTMOUTH COLLEGE · PI BAILEY-KELLOGG, CHRIS, GRISWOLD, KARL E · 2011 to 2019
$3.4M
NIGMS NIH HHS R01 GM098977
6 · The paper itself

Abstract

Protein engineering by combinatorial site-directed mutagenesis evaluates a portion of the sequence space near a target protein, seeking variants with improved properties (e.g., stability, activity, immunogenicity). In order to improve the hit-rate of beneficial variants in such mutagenesis libraries, we develop methods to select optimal positions and corresponding sets of the mutations that will be used, in all combinations, in constructing a library for experimental evaluation. Our approach, OCoM (Optimization of Combinatorial Mutagenesis), encompasses both degenerate oligonucleotides and specified point mutations, and can be directed accordingly by requirements of experimental cost and library size. It evaluates the quality of the resulting library by one- and two-body sequence potentials, averaged over the variants. To ensure that it is not simply recapitulating extant sequences, it balances the quality of a library with an explicit evaluation of the novelty of its members. We show that, despite dealing with a combinatorial set of variants, in our approach the resulting library optimization problem is actually isomorphic to single-variant optimization. By the same token, this means that the two-body sequence potential results in an NP-hard optimization problem. We present an efficient dynamic programming algorithm for the one-body case and a practically-efficient integer programming approach for the general two-body case. We demonstrate the effectiveness of our approach in designing libraries for three different case study proteins targeted by previous combinatorial libraries--a green fluorescent protein, a cytochrome P450, and a beta lactamase. We found that OCoM worked quite efficiently in practice, requiring only 1 hour even for the massive design problem of selecting 18 mutations to generate 10⁷ variants of a 443-residue P450. We demonstrate the general ability of OCoM in enabling the protein engineer to explore and evaluate trade-offs between quality and novelty as well as library construction technique, and identify optimal libraries for experimental evaluation.

Indexed as

Mutagenesis, Site-DirectedAlgorithmsAmino Acid SequenceAmino Acid SubstitutionAnimalsBacterial Proteinsbeta-LactamasesCytochrome P-450 Enzyme SystemGene LibraryGreen Fluorescent ProteinsHydrozoaModels, GeneticMolecular Sequence DataMutationSequence Analysis, ProteinBacterial Proteinsbeta-LactamasesCytochrome P-450 Enzyme SystemGreen Fluorescent Proteins

Identifiers

PMID21923411
PMCPMC5220575

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Registered trials

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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.