Evidence map›Paper›PMID 27914063›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2017

EpiSweep: Computationally Driven Reengineering of Therapeutic Proteins to Reduce Immunogenicity While Maintaining Function.

Yoonjoo Choi, Deeptak Verma, Karl E Griswold, Chris Bailey-Kellogg

Open access · greenAbstract read
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
3.8field-weighted citation impact, top 6% of its field
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

19 citing papers in PubMed, 40 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. How can we discover developable antibody-based biotherapeutics?Frontiers in molecular biosciences · 2023
    Review
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. 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
  18. Pareto Optimization of Combinatorial Mutagenesis Libraries.IEEE/ACM transactions on computational biology and bioinformatics
    Article
  19. Review
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

4 authors at 1 institution in 1 country.

Yoonjoo ChoiDepartment of Computer Science, Dartmouth, Hanover, NH, USA.
Deeptak VermaDepartment of Computer Science, Dartmouth, Hanover, NH, USA.
Karl E GriswoldThayer School of Engineering, Dartmouth, Hanover, NH, USA.
Chris Bailey-KelloggDepartment of Computer Science, Dartmouth, Hanover, NH, USA. cbk@cs.dartmouth.edu.
Dartmouth College · US

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

Therapeutic proteins are yielding ever more advanced and efficacious new drugs, but the biological origins of these highly effective therapeutics render them subject to immune surveillance within the patient's body. When recognized by the immune system as a foreign agent, protein drugs elicit a coordinated response that can manifest a range of clinical complications including rapid drug clearance, loss of functionality and efficacy, delayed infusion-like allergic reactions, more serious anaphylactic shock, and even induced auto-immunity. It is thus often necessary to deimmunize an exogenous protein in order to enable its clinical application; critically, the deimmunization process must also maintain the desired therapeutic activity.To meet the growing need for effective, efficient, and broadly applicable protein deimmunization technologies, we have developed the EpiSweep suite of protein design algorithms. EpiSweep seamlessly integrates computational prediction of immunogenic T cell epitopes with sequence- or structure-based assessment of the impacts of mutations on protein stability and function, in order to select combinations of mutations that make Pareto optimal trade-offs between the competing goals of low immunogenicity and high-level function. The methods are applicable both to the design of individual functionally deimmunized variants as well as the design of combinatorial libraries enriched in functionally deimmunized variants. After validating EpiSweep in a series of retrospective case studies providing comparisons to conventional approaches to T cell epitope deletion, we have experimentally demonstrated it to be highly effective in prospective application to deimmunization of a number of different therapeutic candidates. We conclude that our broadly applicable computational protein design algorithms guide the engineer towards the most promising deimmunized therapeutic candidates, and thereby have the potential to accelerate development of new protein drugs by shortening time frames and improving hit rates.

Indexed as

ProteinsSoftwareComputational BiologyComputer SimulationDatabases, GeneticEpitopes, T-LymphocyteModels, MolecularProtein ConformationProtein EngineeringWeb BrowserWorkflowEpitopes, T-LymphocyteProteinsBiologicsCombinatorial libraryComputational protein designDeimmunizationImmunogenicityPareto optimizationProtein engineeringT cell epitopeTherapeutic proteins

Identifiers

PMID27914063
PMCPMC5142831
OpenAlexW2558577646

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.