Literature DB >> 27914063

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

Yoonjoo Choi1, Deeptak Verma1, Karl E Griswold2, Chris Bailey-Kellogg3.   

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.

Entities:  

Keywords:  Biologics; Combinatorial library; Computational protein design; Deimmunization; Immunogenicity; Pareto optimization; Protein engineering; T cell epitope; Therapeutic proteins

Mesh:

Substances:

Year:  2017        PMID: 27914063      PMCID: PMC5142831          DOI: 10.1007/978-1-4939-6637-0_20

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  36 in total

Review 1.  Immunogenicity of protein therapeutics.

Authors:  Anne S De Groot; David W Scott
Journal:  Trends Immunol       Date:  2007-10-25       Impact factor: 16.687

2.  Depletion of T cell epitopes in lysostaphin mitigates anti-drug antibody response and enhances antibacterial efficacy in vivo.

Authors:  Hongliang Zhao; Deeptak Verma; Wen Li; Yoonjoo Choi; Christian Ndong; Steven N Fiering; Chris Bailey-Kellogg; Karl E Griswold
Journal:  Chem Biol       Date:  2015-05-21

3.  Optimization of therapeutic proteins to delete T-cell epitopes while maintaining beneficial residue interactions.

Authors:  Andrew S Parker; Karl E Griswold; Chris Bailey-Kellogg
Journal:  J Bioinform Comput Biol       Date:  2011-04       Impact factor: 1.122

4.  Structure-guided deimmunization of therapeutic proteins.

Authors:  Andrew S Parker; Yoonjoo Choi; Karl E Griswold; Chris Bailey-Kellogg
Journal:  J Comput Biol       Date:  2013-02       Impact factor: 1.479

5.  A divide-and-conquer approach to determine the Pareto frontier for optimization of protein engineering experiments.

Authors:  Lu He; Alan M Friedman; Chris Bailey-Kellogg
Journal:  Proteins       Date:  2011-12-16

6.  Protein deimmunization via structure-based design enables efficient epitope deletion at high mutational loads.

Authors:  Regina S Salvat; Yoonjoo Choi; Alexandra Bishop; Chris Bailey-Kellogg; Karl E Griswold
Journal:  Biotechnol Bioeng       Date:  2015-02-23       Impact factor: 4.530

7.  Computationally driven deletion of broadly distributed T cell epitopes in a biotherapeutic candidate.

Authors:  Regina S Salvat; Andrew S Parker; Andrew Guilliams; Yoonjoo Choi; Chris Bailey-Kellogg; Karl E Griswold
Journal:  Cell Mol Life Sci       Date:  2014-06-01       Impact factor: 9.261

8.  Removing T-cell epitopes with computational protein design.

Authors:  Chris King; Esteban N Garza; Ronit Mazor; Jonathan L Linehan; Ira Pastan; Marion Pepper; David Baker
Journal:  Proc Natl Acad Sci U S A       Date:  2014-05-19       Impact factor: 12.779

9.  Recombinant immunotoxin for cancer treatment with low immunogenicity by identification and silencing of human T-cell epitopes.

Authors:  Ronit Mazor; Jaime A Eberle; Xiaobo Hu; Aaron N Vassall; Masanori Onda; Richard Beers; Elizabeth C Lee; Robert J Kreitman; Byungkook Lee; David Baker; Chris King; Raffit Hassan; Itai Benhar; Ira Pastan
Journal:  Proc Natl Acad Sci U S A       Date:  2014-05-05       Impact factor: 12.779

10.  Mapping the Pareto optimal design space for a functionally deimmunized biotherapeutic candidate.

Authors:  Regina S Salvat; Andrew S Parker; Yoonjoo Choi; Chris Bailey-Kellogg; Karl E Griswold
Journal:  PLoS Comput Biol       Date:  2015-01-08       Impact factor: 4.475

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  8 in total

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

Authors:  Regina S Salvat; Deeptak Verma; Andrew S Parker; Jack R Kirsch; Seth A Brooks; Chris Bailey-Kellogg; Karl E Griswold
Journal:  Proc Natl Acad Sci U S A       Date:  2017-06-12       Impact factor: 11.205

2.  Pareto Optimization of Combinatorial Mutagenesis Libraries.

Authors:  Deeptak Verma; Gevorg Grigoryan; Chris Bailey-Kellogg
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2018-07-23       Impact factor: 3.710

3.  Development of a strategy and computational application to select candidate protein analogues with reduced HLA binding and immunogenicity.

Authors:  Sandeep Kumar Dhanda; Alba Grifoni; John Pham; Kerrie Vaughan; John Sidney; Bjoern Peters; Alessandro Sette
Journal:  Immunology       Date:  2017-09-28       Impact factor: 7.397

4.  Design of peptides with high affinity binding to a monoclonal antibody as a basis for immunotherapy.

Authors:  Surendra S Negi; Randall M Goldblum; Werner Braun; Terumi Midoro-Horiuti
Journal:  Peptides       Date:  2021-08-16       Impact factor: 3.750

5.  MHCEpitopeEnergy, a Flexible Rosetta-Based Biotherapeutic Deimmunization Platform.

Authors:  Brahm J Yachnin; Vikram Khipple Mulligan; Sagar D Khare; Chris Bailey-Kellogg
Journal:  J Chem Inf Model       Date:  2021-04-26       Impact factor: 6.162

6.  Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space.

Authors:  Emily K Makowski; Patrick C Kinnunen; Jie Huang; Lina Wu; Matthew D Smith; Tiexin Wang; Alec A Desai; Craig N Streu; Yulei Zhang; Jennifer M Zupancic; John S Schardt; Jennifer J Linderman; Peter M Tessier
Journal:  Nat Commun       Date:  2022-07-01       Impact factor: 17.694

7.  Computationally-driven identification of antibody epitopes.

Authors:  Casey K Hua; Albert T Gacerez; Charles L Sentman; Margaret E Ackerman; Yoonjoo Choi; Chris Bailey-Kellogg
Journal:  Elife       Date:  2017-12-04       Impact factor: 8.140

Review 8.  Discovery-stage identification of drug-like antibodies using emerging experimental and computational methods.

Authors:  Emily K Makowski; Lina Wu; Priyanka Gupta; Peter M Tessier
Journal:  MAbs       Date:  2021 Jan-Dec       Impact factor: 5.857

  8 in total

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