title: EpiSweep: Computationally Driven Reengineering of Therapeutic Proteins to Reduce Immunogenicity While Maintaining Function.
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.
authors.affiliation: Department of Computer Science, Dartmouth, Hanover, NH, USA.
authors.forename: Yoonjoo
authors.initials: Y
authors.lastname: Choi
authors.affiliation: Department of Computer Science, Dartmouth, Hanover, NH, USA.
authors.forename: Deeptak
authors.initials: D
authors.lastname: Verma
authors.affiliation: Thayer School of Engineering, Dartmouth, Hanover, NH, USA.
authors.forename: Karl E
authors.initials: KE
authors.lastname: Griswold
authors.affiliation: Department of Computer Science, Dartmouth, Hanover, NH, USA. cbk@cs.dartmouth.edu.
authors.forename: Chris
authors.initials: C
authors.lastname: Bailey-Kellogg
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030541:Databases, Genetic; D018984:Epitopes, T-Lymphocyte / Q000737:chemistry / Q000235:genetics / Q000276:immunology; D008958:Models, Molecular; D011487:Protein Conformation; D015202:Protein Engineering / Q000379:methods*; D011506:Proteins / Q000235:genetics / Q000276:immunology / Q000494:pharmacology / Q000627:therapeutic use; D012984:Software; D064878:Web Browser; D057188:Workflow
chemical_list: D018984:Epitopes, T-Lymphocyte; D011506:Proteins
keywords: Biologics; Combinatorial library; Computational protein design; Deimmunization; Immunogenicity; Pareto optimization; Protein engineering; T cell epitope; Therapeutic proteins