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

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.

[1]  Chris Bailey-Kellogg,et al.  Structure‐based design of combinatorial mutagenesis libraries , 2015, Protein science : a publication of the Protein Society.

[2]  Anne S De Groot,et al.  Immunogenicity of protein therapeutics. , 2007, Trends in immunology.

[3]  Marc De Maeyer,et al.  Elimination of a Human T-cell Region in Staphylokinase by T-cell Screening and Computer Modeling , 2002, Thrombosis and Haemostasis.

[4]  Huub Schellekens,et al.  Bioequivalence and the immunogenicity of biopharmaceuticals , 2002, Nature Reviews Drug Discovery.

[5]  Saurabh Aggarwal,et al.  What's fueling the biotech engine—2012 to 2013 , 2014, Nature Biotechnology.

[6]  Chris Bailey-Kellogg,et al.  Optimization of Combinatorial Mutagenesis , 2011, RECOMB.

[7]  Amy C. Anderson,et al.  Computational structure-based redesign of enzyme activity , 2009, Proceedings of the National Academy of Sciences.

[8]  Wolfgang Aehle,et al.  A β-lactamase with reduced immunogenicity for the targeted delivery of chemotherapeutics using antibody-directed enzyme prodrug therapy , 2005, Molecular Cancer Therapeutics.

[9]  Gajendra P. S. Raghava,et al.  ProPred: prediction of HLA-DR binding sites , 2001, Bioinform..

[10]  Chris Bailey-Kellogg,et al.  Computationally driven deletion of broadly distributed T cell epitopes in a biotherapeutic candidate , 2014, Cellular and Molecular Life Sciences.

[11]  David Baker,et al.  Protein Structure Prediction Using Rosetta , 2004, Numerical Computer Methods, Part D.

[12]  Regina S. Salvat,et al.  Structure-based redesign of lysostaphin yields potent antistaphylococcal enzymes that evade immune cell surveillance , 2015, Molecular therapy. Methods & clinical development.

[13]  Leonard Moise,et al.  Effect of HLA DR epitope de-immunization of Factor VIII in vitro and in vivo. , 2012, Clinical immunology.

[14]  D. Baker,et al.  Recombinant immunotoxin for cancer treatment with low immunogenicity by identification and silencing of human T-cell epitopes , 2014, Proceedings of the National Academy of Sciences.

[15]  Chris Bailey-Kellogg,et al.  Structure‐based redesign of proteins for minimal T‐cell epitope content , 2013, J. Comput. Chem..

[16]  Chris Bailey-Kellogg,et al.  Protein deimmunization via structure‐based design enables efficient epitope deletion at high mutational loads , 2015, Biotechnology and bioengineering.

[17]  George Georgiou,et al.  Therapeutic enzyme deimmunization by combinatorial T-cell epitope removal using neutral drift , 2011, Proceedings of the National Academy of Sciences.

[18]  W. Delano The PyMOL Molecular Graphics System , 2002 .

[19]  Bruce Randall Donald,et al.  Protein Design Using Continuous Rotamers , 2012, PLoS Comput. Biol..

[20]  Fei Zhou,et al.  Ultra-Fast Evaluation of Protein Energies Directly from Sequence , 2006, PLoS Comput. Biol..

[21]  David Baker,et al.  Removing T-cell epitopes with computational protein design , 2014, Proceedings of the National Academy of Sciences.

[22]  J. Ponder,et al.  An efficient newton‐like method for molecular mechanics energy minimization of large molecules , 1987 .

[23]  Chris Bailey-Kellogg,et al.  Structure-Guided Deimmunization of Therapeutic Proteins , 2012, RECOMB.

[24]  Chris Bailey-Kellogg,et al.  Design and analysis of immune-evading enzymes for ADEPT therapy. , 2012, Protein engineering, design & selection : PEDS.

[25]  P Argos,et al.  Oligopeptide biases in protein sequences and their use in predicting protein coding regions in nucleotide sequences , 1988, Proteins.

[26]  J. Cizeau,et al.  Engineering and Biological Characterization of VB6-845, an Anti-EpCAM Immunotoxin Containing a T-cell Epitope-depleted Variant of the Plant Toxin Bouganin , 2009, Journal of immunotherapy.

[27]  Chris Bailey-Kellogg,et al.  Gene and Protein Sequence Optimization for High-Level Production of Fully Active and Aglycosylated Lysostaphin in Pichia pastoris , 2014, Applied and Environmental Microbiology.

[28]  Stefan M. Larson,et al.  Analysis of covariation in an SH3 domain sequence alignment: applications in tertiary contact prediction and the design of compensating hydrophobic core substitutions. , 2000, Journal of molecular biology.

[29]  Leonard Moise,et al.  Prediction of immunogenicity for therapeutic proteins: state of the art. , 2007, Current opinion in drug discovery & development.

[30]  Gevorg Grigoryan,et al.  Design of protein-interaction specificity affords selective bZIP-binding peptides , 2009, Nature.

[31]  Chris Bailey-Kellogg,et al.  Optimization of Therapeutic proteins to Delete T-Cell epitopes while Maintaining Beneficial Residue Interactions , 2011, J. Bioinform. Comput. Biol..

[32]  Chris Bailey-Kellogg,et al.  Depletion of T cell epitopes in lysostaphin mitigates anti-drug antibody response and enhances antibacterial efficacy in vivo. , 2015, Chemistry & biology.

[33]  Chris Bailey-Kellogg,et al.  A divide‐and‐conquer approach to determine the Pareto frontier for optimization of protein engineering experiments , 2012, Proteins.

[34]  W. Martin,et al.  De-immunization of therapeutic proteins by T-cell epitope modification. , 2005, Developments in biologicals.

[35]  Chris Bailey-Kellogg,et al.  Mapping the Pareto Optimal Design Space for a Functionally Deimmunized Biotherapeutic Candidate , 2015, PLoS Comput. Biol..

[36]  E. Mylonakis,et al.  Lysostaphin: a silver bullet for staph. , 2012 .

[37]  C Kooperberg,et al.  Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. , 1997, Journal of molecular biology.

[38]  Peter A. Kollman,et al.  AMBER: Assisted model building with energy refinement. A general program for modeling molecules and their interactions , 1981 .

[39]  Chris Bailey-Kellogg,et al.  Open Access Methodology Article Optimization Algorithms for Functional Deimmunization of Therapeutic Proteins , 2022 .

[40]  M F del Guercio,et al.  Several common HLA-DR types share largely overlapping peptide binding repertoires. , 1998, Journal of immunology.