Protein deimmunization via structure‐based design enables efficient epitope deletion at high mutational loads

Anti‐drug immune responses are a unique risk factor for biotherapeutics, and undesired immunogenicity can alter pharmacokinetics, compromise drug efficacy, and in some cases even threaten patient safety. To fully capitalize on the promise of biotherapeutics, more efficient and generally applicable protein deimmunization tools are needed. Mutagenic deletion of a protein's T cell epitopes is one powerful strategy to engineer immunotolerance, but deimmunizing mutations must maintain protein structure and function. Here, EpiSweep, a structure‐based protein design and deimmunization algorithm, has been used to produce a panel of seven beta‐lactamase drug candidates having 27–47% reductions in predicted epitope content. Despite bearing eight mutations each, all seven engineered enzymes maintained good stability and activity. At the same time, the variants exhibited dramatically reduced interaction with human class II major histocompatibility complex proteins, key regulators of anti‐drug immune responses. When compared to 8‐mutation designs generated with a sequence‐based deimmunization algorithm, the structure‐based designs retained greater thermostability and possessed fewer high affinity epitopes, the dominant drivers of anti‐biotherapeutic immune responses. These experimental results validate the first structure‐based deimmunization algorithm capable of mapping optimal biotherapeutic design space. By designing optimal mutations that reduce immunogenic potential while imparting favorable intramolecular interactions, broadly distributed epitopes may be simultaneously targeted using high mutational loads. Biotechnol. Bioeng. 2015;112: 1306–1318. © 2015 Wiley Periodicals, Inc.

[1]  Ronit Mazor,et al.  Identification and elimination of an immunodominant T-cell epitope in recombinant immunotoxins based on Pseudomonas exotoxin A , 2012, Proceedings of the National Academy of Sciences.

[2]  Bruce Tidor,et al.  Progress in computational protein design. , 2007, Current opinion in biotechnology.

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

[4]  Bjoern Peters,et al.  Evaluating the Immunogenicity of Protein Drugs by Applying In Vitro MHC Binding Data and the Immune Epitope Database and Analysis Resource , 2013, Clinical & developmental immunology.

[5]  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.

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

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

[8]  Maria D. F. S. Barbosa,et al.  Immunogenicity of biotherapeutics in the context of developing biosimilars and biobetters. , 2011, Drug discovery today.

[9]  L. Holm,et al.  The Pfam protein families database , 2005, Nucleic Acids Res..

[10]  Morten Nielsen,et al.  Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method , 2007, BMC Bioinformatics.

[11]  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.

[12]  A. Jevnikar,et al.  The relationship between predicted peptide–MHC class II affinity and T-cell activation in a HLA-DRβ1*0401 transgenic mouse model , 2002, Arthritis research & therapy.

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

[14]  Fiona Adair,et al.  The immunogenicity of therapeutic proteins , 2002 .

[15]  U. Şahin,et al.  Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices , 1999, Nature Biotechnology.

[16]  Ira Mellman,et al.  Cell biology of antigen processing in vitro and in vivo. , 2005, Annual review of immunology.

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

[18]  Chris Bailey-Kellogg,et al.  A high throughput MHC II binding assay for quantitative analysis of peptide epitopes. , 2014, Journal of visualized experiments : JoVE.

[19]  W. C. Still,et al.  Semianalytical treatment of solvation for molecular mechanics and dynamics , 1990 .

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

[21]  V. Hornak,et al.  Comparison of multiple Amber force fields and development of improved protein backbone parameters , 2006, Proteins.

[22]  Morten Nielsen,et al.  MHC Class II epitope predictive algorithms , 2010, Immunology.

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

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

[25]  John Sidney,et al.  The HLA Molecules DQA1*0501/B1*0201 and DQA1*0301/B1*0302 Share an Extensive Overlap in Peptide Binding Specificity1 , 2002, The Journal of Immunology.

[26]  William W. Kwok,et al.  Antibiotic-refractory Lyme arthritis is associated with HLA-DR molecules that bind a Borrelia burgdorferi peptide , 2006, The Journal of experimental medicine.

[27]  Marcia Stickler,et al.  Elimination of an Immunodominant CD4+ T Cell Epitope in Human IFN-β Does Not Result in an In Vivo Response Directed at the Subdominant Epitope , 2004, The Journal of Immunology.

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

[29]  John Sidney,et al.  A Systematic Assessment of MHC Class II Peptide Binding Predictions and Evaluation of a Consensus Approach , 2008, PLoS Comput. Biol..

[30]  Christine J. Bryson,et al.  Immunogenicity of protein therapeutics: The key causes, consequences and challenges. , 2010, Self/nonself.

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

[32]  Tim D. Jones,et al.  Prediction of Immunogenicity of Therapeutic Proteins , 2010, BioDrugs.

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

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

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

[36]  D E Banks,et al.  Immunodominance of a low-affinity major histocompatibility complex-binding myelin basic protein epitope (residues 111-129) in HLA-DR4 (B1*0401) subjects is associated with a restricted T cell receptor repertoire. , 1997, The Journal of clinical investigation.

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

[38]  F. Niesen,et al.  The use of differential scanning fluorimetry to detect ligand interactions that promote protein stability , 2007, Nature Protocols.

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

[40]  R. Dubridge,et al.  The immunogenicity of humanized and fully human antibodies , 2010, mAbs.

[41]  A. Sant,et al.  Understanding the focused CD4 T cell response to antigen and pathogenic organisms , 2009, Immunologic research.

[42]  E. Birney,et al.  Pfam: the protein families database , 2013, Nucleic Acids Res..

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

[44]  J. Richardson,et al.  The penultimate rotamer library , 2000, Proteins.

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

[46]  Peter A. Kollman,et al.  AMBER, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules , 1995 .

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

[48]  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.

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

[50]  D. Collen,et al.  Recombinant staphylokinase variants with altered immunoreactivity. IV: Identification of variants with reduced antibody induction but intact potency. , 1997, Circulation.