MHCEpitopeEnergy, a Flexible Rosetta-Based Biotherapeutic Deimmunization Platform

As non-"self" macromolecules, biotherapeutics can trigger an immune response that can reduce drug efficacy, require patients to be taken off therapy, or even cause life-threatening reactions. To enable the flexible and facile design of protein biotherapeutics while reducing the prevalence of T-cell epitopes that drive immune recognition, we have integrated into the Rosetta protein design suite a new scoring term that allows design protocols to account for predicted or experimentally identified epitopes in the optimized objective function. This flexible scoring term can be used in any Rosetta design trajectory, can be targeted to specific regions of a protein, and can be readily extended to work with a variety of epitope predictors. By performing extensive design runs with varied design parameter choices for three case study proteins as well as a larger diverse benchmark, we show that the incorporation of this scoring term enables the effective exploration of an alternative, deimmunized sequence space to discover diverse proteins that are potentially highly deimmunized while retaining physical and chemical qualities similar to those yielded by equivalent nondeimmunizing sequence design protocols.

[1]  D. Baker,et al.  RosettaHoles: Rapid assessment of protein core packing for structure prediction, refinement, design, and validation , 2008, Protein science : a publication of the Protein Society.

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

[3]  J. Chamberlain,et al.  Cas9 immunity creates challenges for CRISPR gene editing therapies , 2018, Nature Communications.

[4]  L. Swint-Kruse.  Using Evolution to Guide Protein Engineering: The Devil IS in the Details. , 2016, Biophysical journal.

[5]  Ramgopal R. Mettu,et al.  CD4+ T-cell epitope prediction using antigen processing constraints. , 2016, Journal of immunological methods.

[6]  J. Greenbaum,et al.  Improved methods for predicting peptide binding affinity to MHC class II molecules , 2018, Immunology.

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

[8]  Robert M. Stroud,et al.  Efficiency of signalling through cytokine receptors depends critically on receptor orientation , 1998, Nature.

[9]  Jens Meiler,et al.  RosettaScripts: A Scripting Language Interface to the Rosetta Macromolecular Modeling Suite , 2011, PloS one.

[10]  Chris Bailey-Kellogg,et al.  Computationally optimized deimmunization libraries yield highly mutated enzymes with low immunogenicity and enhanced activity , 2017, Proceedings of the National Academy of Sciences.

[11]  Bjoern Peters,et al.  Functional classification of class II human leukocyte antigen (HLA) molecules reveals seven different supertypes and a surprising degree of repertoire sharing across supertypes , 2011, Immunogenetics.

[12]  Bjoern Peters,et al.  Molecular Determinants of T Cell Epitope Recognition to the Common Timothy Grass Allergen , 2010, The Journal of Immunology.

[13]  Russ B. Altman,et al.  Predicting HLA class II antigen presentation through integrated deep learning , 2019, Nature Biotechnology.

[14]  Thomas L. Madden,et al.  Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements. , 2001, Nucleic acids research.

[15]  Sergey Lyskov,et al.  PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta , 2010, Bioinform..

[16]  M. Nielsen,et al.  Structural Properties of MHC Class II Ligands, Implications for the Prediction of MHC Class II Epitopes , 2010, PloS one.

[17]  C. D. Ranter,et al.  Three-dimensional structure of staphylokinase, a plasminogen activator with therapeutic potential , 1997, Nature Structural Biology.

[18]  Frans Van de Werf,et al.  An international randomized trial comparing four thrombolytic strategies for acute myocardial infarction. , 1993, The New England journal of medicine.

[19]  T. Terwilliger,et al.  Engineering and characterization of a superfolder green fluorescent protein , 2006, Nature Biotechnology.

[20]  Zuben E Sauna,et al.  Evaluating and Mitigating the Immunogenicity of Therapeutic Proteins. , 2018, Trends in biotechnology.

[21]  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.

[22]  Timothy A. Whitehead,et al.  Computational Design of Proteins Targeting the Conserved Stem Region of Influenza Hemagglutinin , 2011, Science.

[23]  M. Weisman,et al.  Adalimumab, a fully human anti-tumor necrosis factor alpha monoclonal antibody, for the treatment of rheumatoid arthritis in patients taking concomitant methotrexate: the ARMADA trial. , 2003, Arthritis and rheumatism.

[24]  C. Bailey-Kellogg,et al.  Globally deimmunized lysostaphin evades human immune surveillance and enables highly efficacious repeat dosing , 2020, Science Advances.

[25]  Jens Keilwagen,et al.  PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R , 2015, Bioinform..

[26]  William S. Lane,et al.  Predominant naturally processed peptides bound to HLA-DR1 are derived from MHC-related molecules and are heterogeneous in size , 1992, Nature.

[27]  Lindong Weng,et al.  Droplet Microfluidics-Enabled High-Throughput Screening for Protein Engineering , 2019, Micromachines.

[28]  Jaime Prilusky,et al.  Automated Structure- and Sequence-Based Design of Proteins for High Bacterial Expression and Stability , 2016, Molecular cell.

[29]  Karl E Griswold,et al.  EpiSweep: Computationally Driven Reengineering of Therapeutic Proteins to Reduce Immunogenicity While Maintaining Function. , 2017, Methods in molecular biology.

[30]  A. Schwan,et al.  Exotoxin A–eEF2 complex structure indicates ADP ribosylation by ribosome mimicry , 2005, Nature.

[31]  A. Rudensky,et al.  Sequence analysis of peptides bound to MHC class II molecules , 1991, Nature.

[32]  E. Topol,et al.  Bivalirudin and provisional glycoprotein IIb/IIIa blockade compared with heparin and planned glycoprotein IIb/IIIa blockade during percutaneous coronary intervention: REPLACE-2 randomized trial. , 2003, JAMA.

[33]  Roland L. Dunbrack,et al.  The Rosetta all-atom energy function for macromolecular modeling and design , 2017, bioRxiv.

[34]  M. Pirmohamed,et al.  Immunogenicity to Biologics: Mechanisms, Prediction and Reduction , 2012, Archivum Immunologiae et Therapiae Experimentalis.

[35]  G. S. Pandey,et al.  Post hoc assessment of the immunogenicity of bioengineered factor VIIa demonstrates the use of preclinical tools , 2017, Science Translational Medicine.

[36]  John Sidney,et al.  Rationally Engineered Therapeutic Proteins with Reduced Immunogenicity , 2005, The Journal of Immunology.

[37]  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.

[38]  Jason W. Labonte,et al.  Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks , 2021, Nature Communications.

[39]  David E. Golan,et al.  Protein therapeutics: a summary and pharmacological classification , 2008, Nature Reviews Drug Discovery.

[40]  Todd M. Allen,et al.  Humanized immune system mouse models: progress, challenges and opportunities , 2019, Nature Immunology.

[41]  Development and validation of a broad scheme for prediction of HLA class II restricted T cell epitopes. , 2015, Journal of immunological methods.

[42]  George Coukos,et al.  Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes , 2019, Nature Biotechnology.

[43]  Alessandro Sette,et al.  The Immune Epitope Database (IEDB): 2018 update , 2018, Nucleic Acids Res..

[44]  Z. Sauna,et al.  Mitigation of T-cell dependent immunogenicity by reengineering factor VIIa analogue. , 2019, Blood advances.

[45]  Xavier Robin,et al.  pROC: an open-source package for R and S+ to analyze and compare ROC curves , 2011, BMC Bioinformatics.

[46]  David Baker,et al.  Comprehensive computational design of ordered peptide macrocycles , 2017, Science.

[47]  Andrew D Ellington,et al.  Synthetic DNA Synthesis and Assembly: Putting the Synthetic in Synthetic Biology. , 2017, Cold Spring Harbor perspectives in biology.

[48]  Brian D. Weitzner,et al.  Macromolecular modeling and design in Rosetta: recent methods and frameworks , 2020, Nature Methods.

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

[50]  Zuben E. Sauna,et al.  Recent advances in (therapeutic protein) drug development , 2017, F1000Research.

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

[52]  Jason W. Labonte,et al.  Computationally designed peptide macrocycle inhibitors of New Delhi metallo-β-lactamase 1 , 2021, Proceedings of the National Academy of Sciences.

[53]  D. Baker,et al.  Global analysis of protein folding using massively parallel design, synthesis, and testing , 2017, Science.