OptMAVEn – A New Framework for the de novo Design of Antibody Variable Region Models Targeting Specific Antigen Epitopes

Antibody-based therapeutics provides novel and efficacious treatments for a number of diseases. Traditional experimental approaches for designing therapeutic antibodies rely on raising antibodies against a target antigen in an immunized animal or directed evolution of antibodies with low affinity for the desired antigen. However, these methods remain time consuming, cannot target a specific epitope and do not lead to broad design principles informing other studies. Computational design methods can overcome some of these limitations by using biophysics models to rationally select antibody parts that maximize affinity for a target antigen epitope. This has been addressed to some extend by OptCDR for the design of complementary determining regions. Here, we extend this earlier contribution by addressing the de novo design of a model of the entire antibody variable region against a given antigen epitope while safeguarding for immunogenicity (Optimal Method for Antibody Variable region Engineering, OptMAVEn). OptMAVEn simulates in silico the in vivo steps of antibody generation and evolution, and is capable of capturing the critical structural features responsible for affinity maturation of antibodies. In addition, a humanization procedure was developed and incorporated into OptMAVEn to minimize the potential immunogenicity of the designed antibody models. As case studies, OptMAVEn was applied to design models of neutralizing antibodies targeting influenza hemagglutinin and HIV gp120. For both HA and gp120, novel computational antibody models with numerous interactions with their target epitopes were generated. The observed rates of mutations and types of amino acid changes during in silico affinity maturation are consistent with what has been observed during in vivo affinity maturation. The results demonstrate that OptMAVEn can efficiently generate diverse computational antibody models with both optimized binding affinity to antigens and reduced immunogenicity.

[1]  Dennis R Livesay,et al.  Conserved sequence and structure association motifs in antibody-protein and antibody-hapten complexes. , 2004, Protein engineering, design & selection : PEDS.

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

[3]  G. P. Smith,et al.  Filamentous fusion phage: novel expression vectors that display cloned antigens on the virion surface. , 1985, Science.

[4]  J. Whittle,et al.  Structural and genetic basis for development of broadly neutralizing influenza antibodies , 2012, Nature.

[5]  S L Morrison,et al.  Chimeric human antibody molecules: mouse antigen-binding domains with human constant region domains. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Brian D. Weitzner,et al.  Benchmarking and Analysis of Protein Docking Performance in Rosetta v3.2 , 2011, PloS one.

[7]  Woody Sherman,et al.  Affinity enhancement of an in vivo matured therapeutic antibody using structure‐based computational design , 2006, Protein science : a publication of the Protein Society.

[8]  Gisbert Schneider.  De novo Molecular Design: Schneider/De novo Molecular Design , 2013 .

[9]  Yanay Ofran,et al.  Large‐scale analysis of somatic hypermutations in antibodies reveals which structural regions, positions and amino acids are modified to improve affinity , 2014, The FEBS journal.

[10]  Bruce Tidor,et al.  Computational design of antibody-affinity improvement beyond in vivo maturation , 2007, Nature Biotechnology.

[11]  Chaim A. Schramm,et al.  Co-evolution of a broadly neutralizing HIV-1 antibody and founder virus , 2013, Nature.

[12]  J. Mascola,et al.  Broadly neutralizing antibodies and the search for an HIV-1 vaccine: the end of the beginning , 2013, Nature Reviews Immunology.

[13]  A. Jungbauer,et al.  The FLAG peptide, a versatile fusion tag for the purification of recombinant proteins. , 2001, Journal of biochemical and biophysical methods.

[14]  Dan S. Tawfik,et al.  Antibody Multispecificity Mediated by Conformational Diversity , 2003, Science.

[15]  Surender Khurana,et al.  Broadly neutralizing human antibody that recognizes the receptor-binding pocket of influenza virus hemagglutinin , 2011, Proceedings of the National Academy of Sciences.

[16]  Christian Bailly,et al.  Strategies and challenges for the next generation of therapeutic antibodies , 2010, Nature Reviews Immunology.

[17]  David Nemazee,et al.  Rational immunogen design to target specific germline B cell receptors , 2012, Retrovirology.

[18]  Tanja Kortemme,et al.  Design of Multi-Specificity in Protein Interfaces , 2007, PLoS Comput. Biol..

[19]  E. Reinherz,et al.  Sequence variability analysis of human class I and class II MHC molecules: functional and structural correlates of amino acid polymorphisms. , 2003, Journal of molecular biology.

[20]  Tongqing Zhou,et al.  Structural Basis for Broad and Potent Neutralization of HIV-1 by Antibody VRC01 , 2010, Science.

[21]  Daniele Sciretti,et al.  Computational protein design with side‐chain conformational entropy , 2009, Proteins.

[22]  C. Deane,et al.  Antibody i-Patch prediction of the antibody binding site improves rigid local antibody-antigen docking. , 2013, Protein engineering, design & selection : PEDS.

[23]  Costas D. Maranas,et al.  MAPs: a database of modular antibody parts for predicting tertiary structures and designing affinity matured antibodies , 2013, BMC Bioinformatics.

[24]  G. A. Lazar,et al.  A molecular immunology approach to antibody humanization and functional optimization. , 2007, Molecular immunology.

[25]  Pham Phung,et al.  Broad and Potent Neutralizing Antibodies from an African Donor Reveal a New HIV-1 Vaccine Target , 2009, Science.

[26]  James E. Crowe,et al.  Antibody Recognition of the Pandemic H1N1 Influenza Virus Hemagglutinin Receptor Binding Site , 2013, Journal of Virology.

[27]  L. Croner,et al.  Stability engineering of scFvs for the development of bispecific and multivalent antibodies. , 2010, Protein engineering, design & selection : PEDS.

[28]  Mathieu Rouard,et al.  IMGT unique numbering for immunoglobulin and T cell receptor constant domains and Ig superfamily C-like domains. , 2005, Developmental and comparative immunology.

[29]  Jeffrey J. Gray,et al.  Toward high‐resolution homology modeling of antibody Fv regions and application to antibody–antigen docking , 2009, Proteins.

[30]  Alexander D. MacKerell,et al.  CHARMM general force field: A force field for drug‐like molecules compatible with the CHARMM all‐atom additive biological force fields , 2009, J. Comput. Chem..

[31]  H. Hoogenboom,et al.  Selecting and screening recombinant antibody libraries , 2005, Nature Biotechnology.

[32]  Wen-Lian Hsu,et al.  Rationalization and Design of the Complementarity Determining Region Sequences in an Antibody-Antigen Recognition Interface , 2012, PloS one.

[33]  Benjamin D. Sellers,et al.  Antibodies as a model system for comparative model refinement , 2010, Proteins.

[34]  C. Maranas,et al.  IPRO: an iterative computational protein library redesign and optimization procedure. , 2006, Biophysical journal.

[35]  James E. Crowe,et al.  A recurring motif for antibody recognition of the receptor-binding site of influenza hemagglutinin , 2013, Nature Structural &Molecular Biology.

[36]  P. Carter.  Potent antibody therapeutics by design , 2006, Nature Reviews Immunology.

[37]  Peter G Schultz,et al.  Structural plasticity and the evolution of antibody affinity and specificity. , 2003, Journal of molecular biology.

[38]  Peter Timmerman,et al.  Affinity maturation of antibodies assisted by in silico modeling , 2008, Proceedings of the National Academy of Sciences.

[39]  Janice M Reichert,et al.  Development trends for therapeutic antibody fragments , 2009, Nature Biotechnology.

[40]  A. Adler,et al.  Monoclonal antibody humanness score and its applications , 2013, BMC Biotechnology.

[41]  Dennis R Livesay,et al.  Thermodynamic stability and flexibility characteristics of antibody fragment complexes. , 2013, Protein and peptide letters.

[42]  C D Maranas,et al.  OptCDR: a general computational method for the design of antibody complementarity determining regions for targeted epitope binding. , 2010, Protein engineering, design & selection : PEDS.

[43]  Luigi Calzolai,et al.  Rational Engineering of a Human Anti-Dengue Antibody through Experimentally Validated Computational Docking , 2013, PloS one.

[44]  Valerie J. Gillet,et al.  De Novo Molecular Design , 2000 .

[45]  Andrew C. R. Martin,et al.  Analyzing the "degree of humanness" of antibody sequences. , 2007, Journal of molecular biology.

[46]  J. Foote,et al.  Immunogenicity of engineered antibodies. , 2005, Methods.

[47]  Antonio Lanzavecchia,et al.  Broadly neutralizing antiviral antibodies. , 2013, Annual review of immunology.

[48]  R. Hoess,et al.  Protein design and phage display. , 2001, Chemical reviews.

[49]  M. Lefranc.  IMGT unique numbering for the variable (V), constant (C), and groove (G) domains of IG, TR, MH, IgSF, and MhSF. , 2011, Cold Spring Harbor protocols.

[50]  Alfonso Jaramillo,et al.  Computational protein design is a challenge for implicit solvation models. , 2005, Biophysical journal.

[51]  Pham Phung,et al.  Broad neutralization coverage of HIV by multiple highly potent antibodies , 2011, Nature.

[52]  M. Lefranc.  IMGT Collier de Perles for the variable (V), constant (C), and groove (G) domains of IG, TR, MH, IgSF, and MhSF. , 2011, Cold Spring Harbor protocols.

[53]  Patrice Duroux,et al.  IMGT/Collier de Perles: IMGT standardized representation of domains (IG, TR, and IgSF variable and constant domains, MH and MhSF groove domains). , 2011, Cold Spring Harbor protocols.

[54]  G. Boulianne,et al.  Production of functional chimaeric mouse/human antibody , 1984, Nature.

[55]  O. Lund,et al.  novel sequence representations Reliable prediction of T-cell epitopes using neural networks with , 2003 .

[56]  T. T. Wu,et al.  Length distribution of CDRH3 in antibodies , 1993, Proteins.

[57]  D. Hilvert,et al.  Protein design by directed evolution. , 2008, Annual review of biophysics.

[58]  Samuel L. DeLuca,et al.  Human Germline Antibody Gene Segments Encode Polyspecific Antibodies , 2013, PLoS Comput. Biol..

[59]  Malgorzata B. Tracka,et al.  Redistribution of Flexibility in Stabilizing Antibody Fragment Mutants Follows Le Châtelier’s Principle , 2014, PloS one.

[60]  Hennie R. Hoogenboom,et al.  Guiding the Selection of Human Antibodies from Phage Display Repertoires to a Single Epitope of an Antigen , 1994, Bio/Technology.

[61]  James E. Crowe,et al.  Structural Basis of Preexisting Immunity to the 2009 H1N1 Pandemic Influenza Virus , 2010, Science.

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

[63]  Dima Kozakov,et al.  Application of asymmetric statistical potentials to antibody-protein docking , 2012, Bioinform..

[64]  M. Karplus,et al.  Effective energy function for proteins in solution , 1999, Proteins.

[65]  Lu-lu Gong,et al.  A Novel Antibody Humanization Method Based on Epitopes Scanning and Molecular Dynamics Simulation , 2013, PloS one.

[66]  Thomas B. Kepler,et al.  Preconfiguration of the antigen-binding site during affinity maturation of a broadly neutralizing influenza virus antibody , 2012, Proceedings of the National Academy of Sciences.