Computational Design of Epitope-Specific Functional Antibodies.

The ultimate goal of protein design is to introduce new biological activity. We propose a computational approach for designing functional antibodies by focusing on functional epitopes, integrating large-scale statistical analysis with multiple structural models. Machine learning is used to analyze these models and predict specific residue-residue contacts. We use this approach to design a functional antibody to counter the proinflammatory effect of the cytokine interleukin-17A (IL-17A). X-ray crystallography confirms that the designed antibody binds the targeted epitope and the interaction is mediated by the designed contacts. Cell-based assays confirm that the antibody is functional. Importantly, this approach does not rely on a high-quality 3D model of the designed complex or even a solved structure of the target. As demonstrated here, this approach can be used to design biologically active antibodies, removing some of the main hurdles in antibody design and in drug discovery.

[1]  R. D. Gietz,et al.  Transformation of yeast by lithium acetate/single-stranded carrier DNA/polyethylene glycol method. , 2002, Methods in enzymology.

[2]  S. Tuske,et al.  Structure of IL-17A in complex with a potent, fully human neutralizing antibody. , 2009, Journal of molecular biology.

[3]  S. Wodak,et al.  Modeling protein–protein and protein–peptide complexes: CAPRI 6th edition , 2017, Proteins.

[4]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[5]  David Baker,et al.  A de novo protein binding pair by computational design and directed evolution. , 2011, Molecular cell.

[6]  Stephen L Mayo,et al.  A de novo designed protein–protein interface , 2007, Protein science : a publication of the Protein Society.

[7]  David Baker,et al.  Macromolecular modeling with rosetta. , 2008, Annual review of biochemistry.

[8]  Eric T. Boder,et al.  Yeast surface display for screening combinatorial polypeptide libraries , 1997, Nature Biotechnology.

[9]  M. Tyka,et al.  Principles for computational design of binding antibodies , 2017, Proceedings of the National Academy of Sciences.

[10]  Philip R. Evans,et al.  How good are my data and what is the resolution? , 2013, Acta crystallographica. Section D, Biological crystallography.

[11]  Sarel J. Fleishman,et al.  AbDesign: An algorithm for combinatorial backbone design guided by natural conformations and sequences , 2015, Proteins.

[12]  J. Krstić,et al.  An Overview of Interleukin-17A and Interleukin-17 Receptor A Structure, Interaction and Signaling. , 2015, Protein and peptide letters.

[13]  Andreas Plückthun,et al.  DARPins and other repeat protein scaffolds: advances in engineering and applications. , 2011, Current opinion in biotechnology.

[14]  Jiye Shi,et al.  Computational design of an epitope-specific Keap1 binding antibody using hotspot residues grafting and CDR loop swapping , 2017, Scientific Reports.

[15]  Yanay Ofran,et al.  Paratome: an online tool for systematic identification of antigen-binding regions in antibodies based on sequence or structure , 2012, Nucleic Acids Res..

[16]  Philip R. Evans,et al.  An introduction to data reduction: space-group determination, scaling and intensity statistics , 2011, Acta crystallographica. Section D, Biological crystallography.

[17]  N. Pannu,et al.  REFMAC5 for the refinement of macromolecular crystal structures , 2011, Acta crystallographica. Section D, Biological crystallography.

[18]  W. L. Jorgensen,et al.  Comparison of simple potential functions for simulating liquid water , 1983 .

[19]  L. K. Ely,et al.  Structural basis of receptor sharing by interleukin 17 cytokines , 2009, Nature Immunology.

[20]  T. Blundell,et al.  Comparative protein modelling by satisfaction of spatial restraints. , 1993, Journal of molecular biology.

[21]  Randy J. Read,et al.  Phaser crystallographic software , 2007, Journal of applied crystallography.

[22]  Yanay Ofran,et al.  Using a combined computational-experimental approach to predict antibody-specific B cell epitopes. , 2014, Structure.

[23]  Steven L Salzberg,et al.  Fast gapped-read alignment with Bowtie 2 , 2012, Nature Methods.

[24]  Yanay Ofran,et al.  Understanding differences between synthetic and natural antibodies can help improve antibody engineering , 2016, mAbs.

[25]  Y. Ofran,et al.  The indistinguishability of epitopes from protein surface is explained by the distinct binding preferences of each of the six antigen-binding loops. , 2013, Protein engineering, design & selection : PEDS.

[26]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[27]  P. Emsley,et al.  Features and development of Coot , 2010, Acta crystallographica. Section D, Biological crystallography.

[28]  Yanay Ofran,et al.  Structural Consensus among Antibodies Defines the Antigen Binding Site , 2012, PLoS Comput. Biol..

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

[30]  Xi Song,et al.  Crystal structures of interleukin 17A and its complex with IL-17 receptor A , 2013, Nature Communications.

[31]  Carsten Kutzner,et al.  GROMACS 4:  Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. , 2008, Journal of chemical theory and computation.

[32]  J. Haidar,et al.  A universal combinatorial design of antibody framework to graft distinct CDR sequences: A bioinformatics approach , 2012, Proteins.

[33]  Randy J. Read,et al.  Overview of the CCP4 suite and current developments , 2011, Acta crystallographica. Section D, Biological crystallography.

[34]  Z. Weng,et al.  ZDOCK: An initial‐stage protein‐docking algorithm , 2003, Proteins.

[35]  W. Kabsch,et al.  Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features , 1983, Biopolymers.

[36]  David W. Ritchie,et al.  Ultra-fast FFT protein docking on graphics processors , 2010, Bioinform..

[37]  D. Baker,et al.  Design of a Novel Globular Protein Fold with Atomic-Level Accuracy , 2003, Science.

[38]  R. Nitsch,et al.  Amyloid-β Peptide-specific DARPins as a Novel Class of Potential Therapeutics for Alzheimer Disease* , 2014, The Journal of Biological Chemistry.

[39]  Y. Sugita,et al.  Conformational transition of Sec machinery inferred from bacterial SecYE structures , 2008, Nature.

[40]  Thomas L. Madden,et al.  Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. , 1997, Nucleic acids research.

[41]  Klaus Schulten,et al.  Computational de novo design of antibodies binding to a peptide with high affinity , 2017, Biotechnology and bioengineering.

[42]  David Baker,et al.  Computational design of a pH-sensitive IgG binding protein , 2013, Proceedings of the National Academy of Sciences.

[43]  Yanay Ofran,et al.  De-novo protein function prediction using DNA binding and RNA binding proteins as a test case , 2016, Nature Communications.

[44]  Graeme Winter,et al.  xia2: an expert system for macromolecular crystallography data reduction , 2010 .

[45]  K Dane Wittrup,et al.  Isolating and engineering human antibodies using yeast surface display , 2006, Nature Protocols.

[46]  K D Wittrup,et al.  Yeast surface display for directed evolution of protein expression, affinity, and stability. , 2000, Methods in enzymology.

[47]  Tao Zhang,et al.  Rational design of TNFα binding proteins based on the de novo designed protein DS119 , 2016, Protein science : a publication of the Protein Society.

[48]  D. Hatton,et al.  A high‐yielding CHO transient system: Coexpression of genes encoding EBNA‐1 and GS enhances transient protein expression , 2014, Biotechnology progress.

[49]  Paul Theodor Pyl,et al.  HTSeq—a Python framework to work with high-throughput sequencing data , 2014, bioRxiv.

[50]  Tongqing Zhou,et al.  Interfacial metal and antibody recognition. , 2005, Proceedings of the National Academy of Sciences of the United States of America.