RosettaAntibodyDesign (RAbD): A general framework for computational antibody design

A structural-bioinformatics-based computational methodology and framework have been developed for the design of antibodies to targets of interest. RosettaAntibodyDesign (RAbD) samples the diverse sequence, structure, and binding space of an antibody to an antigen in highly customizable protocols for the design of antibodies in a broad range of applications. The program samples antibody sequences and structures by grafting structures from a widely accepted set of the canonical clusters of CDRs (North et al., J. Mol. Biol., 406:228–256, 2011). It then performs sequence design according to amino acid sequence profiles of each cluster, and samples CDR backbones using a flexible-backbone design protocol incorporating cluster-based CDR constraints. Starting from an existing experimental or computationally modeled antigen-antibody structure, RAbD can be used to redesign a single CDR or multiple CDRs with loops of different length, conformation, and sequence. We rigorously benchmarked RAbD on a set of 60 diverse antibody–antigen complexes, using two design strategies—optimizing total Rosetta energy and optimizing interface energy alone. We utilized two novel metrics for measuring success in computational protein design. The design risk ratio (DRR) is equal to the frequency of recovery of native CDR lengths and clusters divided by the frequency of sampling of those features during the Monte Carlo design procedure. Ratios greater than 1.0 indicate that the design process is picking out the native more frequently than expected from their sampled rate. We achieved DRRs for the non-H3 CDRs of between 2.4 and 4.0. The antigen risk ratio (ARR) is the ratio of frequencies of the native amino acid types, CDR lengths, and clusters in the output decoys for simulations performed in the presence and absence of the antigen. For CDRs, we achieved cluster ARRs as high as 2.5 for L1 and 1.5 for H2. For sequence design simulations without CDR grafting, the overall recovery for the native amino acid types for residues that contact the antigen in the native structures was 72% in simulations performed in the presence of the antigen and 48% in simulations performed without the antigen, for an ARR of 1.5. For the non-contacting residues, the ARR was 1.08. This shows that the sequence profiles are able to maintain the amino acid types of these conserved, buried sites, while recovery of the exposed, contacting residues requires the presence of the antigen-antibody interface. We tested RAbD experimentally on both a lambda and kappa antibody–antigen complex, successfully improving their affinities 10 to 50 fold by replacing individual CDRs of the native antibody with new CDR lengths and clusters.

[1]  Colin A. Smith,et al.  Backrub-like backbone simulation recapitulates natural protein conformational variability and improves mutant side-chain prediction. , 2008, Journal of molecular biology.

[2]  Brian D. Weitzner,et al.  Modeling and docking of antibody structures with Rosetta , 2017, Nature Protocols.

[3]  Roland L. Dunbrack,et al.  Stability engineering of anti-EGFR scFv antibodies by rational design of a lambda-to-kappa swap of the VL framework using a structure-guided approach , 2015, mAbs.

[4]  D. Huszar,et al.  Human antibodies from transgenic mice. , 1995, International reviews of immunology.

[5]  Akbar Nayeem,et al.  A comparative study of the simulated‐annealing and Monte Carlo‐with‐minimization approaches to the minimum‐energy structures of polypeptides: [Met]‐enkephalin , 1991 .

[6]  Brian D. Weitzner,et al.  The Origin of CDR H 3 Structural Diversity , 2022 .

[7]  O. Schueler‐Furman,et al.  Progress in protein–protein docking: Atomic resolution predictions in the CAPRI experiment using RosettaDock with an improved treatment of side‐chain flexibility , 2005, Proteins.

[8]  Costas D. Maranas,et al.  OptMAVEn – A New Framework for the de novo Design of Antibody Variable Region Models Targeting Specific Antigen Epitopes , 2014, PloS one.

[9]  Jeffrey J. Molldrem,et al.  Activity of 8F4, a T cell receptor-like anti-PR1/HLA-A2 antibody, against primary human AML in vivo , 2016, Leukemia.

[10]  P. Hudson,et al.  Engineered antibody fragments and the rise of single domains , 2005, Nature Biotechnology.

[11]  H. Wolfson,et al.  Shape complementarity at protein–protein interfaces , 1994, Biopolymers.

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

[13]  Roland L. Dunbrack,et al.  A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions. , 2011, Structure.

[14]  Yu Yao,et al.  Human cancer immunotherapy with antibodies to the PD-1 and PD-L1 pathway. , 2015, Trends in molecular medicine.

[15]  Eric A. Althoff,et al.  De Novo Computational Design of Retro-Aldol Enzymes , 2008, Science.

[16]  D. Baker,et al.  Alternate states of proteins revealed by detailed energy landscape mapping. , 2011, Journal of molecular biology.

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

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

[19]  Haruki Nakamura,et al.  Computer-aided antibody design , 2012, Protein engineering, design & selection : PEDS.

[20]  E. Diamandis,et al.  The biotin-(strept)avidin system: principles and applications in biotechnology. , 1991, Clinical chemistry.

[21]  M. Lefranc IMGT, the International ImMunoGeneTics Information System. , 2011, Cold Spring Harbor protocols.

[22]  Andrew M Wollacott,et al.  Prediction of amino acid sequence from structure , 2000, Protein science : a publication of the Protein Society.

[23]  Brian D. Weitzner,et al.  Accurate Structure Prediction of CDR H3 Loops Enabled by a Novel Structure-Based C-Terminal Constraint , 2017, The Journal of Immunology.

[24]  G. Winter,et al.  Selection of phage antibodies by binding affinity. Mimicking affinity maturation. , 1992, Journal of molecular biology.

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

[26]  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.

[27]  Jeffrey J. Gray,et al.  Modeling oblong proteins and water‐mediated interfaces with RosettaDock in CAPRI rounds 28–35 , 2017, Proteins.

[28]  Rhiju Das,et al.  Atomic-Accuracy Prediction of Protein Loop Structures through an RNA-Inspired Ansatz , 2012, PloS one.

[29]  M. Zalis,et al.  Visualizing and quantifying molecular goodness-of-fit: small-probe contact dots with explicit hydrogen atoms. , 1999, Journal of molecular biology.

[30]  H R Hoogenboom,et al.  Natural and designer binding sites made by phage display technology. , 2000, Immunology today.

[31]  Jared Adolf-Bryfogle,et al.  The PyRosetta Toolkit: A Graphical User Interface for the Rosetta Software Suite , 2013, PloS one.

[32]  Andrew C. R. Martin,et al.  Analysis and prediction of VH/VL packing in antibodies. , 2010, Protein engineering, design & selection : PEDS.

[33]  Brian Kuhlman,et al.  Structure-based design of supercharged, highly thermoresistant antibodies. , 2012, Chemistry & biology.

[34]  Krishna Praneeth Kilambi,et al.  Extending RosettaDock with water, sugar, and pH for prediction of complex structures and affinities for CAPRI rounds 20–27 , 2013, Proteins.

[35]  V. Roberts,et al.  Humanization and molecular modeling of the anti-CD4 monoclonal antibody, OKT4A. , 1996, Journal of immunology.

[36]  Eun Jung Choi,et al.  Incorporation of Noncanonical Amino Acids into Rosetta and Use in Computational Protein-Peptide Interface Design , 2012, PloS one.

[37]  Deping Wang,et al.  Structure-Guided Design of Antibodies. , 2010, Current computer-aided drug design.

[38]  Dirk Ponsel,et al.  High Affinity, Developability and Functional Size: The Holy Grail of Combinatorial Antibody Library Generation , 2011, Molecules.

[39]  E. Coutsias,et al.  Sub-angstrom accuracy in protein loop reconstruction by robotics-inspired conformational sampling , 2009, Nature Methods.

[40]  Jack Snoeyink,et al.  Scientific benchmarks for guiding macromolecular energy function improvement. , 2013, Methods in enzymology.

[41]  D. Baker,et al.  Relaxation of backbone bond geometry improves protein energy landscape modeling , 2014, Protein science : a publication of the Protein Society.

[42]  Jens Meiler,et al.  ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. , 2011, Methods in enzymology.

[43]  S. A. Marshall,et al.  Rational design and engineering of therapeutic proteins. , 2003, Drug discovery today.

[44]  Peter M Tessier,et al.  Advances in Antibody Design. , 2015, Annual review of biomedical engineering.

[45]  A. Bondi van der Waals Volumes and Radii , 1964 .

[46]  David Baker,et al.  A Pareto-Optimal Refinement Method for Protein Design Scaffolds , 2013, PloS one.

[47]  A. Plückthun,et al.  Yet another numbering scheme for immunoglobulin variable domains: an automatic modeling and analysis tool. , 2001, Journal of molecular biology.

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

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

[50]  Qifang Xu,et al.  PyIgClassify: a database of antibody CDR structural classifications , 2014, Nucleic Acids Res..

[51]  G. Georgiou,et al.  Antibody affinity maturation using bacterial surface display. , 1998, Protein engineering.

[52]  A Tramontano,et al.  Conformations of the third hypervariable region in the VH domain of immunoglobulins. , 1998, Journal of molecular biology.

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

[54]  David Baker,et al.  De Novo Enzyme Design Using Rosetta3 , 2011, PloS one.

[55]  Jeffrey J. Gray,et al.  RosettaAntibody: antibody variable region homology modeling server , 2009, Nucleic Acids Res..

[56]  H R Hoogenboom,et al.  Designing and optimizing library selection strategies for generating high-affinity antibodies. , 1997, Trends in biotechnology.

[57]  T. Clackson,et al.  Making antibody fragments using phage display libraries , 1991, Nature.

[58]  David Baker,et al.  Hotspot-centric de novo design of protein binders. , 2011, Journal of molecular biology.

[59]  S. Henikoff,et al.  Amino acid substitution matrices from protein blocks. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[60]  Jeffrey J. Gray,et al.  Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations. , 2003, Journal of molecular biology.

[61]  Yan Li,et al.  Affinity maturation of antiHER2 monoclonal antibody MIL5 using an epitope-specific synthetic phage library by computational design , 2013, Journal of biomolecular structure & dynamics.

[62]  G. Winter,et al.  Making antibodies by phage display technology. , 1994, Annual review of immunology.

[63]  H. Davies,et al.  When can odds ratios mislead? , 1998, BMJ.

[64]  C. Chothia The nature of the accessible and buried surfaces in proteins. , 1976, Journal of molecular biology.

[65]  Brian Kuhlman,et al.  Computational design of the sequence and structure of a protein-binding peptide. , 2011, Journal of the American Chemical Society.

[66]  Kenneth M. Merz,et al.  Rapid approximation to molecular surface area via the use of Boolean logic and look‐up tables , 1993, J. Comput. Chem..

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

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

[69]  Bernhardt L. Trout,et al.  Design of therapeutic proteins with enhanced stability , 2009, Proceedings of the National Academy of Sciences.

[70]  B. Burt Gerstman,et al.  Epidemiology Kept Simple: An Introduction to Traditional and Modern Epidemiology , 2013 .

[71]  Andreas Plückthun,et al.  Directed evolution of an anti-prion protein scFv fragment to an affinity of 1 pM and its structural interpretation. , 2006, Journal of molecular biology.

[72]  B. Kuhlman,et al.  A comparison of successful and failed protein interface designs highlights the challenges of designing buried hydrogen bonds , 2013, Protein science : a publication of the Protein Society.

[73]  Gregory P. Adams,et al.  Development of engineered antibodies specific for the Müllerian inhibiting substance type II receptor: a promising candidate for targeted therapy of ovarian cancer , 2006, Molecular Cancer Therapeutics.

[74]  Roland L. Dunbrack,et al.  A new clustering of antibody CDR loop conformations. , 2011, Journal of molecular biology.

[75]  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.

[76]  N. Lonberg,et al.  Human Monoclonal Antibodies from Transgenic Mice , 2008, Handbook of experimental pharmacology.

[77]  J. R. Holt,et al.  Transmembrane channel-like (TMC) genes are required for auditory and vestibular mechanosensation , 2014, Pflügers Archiv - European Journal of Physiology.

[78]  A. Gavezzotti,et al.  The calculation of molecular volumes and the use of volume analysis in the investigation of structured media and of solid-state organic reactivity , 1983 .

[79]  Alexander D. MacKerell,et al.  All-atom empirical potential for molecular modeling and dynamics studies of proteins. , 1998, The journal of physical chemistry. B.

[80]  T. Bratkovič,et al.  Progress in phage display: evolution of the technique and its applications , 2010, Cellular and Molecular Life Sciences.

[81]  G. Winter,et al.  Phage antibodies: filamentous phage displaying antibody variable domains , 1990, Nature.

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

[83]  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.

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

[85]  Brian D. Weitzner,et al.  Blind prediction performance of RosettaAntibody 3.0: Grafting, relaxation, kinematic loop modeling, and full CDR optimization , 2014, Proteins.

[86]  Thomas Szyperski,et al.  Computational design of a PAK1 binding protein. , 2010, Journal of molecular biology.

[87]  Steven M. Lewis,et al.  Anchored Design of Protein-Protein Interfaces , 2011, PloS one.

[88]  Roland L. Dunbrack,et al.  Bayesian statistical analysis of protein side‐chain rotamer preferences , 1997, Protein science : a publication of the Protein Society.

[89]  Karen M Polizzi,et al.  Better library design: data‐driven protein engineering , 2007, Biotechnology journal.

[90]  Ross Ihaka,et al.  Gentleman R: R: A language for data analysis and graphics , 1996 .

[91]  A. Lesk,et al.  Canonical structures for the hypervariable regions of immunoglobulins. , 1987, Journal of molecular biology.

[92]  B. Kuhlman,et al.  Design of structurally distinct proteins using strategies inspired by evolution , 2016, Science.

[93]  Brian D. Weitzner,et al.  The origin of CDR H3 structural diversity. , 2015, Structure.

[94]  Ian W. Davis,et al.  The backrub motion: how protein backbone shrugs when a sidechain dances. , 2006, Structure.

[95]  Anna M Wu,et al.  Arming antibodies: prospects and challenges for immunoconjugates , 2005, Nature Biotechnology.

[96]  B. Kuhlman,et al.  SwiftLib: rapid degenerate-codon-library optimization through dynamic programming , 2014, Nucleic acids research.

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

[98]  David Baker,et al.  Protein-protein docking with backbone flexibility. , 2007, Journal of molecular biology.

[99]  Sergey Lyskov,et al.  Improved prediction of antibody VL-VH orientation. , 2016, Protein engineering, design & selection : PEDS.

[100]  G. Adams,et al.  High affinity restricts the localization and tumor penetration of single-chain fv antibody molecules. , 2001, Cancer research.

[101]  Roland L. Dunbrack,et al.  The Role of Balanced Training and Testing Data Sets for Binary Classifiers in Bioinformatics , 2013, PloS one.

[102]  C. Akdis,et al.  Identification of a B-cell epitope of hyaluronidase, a major bee venom allergen, from its crystal structure in complex with a specific Fab. , 2007, Journal of molecular biology.

[103]  Adrian A Canutescu,et al.  Cyclic coordinate descent: A robotics algorithm for protein loop closure , 2003, Protein science : a publication of the Protein Society.

[104]  David Baker,et al.  Foldit Standalone: a video game-derived protein structure manipulation interface using Rosetta , 2017, Bioinform..

[105]  Shuming Nie,et al.  Single chain epidermal growth factor receptor antibody conjugated nanoparticles for in vivo tumor targeting and imaging. , 2008, Small.

[106]  Roland L. Dunbrack,et al.  Conformation dependence of backbone geometry in proteins. , 2009, Structure.

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

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

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

[110]  C. Milstein,et al.  The hybridoma revolution: an offshoot of basic research. , 1999, BioEssays : news and reviews in molecular, cellular and developmental biology.

[111]  Eric A. Althoff,et al.  Kemp elimination catalysts by computational enzyme design , 2008, Nature.

[112]  Brian Kuhlman,et al.  Engineering a protein–protein interface using a computationally designed library , 2010, Proceedings of the National Academy of Sciences.

[113]  G. Adams,et al.  Monoclonal antibody therapy of cancer , 1999, Nature Biotechnology.

[114]  A. Lesk,et al.  Standard conformations for the canonical structures of immunoglobulins. , 1997, Journal of molecular biology.

[115]  Sergey Lyskov,et al.  Alternative Computational Protocols for Supercharging Protein Surfaces for Reversible Unfolding and Retention of Stability , 2013, PloS one.

[116]  B. Kuhlman,et al.  Computational design of affinity and specificity at protein-protein interfaces. , 2009, Current opinion in structural biology.

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

[118]  Andrew J. Martin,et al.  Structural families in loops of homologous proteins: automatic classification, modelling and application to antibodies. , 1996, Journal of molecular biology.