Computer-Aided Antibody Design: An Overview.

The use of monoclonal antibody as the next generation protein therapeutics with remarkable success has surged the development of antibody engineering to design molecules for optimizing affinity, better efficacy, greater safety and therapeutic function. Therefore, computational methods have become increasingly important to generate hypotheses, interpret and guide experimental works. In this chapter, we discussed the overall antibody design by computational approches.

[1]  Ying Zhang,et al.  Mechanisms of Pyrazinamide Action and Resistance. , 2014, Microbiology spectrum.

[2]  Xiaojie Xu,et al.  Predictions of Binding of a Diverse Set of Ligands to Gelatinase-A by a Combination of Molecular Dynamics and Continuum Solvent Models , 2002 .

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

[4]  Gajendra P. S. Raghava,et al.  AntigenDB: an immunoinformatics database of pathogen antigens , 2009, Nucleic Acids Res..

[5]  Yuxin Li,et al.  Pep-3D-Search: a method for B-cell epitope prediction based on mimotope analysis , 2008, BMC Bioinformatics.

[6]  M. Levitt Accurate modeling of protein conformation by automatic segment matching. , 1992, Journal of molecular biology.

[7]  A. Kidera,et al.  Conformational sampling of CDR-H3 in antibodies by multicanonical molecular dynamics simulation. , 1998, Journal of molecular biology.

[8]  William D. Lees,et al.  Investigating Substitutions in Antibody–Antigen Complexes Using Molecular Dynamics: A Case Study with Broad-spectrum, Influenza A Antibodies , 2017, Front. Immunol..

[9]  Dale E Tronrud,et al.  Introduction to macromolecular refinement. , 2004, Methods in molecular biology.

[10]  S. Ben-Horin,et al.  The role of very late antigen-1 in immune-mediated inflammation. , 2004, Clinical immunology.

[11]  Luca Varani,et al.  Computational Docking of Antibody-Antigen Complexes, Opportunities and Pitfalls Illustrated by Influenza Hemagglutinin , 2011, International journal of molecular sciences.

[12]  G J Williams,et al.  The Protein Data Bank: a computer-based archival file for macromolecular structures. , 1978, Archives of biochemistry and biophysics.

[13]  Zhiping Weng,et al.  Docking unbound proteins using shape complementarity, desolvation, and electrostatics , 2002, Proteins.

[14]  Michel F Sanner,et al.  FLIPDock: Docking flexible ligands into flexible receptors , 2007, Proteins.

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

[16]  Jeffrey J. Gray,et al.  Conformer selection and induced fit in flexible backbone protein-protein docking using computational and NMR ensembles. , 2008, Journal of molecular biology.

[17]  Jian Huang,et al.  CED: a conformational epitope database , 2006, BMC Immunology.

[18]  T. Yeates,et al.  Verification of protein structures: Patterns of nonbonded atomic interactions , 1993, Protein science : a publication of the Protein Society.

[19]  Zhiping Weng,et al.  ZDOCK server: interactive docking prediction of protein-protein complexes and symmetric multimers , 2014, Bioinform..

[20]  Xiaoqin Zou,et al.  Advances and Challenges in Protein-Ligand Docking , 2010, International journal of molecular sciences.

[21]  Haruki Nakamura,et al.  Structural classification of CDR‐H3 revisited: A lesson in antibody modeling , 2008, Proteins.

[22]  David E. Shaw,et al.  The future of molecular dynamics simulations in drug discovery , 2011, Journal of Computer-Aided Molecular Design.

[23]  Arieh Warshel,et al.  Multiscale simulations of protein landscapes: Using coarse‐grained models as reference potentials to full explicit models , 2010, Proteins.

[24]  C. Oostenbrink,et al.  Antibody humanization by molecular dynamics simulations—in‐silico guided selection of critical backmutations , 2016, Journal of molecular recognition : JMR.

[25]  Tyler Day,et al.  Ab initio structure prediction of the antibody hypervariable H3 loop , 2013, Proteins.

[26]  Thomas B. Kepler,et al.  SoDA: implementation of a 3D alignment algorithm for inference of antigen receptor recombinations , 2006, Bioinform..

[27]  Wei Li,et al.  ElliPro: a new structure-based tool for the prediction of antibody epitopes , 2008, BMC Bioinformatics.

[28]  P. Bates,et al.  SwarmDock and the Use of Normal Modes in Protein-Protein Docking , 2010, International journal of molecular sciences.

[29]  O. Lund,et al.  Prediction of residues in discontinuous B‐cell epitopes using protein 3D structures , 2006, Protein science : a publication of the Protein Society.

[30]  Thomas Simonson,et al.  Pairwise decomposition of an MMGBSA energy function for computational protein design , 2014, J. Comput. Chem..

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

[32]  M. Karow,et al.  Evolution and emergence of therapeutic monoclonal antibodies: what cardiologists need to know. , 2013, Circulation.

[33]  Pierre Baldi,et al.  PEPITO: improved discontinuous B-cell epitope prediction using multiple distance thresholds and half sphere exposure , 2008, Bioinform..

[34]  Kouhei Tsumoto,et al.  Affinity Improvement of a Therapeutic Antibody by Structure-Based Computational Design: Generation of Electrostatic Interactions in the Transition State Stabilizes the Antibody-Antigen Complex , 2014, PloS one.

[35]  Anna Tramontano,et al.  The role of molecular modelling in biomedical research , 2006, FEBS letters.

[36]  P. Bradley,et al.  Toward High-Resolution de Novo Structure Prediction for Small Proteins , 2005, Science.

[37]  O. Lund,et al.  The design and implementation of the immune epitope database and analysis resource , 2005, Immunogenetics.

[38]  Vasant Honavar,et al.  Recent advances in B-cell epitope prediction methods , 2010, Immunome research.

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

[40]  Sergio Rosales-Mendoza,et al.  An overview of bioinformatics tools for epitope prediction: Implications on vaccine development , 2015, J. Biomed. Informatics.

[41]  P. Kollman,et al.  Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. , 2000, Accounts of chemical research.

[42]  Yang Lu,et al.  MimoPro: a more efficient Web-based tool for epitope prediction using phage display libraries , 2011, BMC Bioinformatics.

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

[44]  Jinbo Xu,et al.  Protein structure prediction using threading. , 2008, Methods in molecular biology.

[45]  Wen Huang,et al.  MTML-msBayes: Approximate Bayesian comparative phylogeographic inference from multiple taxa and multiple loci with rate heterogeneity , 2011, BMC Bioinformatics.

[46]  L. Stamatatos,et al.  Computational design of epitope-scaffolds allows induction of antibodies specific for a poorly immunogenic HIV vaccine epitope. , 2010, Structure.

[47]  Y. Liao,et al.  Production of a phage-displayed mouse ScFv antibody against fumonisin B1 and molecular docking analysis of their interactions , 2016, Biotechnology and Bioprocess Engineering.

[48]  Marjana Novič,et al.  The Comparison of Docking Search Algorithms and Scoring Functions: An Overview and Case Studies , 2016 .

[49]  J M Masson,et al.  Alanine-stretch scanning mutagenesis: a simple and efficient method to probe protein structure and function. , 1997, Nucleic acids research.

[50]  M. Vendruscolo,et al.  Rational design of antibodies targeting specific epitopes within intrinsically disordered proteins , 2015, Proceedings of the National Academy of Sciences.

[51]  R. Tierney,et al.  Development of Germline-Humanized Antibodies Neutralizing Botulinum Neurotoxin A and B , 2016, PloS one.

[52]  D. Tronrud Conjugate-direction minimization: an improved method for the refinement of macromolecules. , 1992, Acta crystallographica. Section A, Foundations of crystallography.

[53]  O. Schueler‐Furman,et al.  Improved side‐chain modeling for protein–protein docking , 2005, Protein science : a publication of the Protein Society.

[54]  Hui Ding,et al.  BDB: biopanning data bank , 2015, Nucleic Acids Res..

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

[56]  J. A. Mcnulty,et al.  Characterization of lymphocyte subsets over a 24-hour period in Pineal-Associated Lymphoid Tissue (PALT) in the chicken , 2006, BMC Immunology.

[57]  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 .

[58]  V. Vyas,et al.  Homology Modeling a Fast Tool for Drug Discovery: Current Perspectives , 2012, Indian journal of pharmaceutical sciences.

[59]  V. Lee,et al.  Improved scFv Anti-HIV-1 p17 Binding Affinity Guided from the Theoretical Calculation of Pairwise Decomposition Energies and Computational Alanine Scanning , 2013, BioMed research international.

[60]  Yasuhiro Matsunaga,et al.  GENESIS: a hybrid-parallel and multi-scale molecular dynamics simulator with enhanced sampling algorithms for biomolecular and cellular simulations , 2015, Wiley interdisciplinary reviews. Computational molecular science.

[61]  Jiye Shi,et al.  SAbDab: the structural antibody database , 2013, Nucleic Acids Res..

[62]  D. Ecker,et al.  The therapeutic monoclonal antibody market , 2015, mAbs.

[63]  Itay Mayrose,et al.  Epitopia: a web-server for predicting B-cell epitopes , 2009, BMC Bioinformatics.

[64]  P. Labute,et al.  Antibody modeling assessment , 2011, Proteins.

[65]  Jens Meiler,et al.  Redesigned HIV antibodies exhibit enhanced neutralizing potency and breadth. , 2015, The Journal of clinical investigation.

[66]  Prasanna R Kolatkar,et al.  Assessment of CASP7 structure predictions for template free targets , 2007, Proteins.

[67]  Richard Friesner,et al.  Antibody structure determination using a combination of homology modeling, energy‐based refinement, and loop prediction , 2014, Proteins.

[68]  Haruki Nakamura,et al.  H3‐rules: identification of CDR‐H3 structures in antibodies , 1999, FEBS letters.

[69]  Paolo Marcatili,et al.  Improving the accuracy of the structure prediction of the third hypervariable loop of the heavy chains of antibodies , 2014, Bioinform..

[70]  David T. Jones THREADER : Protein Sequence Threading by Double Dynamic Programming , 1998 .

[71]  David E. Kim,et al.  Computational Alanine Scanning of Protein-Protein Interfaces , 2004, Science's STKE.

[72]  D. Eisenberg,et al.  VERIFY3D: assessment of protein models with three-dimensional profiles. , 1997, Methods in enzymology.

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

[74]  Tuomo Laitinen,et al.  Free energy simulations and MM–PBSA analyses on the affinity and specificity of steroid binding to antiestradiol antibody , 2004, Proteins.

[75]  Arthur J. Olson,et al.  AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading , 2009, J. Comput. Chem..

[76]  D. Baker,et al.  Elicitation of structure-specific antibodies by epitope scaffolds , 2010, Proceedings of the National Academy of Sciences.

[77]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[78]  J. Greer Comparative modeling methods: Application to the family of the mammalian serine proteases , 1990, Proteins.

[79]  E. Jaeger,et al.  Docking: successes and challenges. , 2005, Current pharmaceutical design.

[80]  David S. Goodsell,et al.  AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility , 2009, J. Comput. Chem..

[81]  Gaba Monika,et al.  AN OVERVIEW ON MOLECULAR DOCKING , 2010 .

[82]  Pedro Alexandrino Fernandes,et al.  Protein–ligand docking: Current status and future challenges , 2006, Proteins.

[83]  Chi Zhang,et al.  Prediction of antigenic epitopes on protein surfaces by consensus scoring , 2009, BMC Bioinformatics.

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

[85]  E. Padlan,et al.  Antibody-antigen complexes. , 1988, Annual review of biochemistry.

[86]  G. A. Lazar,et al.  Engineered antibody Fc variants with enhanced effector function. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[87]  Chris Bailey-Kellogg,et al.  Machine Learning Methods Enable Predictive Modeling of Antibody Feature:Function Relationships in RV144 Vaccinees , 2015, PLoS Comput. Biol..

[88]  Yang Zhang,et al.  The I-TASSER Suite: protein structure and function prediction , 2014, Nature Methods.

[89]  Paolo Marcatili,et al.  PIGS: automatic prediction of antibody structures , 2008, Bioinform..

[90]  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.

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

[92]  S. Tonegawa Somatic generation of antibody diversity , 1983, Nature.

[93]  Ben M. Webb,et al.  Comparative Protein Structure Modeling Using Modeller , 2006, Current protocols in bioinformatics.

[94]  E. Myers,et al.  Basic local alignment search tool. , 1990, Journal of molecular biology.

[95]  Luciano Milanesi,et al.  ASPD (Artificially Selected Proteins/Peptides Database): a database of proteins and peptides evolved in vitro , 2002, Nucleic Acids Res..

[96]  Pedro Alexandrino Fernandes,et al.  Computational alanine scanning mutagenesis—An improved methodological approach , 2007, J. Comput. Chem..

[97]  Ilya A Vakser,et al.  Protein-protein docking: from interaction to interactome. , 2014, Biophysical journal.

[98]  S. Quake,et al.  The promise and challenge of high-throughput sequencing of the antibody repertoire , 2014, Nature Biotechnology.

[99]  Jens Meiler,et al.  Antibodies: Computer-Aided Prediction of Structure and Design of Function. , 2014, Microbiology spectrum.

[100]  P. Dong,et al.  Emerging Therapeutic Biomarkers in Endometrial Cancer , 2013, BioMed research international.

[101]  Gary L Gilliland,et al.  Canonical structures of short CDR-L3 in antibodies , 2014, Proteins.

[102]  J. Reichert,et al.  Development trends for human monoclonal antibody therapeutics , 2010, Nature Reviews Drug Discovery.

[103]  Juan C Almagro,et al.  Second antibody modeling assessment (AMA‐II) , 2014, Proteins.

[104]  Hao Fan,et al.  Refinement of homology‐based protein structures by molecular dynamics simulation techniques , 2004, Protein science : a publication of the Protein Society.

[105]  Jing Li,et al.  Research and development of next generation of antibody-based therapeutics , 2010, Acta Pharmacologica Sinica.

[106]  A. Liwo,et al.  Physics-based protein-structure prediction using a hierarchical protocol based on the UNRES force field: assessment in two blind tests. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

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

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

[109]  G. N. Ramachandran,et al.  Stereochemistry of polypeptide chain configurations. , 1963, Journal of molecular biology.

[110]  V. Lee,et al.  Pairwise decomposition of residue interaction energies of single chain Fv with HIV-1 p17 epitope variants. , 2010, Molecular immunology.

[111]  L Wang,et al.  Molecular dynamics and free-energy calculations applied to affinity maturation in antibody 48G7. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[112]  A. Beck Biosimilar, biobetter and next generation therapeutic antibodies , 2011, mAbs.

[113]  Josep Ramón Goñi,et al.  Molecular dynamics simulations: advances and applications , 2015, Advances and applications in bioinformatics and chemistry : AABC.

[114]  Zhiqiang Ma,et al.  Bioinformatics Resources and Tools for Conformational B-Cell Epitope Prediction , 2013, Comput. Math. Methods Medicine.

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

[116]  Peng Liu,et al.  New threats to health data privacy , 2011, BMC Bioinformatics.

[117]  Thomas Lengauer,et al.  A fast flexible docking method using an incremental construction algorithm. , 1996, Journal of molecular biology.

[118]  Z. Xiang,et al.  Advances in homology protein structure modeling. , 2006, Current protein & peptide science.

[119]  David S. Goodsell,et al.  AutoDockFR: Advances in Protein-Ligand Docking with Explicitly Specified Binding Site Flexibility , 2015, PLoS Comput. Biol..

[120]  David S. Goodsell,et al.  Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function , 1998 .

[121]  J. Garnier,et al.  Modeling of protein loops by simulated annealing , 1993, Protein science : a publication of the Protein Society.

[122]  M. Mezei,et al.  Molecular docking: a powerful approach for structure-based drug discovery. , 2011, Current computer-aided drug design.

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

[124]  Deborah Hix,et al.  The immune epitope database (IEDB) 3.0 , 2014, Nucleic Acids Res..

[125]  Di Wu,et al.  SEPPA: a computational server for spatial epitope prediction of protein antigens , 2009, Nucleic Acids Res..

[126]  Justin K H Liu The history of monoclonal antibody development – Progress, remaining challenges and future innovations , 2014, Annals of medicine and surgery.

[127]  M. Cheng,et al.  Structural design of disialoganglioside GD2 and CD3‐bispecific antibodies to redirect T cells for tumor therapy , 2015, International journal of cancer.