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 IG and TR chains [3, 4] and more particularly of their variable domains (V-DOMAIN) which, at their N-terminal end, recognize and bind the antigens [2] (Fig. 2). The IG and TR synthesis includes several unique mechanisms that occur at the DNA level: combinatorial rearrangements of the V, D, and J genes that code the V-DOMAIN [the V–(D)–J being spliced to the C gene that encodes the C-REGION in the transcript]; exonuclease trimming at the ends of the V, D, and J genes; and random addition of nucleotides by the terminal deoxynucleotidyl transferase (TdT) (DNTT, DNA nucleotidylexotransferase) that creates the junc- tional N-diversity regions, and later during B cell differentiation, for the IG, somatic hypermutations, and class or subclass switch [3, 4] (Fig. 1). Antibody and IMGT®: 30 Years of Immunoinformatics 5 Fig. 2 Variable VH and VL domains of an immunoglobulin antigen binding site (With permission from M-P. Lefranc and G. Lefranc, LIGM, Founders and Authors of IMGT®, the international ImMunoGeneTics information system®, http://www. imgt.org [1]) 2 IMGT-ONTOLOGY and IMGT Scientific Chart Rules The first IMGT® breakthrough has been the IMGT gene and allele nomenclature for IG and TR [2–32], which allowed genes and data of the complex and highly diversified adaptive immune responses to be managed in genomic databases and tools. The second IMGT® breakthrough has been the IMGT unique numbering [33–40] and the IMGT Collier de Perles [41–47]. These breakthroughs have been formalized as concepts of classification (see Subheading 4) and con- cepts of numerotation (see Subheading 5) and have led to the imple- mentation of IMGT-ONTOLOGY [48–61] with seven axioms:   IDENTIFICATION, CLASSIFICATION, DESCRIPTION, NUMEROTATION, LOCALIZATION, ORIENTATION, and OBTENTION (Fig. 3).

Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi PART I INFORMATION OF A NTIBODY S EQUENCES AND STRUCTURES 1 Antibody Sequence and Structure Analyses Using IMGT®: 30 Years of Immunoinformatics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Marie-Paule Lefranc and Ge ́rard Lefranc 2 Structural Classification of CDR-H3 in Single-Domain V HH Antibodies . . . . . . 61 Daisuke Kuroda and Kouhei Tsumoto PART II M ODELING A NTIBODY S TRUCTURES AND D YNAMICS 3 Computational Modeling of Antibody and T-Cell Receptor (CDR3 Loops). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 Frederikke I. Marin and Paolo Marcatili 4 Molecular Dynamics Simulation for Investigating Antigen–Antibody Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Takefumi Yamashita 5 Molecular Dynamics Methods for Antibody Design . . . . . . . . . . . . . . . . . . . . . . . . . 109 Matthew Carter Childers and Valerie Daggett 6 Probing Conformational Dynamics of Antibodies with Geometric Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 Andrejs Tucs, Koji Tsuda, and Adnan Sljoka PART III PREDICTION AND O PTIMIZATION OF BIOLOGICAL AND BIOPHYSICAL PROPERTIES OF A NTIBODIES 7 PITHA: A Webtool to Predict Immunogenicity for Humanized and Fully Human Therapeutic Antibodies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Shide Liang and Chi Zhang 8 Thermal Stability Estimation of Single Domain Antibodies Using Molecular Dynamics Simulations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Gert-Jan Bekker and Narutoshi Kamiya 9 Assessing and Engineering Antibody Stability Using Experimental and Computational Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 Cheng Zhang and Paul Anthony Dalby 10 In Silico Prediction Method for Protein Asparagine Deamidation . . . . . . . . . . . . . 199 Lei Jia and Yaxiong Sun vii viii Contents 11 Structure-Based Optimization of Antibody-Based Biotherapeutics for Improved Developability: A Practical Guide for Molecular Modelers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 Nels Thorsteinson, Stephen R. Comeau Jr, and Sandeep Kumar PART IV P REDICTION OF ANTIBODY-A NTIGEN INTERACTIONS 12 B-Cell Epitope Predictions Using Computational Methods . . . . . . . . . . . . . . . . . . 239 Dandan Zheng, Shide Liang, and Chi Zhang 13 Computational Epitope Prediction and Design for Antibody Development and Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 Riccardo Capelli, Stefano A. Serapian, and Giorgio Colombo 14 Information-Driven Antibody–Antigen Modelling with HADDOCK . . . . . . . . . 267 Francesco Ambrosetti, Zuzana Jandova, and Alexandre M. J. J. Bonvin 15 Structural Modeling of Adaptive Immune Responses to Infection. . . . . . . . . . . . . 283 Tina Lusiany, Zichang Xu, Dianita S. Saputri, Hendra S. Ismanto, Sedat Aybars Nazlica, and Daron M. Standley 16 Protein–Protein Interaction Modelling with the Fragment Molecular Orbital Method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 Shigenori Tanaka PART V C OMPUTER-A IDED A NTIBODY A FFINITY M ATURATION AND BEYOND 17 Structural Considerations in Affinity Maturation of Antibody-Based Biotherapeutic Candidates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 Stephen R. Comeau, Nels Thorsteinson, and Sandeep Kumar 18 Structure-Based Affinity Maturation of Antibody Based on Double-Point Mutations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323 Shuntaro Chiba, Yasushi Okuno, and Masateru Ohta 19 Antibody Affinity Maturation Using Computational Methods: From an Initial Hit to Small-Scale Expression of Optimized Binders . . . . . . . . . . . . . . . . 333 Barbara Medagli, Miguel A. Soler, Rita De Zorzi, and Sara Fortuna 20 Optimizing Antibody–Antigen Binding Affinities with the ADAPT Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 Traian Sulea, Christophe Deprez, Christopher R. Corbeil, and Enrico O. Purisima 21 Using Graph-Based Signatures to Guide Rational Antibody Engineering . . . . . . 375 David B. Ascher, Lisa M. Kaminskas, Yoochan Myung, and Douglas E. V. Pires 22 A Computational Framework for Determining the Breadth of Antibodies Against Highly Mutable Pathogens. . . . . . . . . . . . . . . . . . . . . . . . . . . 399 Simone Conti and Martin Karplus

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