Abstract
The immune systems protect our bodies from foreign molecules or antigens, where antibodies play important roles. Antibodies evolve over time upon antigen encounter by somatically mutating their genome sequences. The end result is a series of antibodies that display higher affinities and specificities to specific antigens. This process is called affinity maturation. Recent improvements in computer hardware and modeling algorithms now enable the rational design of protein structures and functions, and several works on computer-aided antibody design have been published. In this chapter, we briefly describe computational methods for antibody affinity maturation, focusing on methods for sampling antibody conformations and for scoring designed antibody variants. We also discuss lessons learned from the successful computer-aided design of antibodies.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jorgensen WL (2004) The many roles of computation in drug discovery. Science 303:1813–1818. https://doi.org/10.1126/science.1096361
Kuroda D, Shirai H, Jacobson MP, Nakamura H (2012) Computer-aided antibody design. Protein Eng Des Sel 25:507–521. https://doi.org/10.1093/protein/gzs024
Krawczyk K, Dunbar J, Deane CM (2017) Computational tools for aiding rational antibody design. Methods Mol Biol. 1529:399–416
Clark LA, Boriack-Sjodin PA, Eldredge J et al (2006) Affinity enhancement of an in vivo matured therapeutic antibody using structure-based computational design. Protein Sci 15:949–960. https://doi.org/10.1110/ps.052030506
Lippow SM, Wittrup KD, Tidor B (2007) Computational design of antibody-affinity improvement beyond in vivo maturation. Nat Biotechnol 25:1171–1176. https://doi.org/10.1038/nbt1336
Li B, Zhao L, Wang C et al (2010) The protein-protein interface evolution acts in a similar way to antibody affinity maturation. J Biol Chem 285:3865–3871. https://doi.org/10.1074/jbc.M109.076547
Kiyoshi M, Caaveiro JMM, Miura E et al (2014) Affinity improvement of a therapeutic antibody by structure-based computational design: generation of electrostatic interactions in the transition state stabilizes the antibody-antigen complex. PLoS One 9:e87099. https://doi.org/10.1371/journal.pone.0087099
Farady CJ, Sellers BD, Jacobson MP, Craik CS (2009) Improving the species cross-reactivity of an antibody using computational design. Bioorg Med Chem Lett 19:3744–3747. https://doi.org/10.1016/j.bmcl.2009.05.005
McConnell AD, Spasojevich V, Macomber JL et al (2013) An integrated approach to extreme thermostabilization and affinity maturation of an antibody. Protein Eng Des Sel 26:151–163. https://doi.org/10.1093/protein/gzs090
Choi Y, Ndong C, Griswold KE, Bailey-Kellogg C (2016) Computationally driven antibody engineering enables simultaneous humanization and thermostabilization. Protein Eng Des Sel 29:419–426. https://doi.org/10.1093/protein/gzw024
Chennamsetty N, Voynov V, Kayser V et al (2009) Design of therapeutic proteins with enhanced stability. Proc Natl Acad Sci U S A 106:11937–11942. https://doi.org/10.1073/pnas.0904191106
Lauer TM, Agrawal NJ, Chennamsetty N et al (2012) Developability index: a rapid in silico tool for the screening of antibody aggregation propensity. J Pharm Sci 101:102–115. https://doi.org/10.1002/jps.22758
Almagro JC, Fransson J (2008) Humanization of antibodies. Front Biosci 13:1619–1633. https://doi.org/10.1093/toxsci/kft065
Abhinandan KR, Martin ACR (2007) Analyzing the “degree of humanness” of antibody sequences. J Mol Biol 369:852–862. https://doi.org/10.1016/j.jmb.2007.02.100
Zhang D, Chen CF, Zhao BB et al (2013) A novel antibody humanization method based on epitopes scanning and molecular dynamics simulation. PLoS One 8:e80636. https://doi.org/10.1371/journal.pone.0080636
Seeliger D (2013) Development of scoring functions for antibody sequence assessment and optimization. PLoS One 8:e76909. https://doi.org/10.1371/journal.pone.0076909
Hanf KJM, Arndt JW, Chen LL et al (2014) Antibody humanization by redesign of complementarity-determining region residues proximate to the acceptor framework. Methods 65:68–76. https://doi.org/10.1016/j.ymeth.2013.06.024
Olimpieri PP, Marcatili P, Tramontano A (2015) Tabhu: tools for antibody humanization. Bioinformatics 31:434–435. https://doi.org/10.1093/bioinformatics/btu667
Margreitter C, Mayrhofer P, Kunert R, Oostenbrink C (2016) Antibody humanization by molecular dynamics simulations—in-silico guided selection of critical backmutations. J Mol Recognit 29:266–275. https://doi.org/10.1002/jmr.2527
Chothia C, Lesk AM (1987) Canonical structures for the hypervariable regions of immunoglobulins. J Mol Biol 196:901–917. https://doi.org/10.1016/0022-2836(87)90412-8
Pettersen EF, Goddard TD, Huang CC et al (2004) UCSF chimera—a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612. https://doi.org/10.1002/jcc.20084
Kuroda D, Shirai H, Kobori M, Nakamura H (2008) Structural classification of CDR-H3 revisited: a lesson in antibody modeling. Proteins Struct Funct Bioinforma 73:608–620. https://doi.org/10.1002/prot.22087
Weitzner BD, Dunbrack RL, Gray JJ (2015) The origin of CDR H3 structural diversity. Structure 23:302–311. https://doi.org/10.1016/j.str.2014.11.010
Tsuchiya Y, Mizuguchi K (2016) The diversity of H3 loops determines the antigen-binding tendencies of antibody CDR loops. Protein Sci 25:815–825. https://doi.org/10.1002/pro.2874
Regep C, Georges G, Shi J et al (2017) The H3 loop of antibodies shows unique structural characteristics. Proteins Struct Funct Bioinforma 85:1311–1318. https://doi.org/10.1002/prot.25291
Dunbar J, Krawczyk K, Leem J et al (2014) SAbDab: the structural antibody database. Nucleic Acids Res 42:D1140–D1146. https://doi.org/10.1093/nar/gkt1043
Adolf-Bryfogle J, Xu Q, North B et al (2015) PyIgClassify: a database of antibody CDR structural classifications. Nucleic Acids Res 43:D432–D438. https://doi.org/10.1093/nar/gku1106
Al-Lazikani B, Lesk AM, Chothia C (1997) Standard conformations for the canonical structures of immunoglobulins. J Mol Biol 273:927–948. https://doi.org/10.1006/jmbi.1997.1354
Kuroda D, Shirai H, Kobori M, Nakamura H (2009) Systematic classification of CDR-L3 in antibodies: implications of the light chain subtypes and the VL-VH interface. Proteins Struct Funct Bioinforma 75:139–146. https://doi.org/10.1002/prot.22230
North B, Lehmann A, Dunbrack RL (2011) A new clustering of antibody CDR loop conformations. J Mol Biol 406:228–256. https://doi.org/10.1016/j.jmb.2010.10.030
Teplyakov A, Gilliland GL (2014) Canonical structures of short CDR-L3 in antibodies. Proteins Struct Funct Bioinforma 82:1668–1673. https://doi.org/10.1002/prot.24559
Foote J, Winter G (1992) Antibody framework residues affecting the conformation of the hypervariable loops. J Mol Biol 224:487–499. https://doi.org/10.1016/0022-2836(92)91010-M
Spiess C, Zhai Q, Carter PJ (2015) Alternative molecular formats and therapeutic applications for bispecific antibodies. Mol Immunol 67:95–106
Tomlinson IM, Walter G, Jones PT et al (1996) The imprint of somatic hypermutation on the repertoire of human germline V genes. J Mol Biol 256:813–817. https://doi.org/10.1006/jmbi.1996.0127
Clark LA, Ganesan S, Papp S, van Vlijmen HWT (2006) Trends in antibody sequence changes during the somatic hypermutation process. J Immunol 177:333–340. https://doi.org/10.4049/jimmunol.177.1.333
Burkovitz A, Sela-Culang I, Ofran Y (2014) Large-scale analysis of somatic hypermutations in antibodies reveals which structural regions, positions and amino acids are modified to improve affinity. FEBS J 281:306–319. https://doi.org/10.1111/febs.12597
DeKosky BJ, Lungu OI, Park D et al (2016) Large-scale sequence and structural comparisons of human naive and antigen-experienced antibody repertoires. Proc Natl Acad Sci U S A 113:E2636–E2645. https://doi.org/10.1073/pnas.1525510113
Li Y, Li H, Yang F et al (2003) X-ray snapshots of the maturation of an antibody response to a protein antigen. Nat Struct Mol Biol 10:482–488. https://doi.org/10.1038/nsb930
Kuroda D, Gray JJ (2016) Shape complementarity and hydrogen bond preferences in protein-protein interfaces: implications for antibody modeling and protein-protein docking. Bioinformatics 32:2451–2456. https://doi.org/10.1093/bioinformatics/btw197
Yanaka S, Moriwaki Y, Tsumoto K, Sugase K (2017) Elucidation of potential sites for antibody engineering by fluctuation editing. Sci Rep 7:9597. https://doi.org/10.1038/s41598-017-10246-9
Briney BS, Willis JR, Crowe JE (2012) Location and length distribution of somatic hypermutation-associated DNA insertions and deletions reveals regions of antibody structural plasticity. Genes Immun 13:523–529. https://doi.org/10.1038/gene.2012.28
Wedemayer GJ, Patten PA, Wang LH et al (1997) Structural insights into the evolution of an antibody combining site. Science 276:1665–1669. https://doi.org/10.1126/science.276.5319.1665
Zimmermann J, Oakman EL, Thorpe IF et al (2006) Antibody evolution constrains conformational heterogeneity by tailoring protein dynamics. Proc Natl Acad Sci 103:13722–13727. https://doi.org/10.1073/pnas.0603282103
Thorpe IF, Brooks CL (2007) Molecular evolution of affinity and flexibility in the immune system. Proc Natl Acad Sci 104:8821–8826. https://doi.org/10.1073/pnas.0610064104
Wong SE, Sellers BD, Jacobson MP (2011) Effects of somatic mutations on CDR loop flexibility during affinity maturation. Proteins Struct Funct Bioinforma 79:821–829. https://doi.org/10.1002/prot.22920
Schmidt AG, Xu H, Khan AR et al (2013) Preconfiguration of the antigen-binding site during affinity maturation of a broadly neutralizing influenza virus antibody. Proc Natl Acad Sci U S A 110:264–269. https://doi.org/10.1073/pnas.1218256109
Li T, Tracka MB, Uddin S et al (2015) Rigidity emerges during antibody evolution in three distinct antibody systems: evidence from QSFR analysis of fab fragments. PLoS Comput Biol 11:e1004327. https://doi.org/10.1371/journal.pcbi.1004327
Furukawa K, Shirai H, Azuma T, Nakamura H (2001) A role of the third complementarity-determining region in the affinity maturation of an antibody. J Biol Chem 276:27622–27628. https://doi.org/10.1074/jbc.M102714200
James LC, Roversi P, Tawfik DS (2003) Antibody multispecificity mediated by conformational diversity. Science 299:1362–1367. https://doi.org/10.1126/science.1079731
Bradbury ARM, Sidhu S, Dübel S, McCafferty J (2011) Beyond natural antibodies: the power of in vitro display technologies. Nat Biotechnol 29:245–254. https://doi.org/10.1038/nbt.1791
Finlay WJJ, Almagro JC (2012) Natural and man-made V-gene repertoires for antibody discovery. Front Immunol 3:342
Gray AC, Sidhu SS, Chandrasekera PC et al (2016) Animal-friendly affinity reagents: replacing the needless in the haystack. Trends Biotechnol 34:960–969
Guntas G, Purbeck C, Kuhlman B (2010) Engineering a protein-protein interface using a computationally designed library. Proc Natl Acad Sci 107:19296–19301. https://doi.org/10.1073/pnas.1006528107
Barderas R, Desmet J, Timmerman P et al (2008) Affinity maturation of antibodies assisted by in silico modeling. Proc Natl Acad Sci U S A 105:9029–9034. https://doi.org/10.1073/pnas.0801221105
Koga N, Tatsumi-Koga R, Liu G et al (2012) Principles for designing ideal protein structures. Nature 491:222–227. https://doi.org/10.1038/nature11600
Marcos E, Basanta B, Chidyausiku TM et al (2017) Principles for designing proteins with cavities formed by curved β sheets. Science 355:201–206. https://doi.org/10.1126/science.aah7389
Moal IH, Moretti R, Baker D, Fernández-Recio J (2013) Scoring functions for protein-protein interactions. Curr Opin Struct Biol 23:862–867
Dunbrack RL (2002) Rotamer libraries in the 21st century. Curr Opin Struct Biol 12:431–440
Samish I, MacDermaid CM, Perez-Aguilar JM, Saven JG (2011) Theoretical and computational protein design. Annu Rev Phys Chem 62:129–149. https://doi.org/10.1146/annurev-physchem-032210-103509
Dahiyat BI, Gordon DB, Mayo SL (1997) Automated design of the surface positions of protein helices. Protein Sci 6:1333–1337. https://doi.org/10.1002/pro.5560060622
Dahiyat BI, Mayo SL (1997) Probing the role of packing specificity in protein design. Proc Natl Acad Sci U S A 94:10172–10177. https://doi.org/10.1073/pnas.94.19.10172
Su A, Mayo SL (1997) Coupling backbone flexibility and amino acid sequence selection in protein design. Protein Sci 6:1701–1707. https://doi.org/10.1002/pro.5560060810
Kuhlman B, Dantas G, Ireton GC et al (2003) Design of a novel globular protein fold with atomic-level accuracy. Science 302:1364–1368. https://doi.org/10.1126/science.1089427
Selzer T, Albeck S, Schreiber G (2000) Rational design of faster associating and tighter binding protein complexes. Nat Struct Biol 7:537–541. https://doi.org/10.1038/76744
Marvin JS, Lowman HB (2003) Redesigning an antibody fragment for faster association with its antigen. Biochemistry 42:7077–7083. https://doi.org/10.1021/bi026947q
Sammond DW, Eletr ZM, Purbeck C et al (2007) Structure-based protocol for identifying mutations that enhance protein-protein binding affinities. J Mol Biol 371:1392–1404. https://doi.org/10.1016/j.jmb.2007.05.096
Filchtinski D, Sharabi O, Rüppel A et al (2010) What makes Ras an efficient molecular switch: a computational, biophysical, and structural study of Ras-GDP interactions with mutants of Raf. J Mol Biol 399:422–435. https://doi.org/10.1016/j.jmb.2010.03.046
Moult J, Pedersen JT, Judson R, Fidelis K (1995) A large-scale experiment to assess protein structure prediction methods. Proteins Struct Funct Bioinforma 23:ii–iv
Janin J, Henrick K, Moult J et al (2003) CAPRI: a critical assessment of PRedicted interactions. Proteins Struct Funct Genet 52:2–9. https://doi.org/10.1002/prot.10381
Michino M, Abola E, GPCR Dock 2008 Participants et al (2009) Community-wide assessment of GPCR structure modelling and ligand docking: GPCR Dock 2008. Nat Rev Drug Discov 8:455–463. https://doi.org/10.1038/nrd2877
Radivojac P, Clark WT, Oron TR et al (2013) A large-scale evaluation of computational protein function prediction. Nat Methods 10:221–227. https://doi.org/10.1038/nmeth.2340
Almagro JC, Teplyakov A, Luo J et al (2014) Second antibody modeling assessment (AMA-II). Proteins Struct Funct Bioinforma 82:1553–1562
Lensink MF, Moal IH, Bates PA et al (2014) Blind prediction of interfacial water positions in CAPRI. Proteins Struct Funct Bioinforma 82:620–632. https://doi.org/10.1002/prot.24439
Lensink MF, Velankar S, Kryshtafovych A et al (2016) Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: a CASP-CAPRI experiment. Proteins Struct Funct Bioinforma 84(Suppl 1):323–348. https://doi.org/10.1002/prot.25007
Fleishman SJ, Whitehead TA, Strauch EM et al (2011) Community-wide assessment of protein-interface modeling suggests improvements to design methodology. J Mol Biol 414:289–302. https://doi.org/10.1016/j.jmb.2011.09.031
Moretti R, Fleishman SJ, Agius R et al (2013) Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions. Proteins Struct Funct Bioinforma 81:1980–1987. https://doi.org/10.1002/prot.24356
Fleishman SJ, Whitehead TA, Ekiert DC et al (2011) Computational design of proteins targeting the conserved stem region of influenza hemagglutinin. Science 332:816–821. https://doi.org/10.1126/science.1202617
Karanicolas J, Corn JE, Chen I et al (2011) A de novo protein binding pair by computational design and directed evolution. Mol Cell 42:250–260. https://doi.org/10.1016/j.molcel.2011.03.010
Stranges PB, Machius M, Miley MJ et al (2011) Computational design of a symmetric homodimer using beta-strand assembly. Proc Natl Acad Sci U S A 108:20562–20567. https://doi.org/10.1073/pnas.1115124108
Sammond DW, Bosch DE, Butterfoss GL et al (2011) Computational design of the sequence and structure of a protein-binding peptide. J Am Chem Soc 133:4190–4192. https://doi.org/10.1021/ja110296z
Whitehead TA, Chevalier A, Song Y et al (2012) Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing. Nat Biotechnol 30:543–548. https://doi.org/10.1038/nbt.2214
Der BS, MacHius M, Miley MJ et al (2012) Metal-mediated affinity and orientation specificity in a computationally designed protein homodimer. J Am Chem Soc 134:375–385. https://doi.org/10.1021/ja208015j
Procko E, Hedman R, Hamilton K et al (2013) Computational design of a protein-based enzyme inhibitor. J Mol Biol 425:3563–3575. https://doi.org/10.1016/j.jmb.2013.06.035
Stranges PB, Kuhlman B (2013) A comparison of successful and failed protein interface designs highlights the challenges of designing buried hydrogen bonds. Protein Sci 22:74–82. https://doi.org/10.1002/pro.2187
Das R, Baker D (2008) Macromolecular Modeling with Rosetta. Annu Rev Biochem 77:363–382. https://doi.org/10.1146/annurev.biochem.77.062906.171838
Guerois R, Nielsen J, Serrano L (2002) Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. J Mol Biol 320:369–387. https://doi.org/10.1016/S0022-2836(02)00442-4
Gainza P, Roberts KE, Georgiev I et al (2013) OSPREY: protein design with ensembles, flexibility, and provable algorithms. Methods Enzymol 523:87–107. https://doi.org/10.1016/B978-0-12-394292-0.00005-9
Pantazes RJ, Grisewood MJ, Li T et al (2015) The iterative protein redesign and optimization (IPRO) suite of programs. J Comput Chem 36:251–263. https://doi.org/10.1002/jcc.23796
Jacobson MP, Pincus DL, Rapp CS et al (2004) A hierarchical approach to all-atom protein loop prediction. Proteins Struct Funct Genet 55:351–367. https://doi.org/10.1002/prot.10613
Gray JJ, Moughon S, Wang C et al (2003) Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations. J Mol Biol 331:281–299. https://doi.org/10.1016/S0022-2836(03)00670-3
Kortemme T, Joachimiak LA, Bullock AN et al (2004) Computational redesign of protein-protein interaction specificity. Nat Struct Mol Biol 11:371–379. https://doi.org/10.1038/nsmb749
Rohl CA, Strauss CE, Misura KM, Baker D (2004) Protein structure prediction using Rosetta. Methods Enzymol 383:66–93. https://doi.org/10.1016/S0076-6879(04)83004-0
Tinberg CE, Khare SD, Dou J et al (2013) Computational design of ligand-binding proteins with high affinity and selectivity. Nature 501:212–216. https://doi.org/10.1038/nature12443
Chaudhury S, Lyskov S, Gray JJ (2010) PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta. Bioinformatics 26:689–691
Fleishman SJ, Leaver-Fay A, Corn JE et al (2011) RosettaScripts: a scripting language interface to the Rosetta macromolecular modeling suite. PLoS One 6:e20161. https://doi.org/10.1371/journal.pone.0020161
Adolf-Bryfogle J, Dunbrack RL (2013) The PyRosetta toolkit: a graphical user interface for the Rosetta software suite. PLoS One 8:e66856. https://doi.org/10.1371/journal.pone.0066856
Lyskov S, Chou FC, Conchúir SÓ et al (2013) Serverification of molecular modeling applications: the Rosetta online server that includes everyone (ROSIE). PLoS One 8:e63906. https://doi.org/10.1371/journal.pone.0063906
Der BS, Kluwe C, Miklos AE et al (2013) Alternative computational protocols for supercharging protein surfaces for reversible unfolding and retention of stability. PLoS One 8:e64363. https://doi.org/10.1371/journal.pone.0064363
Willis JR, Sapparapu G, Murrell S et al (2015) Redesigned HIV antibodies exhibit enhanced neutralizing potency and breadth. J Clin Invest 125:2523–2531. https://doi.org/10.1172/JCI80693
Ollikainen N, Smith CA, Fraser JS, Kortemme T (2013) Flexible backbone sampling methods to model and design protein alternative conformations. Methods Enzymol 523:61–85. https://doi.org/10.1016/B978-0-12-394292-0.00004-7
Babor M, Mandell DJ, Kortemme T (2011) Assessment of flexible backbone protein design methods for sequence library prediction in the therapeutic antibody Herceptin-HER2 interface. Protein Sci 20:1082–1089. https://doi.org/10.1002/pro.632
Jackson EL, Ollikainen N, Covert AW et al (2013) Amino-acid site variability among natural and designed proteins. PeerJ 1:e211. https://doi.org/10.7717/peerj.211
Vajda S, Hall DR, Kozakov D (2013) Sampling and scoring: a marriage made in heaven. Proteins Struct Funct Bioinforma 81:1874–1884
Krivov GG, Shapovalov MV, Dunbrack RL (2009) Improved prediction of protein side-chain conformations with SCWRL4. Proteins Struct Funct Bioinforma 77:778–795. https://doi.org/10.1002/prot.22488
Liang S, Zheng D, Zhang C, Standley DM (2011) Fast and accurate prediction of protein side-chain conformations. Bioinformatics 27:2913–2914. https://doi.org/10.1093/bioinformatics/btr482
Miao Z, Cao Y, Jiang T (2011) RASP: rapid modeling of protein side chain conformations. Bioinformatics 27:3117–3122. https://doi.org/10.1093/bioinformatics/btr538
Nagata K, Randall A, Baldi P (2012) SIDEpro: a novel machine learning approach for the fast and accurate prediction of side-chain conformations. Proteins 80:142–153. https://doi.org/10.1002/prot.23170
Sulea T, Vivcharuk V, Corbeil CR et al (2016) Assessment of solvated interaction energy function for ranking antibody-antigen binding affinities. J Chem Inf Model 56:1292–1303. https://doi.org/10.1021/acs.jcim.6b00043
Poosarla VG, Li T, Goh BC et al (2017) Computational de novo design of antibodies binding to a peptide with high affinity. Biotechnol Bioeng 114:1331–1342. https://doi.org/10.1002/bit.26244
Entzminger KC, Hyun J, Pantazes RJ et al (2017) De novo design of antibody complementarity determining regions binding a FLAG tetra-peptide. Sci Rep 7:10295. https://doi.org/10.1038/s41598-017-10737-9
Baran D, Pszolla MG, Lapidoth GD et al (2017) Principles for computational design of binding antibodies. Proc Natl Acad Sci U S A 114:10900–10905. https://doi.org/10.1073/pnas.1707171114
Fukunaga A, Tsumoto K (2013) Improving the affinity of an antibody for its antigen via long-range electrostatic interactions. Protein Eng Des Sel 26:773–780. https://doi.org/10.1093/protein/gzt053
MacKerell AD, Bashford D, Bellott M et al (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B 102:3586–3616. https://doi.org/10.1021/jp973084f
Kangas E, Tidor B (1998) Optimizing electrostatic affinity in ligand-receptor binding: theory, computation, and ligand properties. J Chem Phys 109:7522–7545. https://doi.org/10.1063/1.477375
Brooks BR, Bruccoleri RE, Olafson BD et al (1983) CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J Comput Chem 4:187–217. https://doi.org/10.1002/jcc.540040211
Looger LL, Hellinga HW (2001) Generalized dead-end elimination algorithms make large-scale protein side-chain structure prediction tractable: implications for protein design and structural genomics. J Mol Biol 307:429–445. https://doi.org/10.1006/jmbi.2000.4424
Kiel C, Selzer T, Shaul Y et al (2004) Electrostatically optimized Ras-binding Ral guanine dissociation stimulator mutants increase the rate of association by stabilizing the encounter complex. Proc Natl Acad Sci U S A 101:9223–9228. https://doi.org/10.1073/pnas.0401160101
Bogan AA, Thorn KS (1998) Anatomy of hot spots in protein interfaces. J Mol Biol 280:1–9. https://doi.org/10.1006/jmbi.1998.1843
Moreira IS, Fernandes PA, Ramos MJ (2007) Hot spots—a review of the protein-protein interface determinant amino-acid residues. Proteins Struct Funct Genet 68:803–812
Moal IH, Fernández-Recio J (2012) SKEMPI: a structural kinetic and energetic database of mutant protein interactions and its use in empirical models. Bioinformatics 28:2600–2607. https://doi.org/10.1093/bioinformatics/bts489
Sirin S, Apgar JR, Bennett EM, Keating AE (2016) AB-bind: antibody binding mutational database for computational affinity predictions. Protein Sci 25:393–409. https://doi.org/10.1002/pro.2829
Akiba H, Tsumoto K (2015) Thermodynamics of antibody-antigen interaction revealed by mutation analysis of antibody variable regions. J Biochem 158:1–13. https://doi.org/10.1093/jb/mvv049
Oberlin M, Kroemer R, Mikol V et al (2012) Engineering protein therapeutics: predictive performances of a structure-based virtual affinity maturation protocol. J Chem Inf Model 52:2204–2214. https://doi.org/10.1021/ci3001474
Zhou H, Zhou Y (2002) Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Protein Sci 11:2714–2726. https://doi.org/10.1110/ps.0217002
Yang Y, Zhou Y (2008) Specific interactions for ab initio folding of protein terminal regions with secondary structures. Proteins Struct Funct Genet 72:793–803. https://doi.org/10.1002/prot.21968
DeBartolo J, Taipale M, Keating AE (2014) Genome-wide prediction and validation of peptides that bind human prosurvival Bcl-2 proteins. PLoS Comput Biol 10:e1003693. https://doi.org/10.1371/journal.pcbi.1003693
Pires DEV, Ascher DB (2016) mCSM-AB: a web server for predicting antibody-antigen affinity changes upon mutation with graph-based signatures. Nucleic Acids Res 44:W469–W473. https://doi.org/10.1093/nar/gkw458
Lapidoth GD, Baran D, Pszolla GM et al (2015) AbDesign: an algorithm for combinatorial backbone design guided by natural conformations and sequences. Proteins Struct Funct Bioinforma 83:1385–1406. https://doi.org/10.1002/prot.24779
Khatib F, Cooper S, Tyka MD et al (2011) Algorithm discovery by protein folding game players. Proc Natl Acad Sci 108:18949–18953. https://doi.org/10.1073/pnas.1115898108
Weitzner BD, Jeliazkov JR, Lyskov S et al (2017) Modeling and docking of antibody structures with Rosetta. Nat Protoc 12:401–416. https://doi.org/10.1038/nprot.2016.180
Weitzner BD, Kuroda D, Marze N et al (2014) Blind prediction performance of RosettaAntibody 3.0: grafting, relaxation, kinematic loop modeling, and full CDR optimization. Proteins Struct Funct Bioinforma 82:1611–1623. https://doi.org/10.1002/prot.24534
Clark AJ, Gindin T, Zhang B et al (2017) Free energy perturbation calculation of relative binding free energy between broadly neutralizing antibodies and the gp120 glycoprotein of HIV-1. J Mol Biol 429:930–947. https://doi.org/10.1016/j.jmb.2016.11.021
Wang L, Berne BJ, Friesner RA (2012) On achieving high accuracy and reliability in the calculation of relative protein-ligand binding affinities. Proc Natl Acad Sci U S A 109:1937–1942. https://doi.org/10.1073/pnas.1114017109
Gapsys V, Michielssens S, Seeliger D, de Groot BL (2016) Accurate and rigorous prediction of the changes in protein free energies in a large-scale mutation scan. Angew Chem Int Ed Engl 55:7364–7368. https://doi.org/10.1002/anie.201510054
Higo J, Kasahara K, Dasgupta B, Nakamura H (2017) Enhancement of canonical sampling by virtual-state transitions. J Chem Phys 146:44104. https://doi.org/10.1063/1.4974087
Jo S, Kim T, Iyer VG, Im W (2008) CHARMM-GUI: a web-based graphical user interface for CHARMM. J Comput Chem 29:1859–1865. https://doi.org/10.1002/jcc.20945
Asti L, Uguzzoni G, Marcatili P, Pagnani A (2016) Maximum-entropy models of sequenced immune repertoires predict antigen-antibody affinity. PLoS Comput Biol 12:e1004870. https://doi.org/10.1371/journal.pcbi.1004870
Wu X, Zhou T, Zhu J et al (2011) Focused evolution of HIV-1 neutralizing antibodies revealed by structures and deep sequencing. Science 333:1593–1602. https://doi.org/10.1126/science.1207532
Georgiou G, Ippolito GC, Beausang J et al (2014) The promise and challenge of high-throughput sequencing of the antibody repertoire. Nat Biotechnol 32:158–168. https://doi.org/10.1038/nbt.2782
Callan CG, Mora T, Walczak AM (2017) Repertoire sequencing and the statistical ensemble approach to adaptive immunity. Curr Opin Syst Biol 1:44–47. https://doi.org/10.1016/j.coisb.2016.12.014
Acknowledgment
D.K. was funded by the Japan Society for the Promotion of Science (grant number 17K18113) and by the Japanese Initiative for Progress of Research on Infectious Diseases for Global Epidemics (grant number JP18fm0208022h).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Kuroda, D., Tsumoto, K. (2018). Antibody Affinity Maturation by Computational Design. In: Nevoltris, D., Chames, P. (eds) Antibody Engineering. Methods in Molecular Biology, vol 1827. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8648-4_2
Download citation
DOI: https://doi.org/10.1007/978-1-4939-8648-4_2
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-8647-7
Online ISBN: 978-1-4939-8648-4
eBook Packages: Springer Protocols