Homology Modeling-Based in Silico Affinity Maturation Improves the Affinity of a Nanobody
Abstract
:1. Introduction
2. Results
2.1. Modeling and Preparation of Structure
2.2. Identifying Key Residue Positions for Affinity Maturation
2.3. ADAPT Cycle–multiple Mutants
2.4. Binding Affinity Validation
2.4.1. Purification of VHHs
2.4.2. SPR
2.4.3. qPCR
2.4.4. Indirect-ELISA by Lead Variant
3. Discussion
4. Materials and Methods
4.1. Homology Modeling
4.2. MD Simulation
4.3. Molecular Docking
4.4. Rational Mutation Hotspots Design Protocol
4.5. In Silico Affinity Maturation
4.6. Protein Expression and Purification
4.7. SPR
4.8. Thermal Stability Measurements
4.9. Indirect ELISA
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
Abbreviations
KD | Dissociation constant |
TM | Melting Temperature |
RMHDP | Rational Mutation Hotspots Design Protocol |
VHHs | Single domain antibodies |
CD47ext | CD47 extracellular domain |
mAbs | Monoclonal antibodies |
ScFvs | Single-chain variable-fragments |
ADAPT | Assisted Design of Antibody and Protein Therapeutics |
MD | Molecule Dynamics |
CDRs | Complementarity determining regions |
OD450 | Optical Densities at 450 nm |
aTTP | acquired Thrombotic Thrombocytopenic Purpura |
DOPE | Discrete Optimized Protein Energy |
MD | Molecular Dynamics |
BSA | Bovine Serum Albumin |
HRP | Horseradish Peroxidase |
NVT | Number of particles, Volume, Temperature |
NPT | Number of particles, Pressure, Temperature |
SPR | Surface Plasmon Resonance |
qPCR | quantitative Polymerase Chain Reaction |
References
- Barclay, A.N. Signal regulatory protein alpha (SIRPalpha)/CD47 interaction and function. Curr. Opin. Immunol. 2009, 21, 47–52. [Google Scholar] [CrossRef] [PubMed]
- Zhang, L.; Huang, H. Targeting the Cancer Biomarker CD47: A Review on the Diverse Mechanisms of the CD47 Pathway in Cancer Treatment. Anticancer Agents Med. Chem. 2016, 16, 658–667. [Google Scholar] [CrossRef] [PubMed]
- Ngo, M.; Han, A.; Lakatos, A.; Sahoo, D.; Hachey, S.J.; Weiskopf, K.; Beck, A.H.; Weissman, I.L.; Boiko, A.D. Antibody Therapy Targeting CD47 and CD271 Effectively Suppresses Melanoma Metastasis in Patient-Derived Xenografts. Cell Rep. 2016, 16, 1701–1716. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Liu, J.; Wang, L.; Zhao, F.; Tseng, S.; Narayanan, C.; Shura, L.; Willingham, S.; Howard, M.; Prohaska, S.; Volkmer, J.; et al. Pre-Clinical Development of a Humanized Anti-CD47 Antibody with Anti-Cancer Therapeutic Potential. PLoS ONE 2015, 10, e0137345. [Google Scholar] [CrossRef] [PubMed]
- Scully, M.; Cataland, S.R.; Peyvandi, F.; Coppo, P.; Knobl, P.; Kremer Hovinga, J.A.; Metjian, A.; de la Rubia, J.; Pavenski, K.; Callewaert, F.; et al. Caplacizumab Treatment for Acquired Thrombotic Thrombocytopenic Purpura. N. Engl. J. Med. 2019, 380, 335–346. [Google Scholar] [CrossRef] [PubMed]
- Allegra, A.; Innao, V.; Gerace, D.; Vaddinelli, D.; Allegra, A.G.; Musolino, C. Nanobodies and Cancer: Current Status and New Perspectives. Cancer Investig. 2018, 36, 221–237. [Google Scholar] [CrossRef] [PubMed]
- Kandalaft, H.; Hussack, G.; Aubry, A.; van Faassen, H.; Guan, Y.; Arbabi-Ghahroudi, M.; MacKenzie, R.; Logan, S.M.; Tanha, J. Targeting surface-layer proteins with single-domain antibodies: A potential therapeutic approach against Clostridium difficile-associated disease. Appl. Microbiol. Biotechnol. 2015, 99, 8549–8562. [Google Scholar] [CrossRef]
- McCafferty, J.; Schofield, D. Identification of optimal protein binders through the use of large genetically encoded display libraries. Curr. Opin. Chem. Biol. 2015, 26, 16–24. [Google Scholar] [CrossRef]
- Tiller, K.E.; Tessier, P.M. Advances in Antibody Design. Annu. Rev. Biomed. Eng. 2015, 17, 191–216. [Google Scholar] [CrossRef] [Green Version]
- Inoue, H.; Suganami, A.; Ishida, I.; Tamura, Y.; Maeda, Y. Affinity maturation of a CDR3-grafted VHH using in silico analysis and surface plasmon resonance. J. Biochem. 2013, 154, 325–332. [Google Scholar] [CrossRef]
- Park, S.G.; Lee, J.S.; Je, E.Y.; Kim, I.J.; Chung, J.H.; Choi, I.H. Affinity maturation of natural antibody using a chain shuffling technique and the expression of recombinant antibodies in Escherichia coli. Biochem. Biophys. Res. Commun. 2000, 275, 553–557. [Google Scholar] [CrossRef]
- Cumbers, S.J.; Williams, G.T.; Davies, S.L.; Grenfell, R.L.; Takeda, S.; Batista, F.D.; Sale, J.E.; Neuberger, M.S. Generation and iterative affinity maturation of antibodies in vitro using hypermutating B-cell lines. Nat. Biotechnol. 2002, 20, 1129–1134. [Google Scholar] [CrossRef]
- Sudha, G.; Nussinov, R.; Srinivasan, N. An overview of recent advances in structural bioinformatics of protein-protein interactions and a guide to their principles. Prog. Biophys. Mol. Biol. 2014, 116, 141–150. [Google Scholar] [CrossRef]
- Yugandhar, K.; Gromiha, M.M. Computational Approaches for Predicting Binding Partners, Interface Residues, and Binding Affinity of Protein–Protein Complexes. In Prediction of Protein Secondary Structure; Zhou, Y., Kloczkowski, A., Eds.; Springer New York: New York, NY, USA, 2017; pp. 237–253. [Google Scholar]
- Soler, M.A.; Fortuna, S.; de Marco, A.; Laio, A. Binding affinity prediction of nanobody-protein complexes by scoring of molecular dynamics trajectories. Phys. Chem. Chem. Phys. 2018, 20, 3438–3444. [Google Scholar] [CrossRef]
- Sirin, S.; Apgar, J.R.; Bennett, E.M.; Keating, A.E. AB-Bind: Antibody binding mutational database for computational affinity predictions. Protein Sci. 2016, 25, 393–409. [Google Scholar] [CrossRef]
- Pires, D.E.; Ascher, D.B. mCSM-AB: A web server for predicting antibody-antigen affinity changes upon mutation with graph-based signatures. Nucleic Acids Res. 2016, 44, W469–W473. [Google Scholar] [CrossRef]
- Kiyoshi, M.; Caaveiro, J.M.; Miura, E.; Nagatoishi, S.; Nakakido, M.; Soga, S.; Shirai, H.; Kawabata, S.; Tsumoto, K. 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 2014, 9, e87099. [Google Scholar] [CrossRef]
- Lin, S.G.; Ba, Z.; Du, Z.; Zhang, Y.; Hu, J.; Alt, F.W. Highly sensitive and unbiased approach for elucidating antibody repertoires. Proc. Natl. Acad. Sci. USA 2016, 113, 7846–7851. [Google Scholar] [CrossRef] [Green Version]
- Fennell, B.J.; McDonnell, B.; Tam, A.S.; Chang, L.; Steven, J.; Broadbent, I.D.; Gao, H.; Kieras, E.; Alley, J.; Luxenberg, D.; et al. CDR-restricted engineering of native human scFvs creates highly stable and soluble bifunctional antibodies for subcutaneous delivery. MABS 2013, 5, 882–895. [Google Scholar] [CrossRef] [Green Version]
- Muyldermans, S. Nanobodies: Natural single-domain antibodies. Annu. Rev. Biochem. 2013, 82, 775–797. [Google Scholar] [CrossRef]
- Hamers-Casterman, C.; Atarhouch, T.; Muyldermans, S.; Robinson, G.; Hamers, C.; Songa, E.B.; Bendahman, N.; Hamers, R. Naturally occurring antibodies devoid of light chains. Nature 1993, 363, 446–448. [Google Scholar] [CrossRef]
- Zavrtanik, U.; Lukan, J.; Loris, R.; Lah, J.; Hadži, S. Structural Basis of Epitope Recognition by Heavy-Chain Camelid Antibodies. J. Mol. Biol. 2018, 430, 4369–4386. [Google Scholar] [CrossRef]
- Mitchell, L.S.; Colwell, L.J. Analysis of nanobody paratopes reveals greater diversity than classical antibodies. Protein Eng. Des. Sel. 2018, 31, 267–275. [Google Scholar] [CrossRef] [Green Version]
- Yau, K.Y.; Dubuc, G.; Li, S.; Hirama, T.; Mackenzie, C.R.; Jermutus, L.; Hall, J.C.; Tanha, J. Affinity maturation of a V(H)H by mutational hotspot randomization. J. Immunol. Methods 2005, 297, 213–224. [Google Scholar] [CrossRef]
- Sulea, T.; Hussack, G.; Ryan, S.; Tanha, J.; Purisima, E.O. Application of Assisted Design of Antibody and Protein Therapeutics (ADAPT) improves efficacy of a Clostridium difficile toxin A single-domain antibody. Sci. Rep. 2018, 8, 2260. [Google Scholar] [CrossRef] [Green Version]
- Hussack, G.; Riazi, A.; Ryan, S.; van Faassen, H.; MacKenzie, R.; Tanha, J.; Arbabi-Ghahroudi, M. Protease-resistant single-domain antibodies inhibit Campylobacter jejuni motility. Protein Eng. Des. Sel. 2014, 27, 191–198. [Google Scholar] [CrossRef]
- McMahon, C.; Baier, A.S.; Pascolutti, R.; Wegrecki, M.; Zheng, S.; Ong, J.X.; Erlandson, S.C.; Hilger, D.; Rasmussen, S.G.F.; Ring, A.M.; et al. Yeast surface display platform for rapid discovery of conformationally selective nanobodies. Nat. Struct. Mol. Biol. 2018, 25, 289–296. [Google Scholar] [CrossRef] [Green Version]
- Uchański, T.; Zögg, T.; Yin, J.; Yuan, D.; Wohlkönig, A.; Fischer, B.; Rosenbaum, D.M.; Kobilka, B.K.; Pardon, E.; Steyaert, J. An improved yeast surface display platform for the screening of nanobody immune libraries. Sci. Rep. 2019, 9, 382. [Google Scholar] [CrossRef]
- Vivcharuk, V.; Baardsnes, J.; Deprez, C.; Sulea, T.; Jaramillo, M.; Corbeil, C.R.; Mullick, A.; Magoon, J.; Marcil, A.; Durocher, Y.; et al. Assisted Design of Antibody and Protein Therapeutics (ADAPT). PLoS ONE 2017, 12, e0181490. [Google Scholar] [CrossRef]
- Soler, M.A.; de Marco, A.; Fortuna, S. Molecular dynamics simulations and docking enable to explore the biophysical factors controlling the yields of engineered nanobodies. Sci. Rep. 2016, 6, 34869. [Google Scholar] [CrossRef]
- Russo, A.; Scognamiglio, P.L.; Hong Enriquez, R.P.; Santambrogio, C.; Grandori, R.; Marasco, D.; Giordano, A.; Scoles, G.; Fortuna, S. In Silico Generation of Peptides by Replica Exchange Monte Carlo: Docking-Based Optimization of Maltose-Binding-Protein Ligands. PLoS ONE 2015, 10, e0133571. [Google Scholar] [CrossRef]
- Soler, M.A.; Rodriguez, A.; Russo, A.; Adedeji, A.F.; Dongmo Foumthuim, C.J.; Cantarutti, C.; Ambrosetti, E.; Casalis, L.; Corazza, A.; Scoles, G.; et al. Computational design of cyclic peptides for the customized oriented immobilization of globular proteins. Phys. Chem. Chem. Phys. 2017, 19, 2740–2748. [Google Scholar] [CrossRef]
- Perricone, U.; Gulotta, M.R.; Lombino, J.; Parrino, B.; Cascioferro, S.; Diana, P.; Cirrincione, G.; Padova, A. An overview of recent molecular dynamics applications as medicinal chemistry tools for the undruggable site challenge. Medchemcomm. 2018, 9, 920–936. [Google Scholar] [CrossRef] [Green Version]
- Pietsch, E.C.; Dong, J.; Cardoso, R.; Zhang, X.; Chin, D.; Hawkins, R.; Dinh, T.; Zhou, M.; Strake, B.; Feng, P.H.; et al. Anti-leukemic activity and tolerability of anti-human CD47 monoclonal antibodies. Blood Cancer J. 2017, 7, e536. [Google Scholar] [CrossRef]
- Gonzalez-Munoz, A.; Bokma, E.; O’Shea, D.; Minton, K.; Strain, M.; Vousden, K.; Rossant, C.; Jermutus, L.; Minter, R. Tailored amino acid diversity for the evolution of antibody affinity. MABS 2012, 4, 664–672. [Google Scholar] [CrossRef] [Green Version]
- Akiba, H.; Tsumoto, K. Thermodynamics of antibody-antigen interaction revealed by mutation analysis of antibody variable regions. J. Biochem. 2015, 158, 1–13. [Google Scholar] [CrossRef]
- Jolly, C.J.; Wagner, S.D.; Rada, C.; Klix, N.; Milstein, C.; Neuberger, M.S. The targeting of somatic hypermutation. Semin. Immunol. 1996, 8, 159–168. [Google Scholar] [CrossRef]
- Goyenechea, B.; Milstein, C. Modifying the sequence of an immunoglobulin V-gene alters the resulting pattern of hypermutation. Proc. Natl. Acad. Sci. USA 1996, 93, 13979–13984. [Google Scholar] [CrossRef] [Green Version]
- Peyvandi, F.; Scully, M.; Kremer Hovinga, J.A.; Knobl, P.; Cataland, S.; De Beuf, K.; Callewaert, F.; De Winter, H.; Zeldin, R.K. Caplacizumab reduces the frequency of major thromboembolic events, exacerbations and death in patients with acquired thrombotic thrombocytopenic purpura. J. Thromb. Haemost. 2017, 15, 1448–1452. [Google Scholar] [CrossRef]
- Siontorou, C.G. Nanobodies as novel agents for disease diagnosis and therapy. Int. J. Nanomed. 2013, 8, 4215–4227. [Google Scholar] [CrossRef]
- Wesolowski, J.; Alzogaray, V.; Reyelt, J.; Unger, M.; Juarez, K.; Urrutia, M.; Cauerhff, A.; Danquah, W.; Rissiek, B.; Scheuplein, F.; et al. Single domain antibodies: Promising experimental and therapeutic tools in infection and immunity. Med. Microbiol. Immunol. 2009, 198, 157–174. [Google Scholar] [CrossRef]
- Stewart, C.S.; MacKenzie, C.R.; Hall, J.C. Isolation, characterization and pentamerization of alpha-cobrotoxin specific single-domain antibodies from a naive phage display library: Preliminary findings for antivenom development. Toxicon 2007, 49, 699–709. [Google Scholar] [CrossRef]
- Wagner, H.J.; Wehrle, S.; Weiss, E.; Cavallari, M.; Weber, W. A Two-Step Approach for the Design and Generation of Nanobodies. Int. J. Mol. Sci. 2018, 19, 3444. [Google Scholar] [CrossRef]
- Qiao, C.X.; Lv, M.; Li, X.Y.; Geng, J.; Li, Y.; Zhang, J.Y.; Lin, Z.; Feng, J.N.; Shen, B.F. Affinity maturation of antiHER2 monoclonal antibody MIL5 using an epitope-specific synthetic phage library by computational design. J. Biomol. Struct. Dyn. 2013, 31, 511–521. [Google Scholar] [CrossRef]
- Yu, X.; Qu, L.; Bigner, D.D.; Chandramohan, V. Selection of novel affinity-matured human chondroitin sulfate proteoglycan 4 antibody fragments by yeast display. Protein Eng. Des. Sel. 2017, 30, 639–647. [Google Scholar] [CrossRef]
- Liu, W.; Song, H.; Chen, Q.; Yu, J.; Xian, M.; Nian, R.; Feng, D. Recent advances in the selection and identification of antigen-specific nanobodies. Mol. Immunol. 2018, 96, 37–47. [Google Scholar] [CrossRef]
- Wang, Y.; Keck, Z.Y.; Saha, A.; Xia, J.; Conrad, F.; Lou, J.; Eckart, M.; Marks, J.D.; Foung, S.K. Affinity maturation to improve human monoclonal antibody neutralization potency and breadth against hepatitis C virus. J. Biol. Chem. 2011, 286, 44218–44233. [Google Scholar] [CrossRef]
- Yamashita, T. Toward rational antibody design: Recent advancements in molecular dynamics simulations. Int. Immunol. 2018, 30, 133–140. [Google Scholar] [CrossRef]
- Tina, K.G.; Bhadra, R.; Srinivasan, N. PIC: Protein Interactions Calculator. Nucleic Acids Res. 2007, 35, W473–W476. [Google Scholar] [CrossRef] [Green Version]
- Burley, S.; Petsko, G. Aromatic-aromatic interaction: A mechanism of protein structure stabilization. Science 1985, 229, 23–28. [Google Scholar] [CrossRef]
- Rege, N.K.; Wickramasinghe, N.P.; Tustan, A.N.; Phillips, N.F.B.; Yee, V.C.; Ismail-Beigi, F.; Weiss, M.A. Structure-based stabilization of insulin as a therapeutic protein assembly via enhanced aromatic-aromatic interactions. J. Biol. Chem. 2018, 293, 10895–10910. [Google Scholar] [CrossRef]
- Gray, H.B.; Winkler, J.R. Hole hopping through tyrosine/tryptophan chains protects proteins from oxidative damage. Proc. Natl. Acad. Sci. USA 2015, 112, 10920–10925. [Google Scholar] [CrossRef] [Green Version]
- Kunz, P.; Zinner, K.; Mücke, N.; Bartoschik, T.; Muyldermans, S.; Hoheisel, J.D. The structural basis of nanobody unfolding reversibility and thermoresistance. Sci. Rep. 2018, 8, 7934. [Google Scholar] [CrossRef]
- Liu, X.; Taylor, R.D.; Griffin, L.; Coker, S.F.; Adams, R.; Ceska, T.; Shi, J.; Lawson, A.D.; Baker, T. Computational design of an epitope-specific Keap1 binding antibody using hotspot residues grafting and CDR loop swapping. Sci. Rep. 2017, 7, 41306. [Google Scholar] [CrossRef] [Green Version]
- Kuroda, D.; Shirai, H.; Jacobson, M.P.; Nakamura, H. Computer-aided antibody design. Protein Eng. Des. Sel. 2012, 25, 507–521. [Google Scholar] [CrossRef]
- Duhoo, Y.; Roche, J.; Trinh, T.T.N.; Desmyter, A.; Gaubert, A.; Kellenberger, C.; Cambillau, C.; Roussel, A.; Leone, P. Camelid nanobodies used as crystallization chaperones for different constructs of PorM, a component of the type IX secretion system from Porphyromonas gingivalis. Acta Crystallogr. F Struct. Biol. Commun. 2017, 73, 286–293. [Google Scholar] [CrossRef]
- Kromann-Hansen, T.; Oldenburg, E.; Yung, K.W.; Ghassabeh, G.H.; Muyldermans, S.; Declerck, P.J.; Huang, M.; Andreasen, P.A.; Ngo, J.C. A Camelid-derived Antibody Fragment Targeting the Active Site of a Serine Protease Balances between Inhibitor and Substrate Behavior. J. Biol. Chem. 2016, 291, 15156–15168. [Google Scholar] [CrossRef] [Green Version]
- Van den Abbeele, A.; De Clercq, S.; De Ganck, A.; De Corte, V.; Van Loo, B.; Soror, S.H.; Srinivasan, V.; Steyaert, J.; Vandekerckhove, J.; Gettemans, J. A llama-derived gelsolin single-domain antibody blocks gelsolin-G-actin interaction. Cell Mol. Life Sci. 2010, 67, 1519–1535. [Google Scholar] [CrossRef]
- Pronk, S.; Pall, S.; Schulz, R.; Larsson, P.; Bjelkmar, P.; Apostolov, R.; Shirts, M.R.; Smith, J.C.; Kasson, P.M.; van der Spoel, D.; et al. GROMACS 4.5: A high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 2013, 29, 845–854. [Google Scholar] [CrossRef]
- Lindorff-Larsen, K.; Piana, S.; Palmo, K.; Maragakis, P.; Klepeis, J.L.; Dror, R.O.; Shaw, D.E. Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins 2010, 78, 1950–1958. [Google Scholar] [CrossRef] [Green Version]
- Bussi, G.; Donadio, D.; Parrinello, M. Canonical sampling through velocity rescaling. J. Chem. Phys. 2007, 126, 014101. [Google Scholar] [CrossRef] [Green Version]
- Aragones, J.L.; Vega, C. Plastic crystal phases of simple water models. J. Chem. Phys. 2009, 130, 244504. [Google Scholar] [CrossRef] [Green Version]
- Hess, B. P-LINCS: A Parallel Linear Constraint Solver for Molecular Simulation. J. Chem. Theory Comput. 2008, 4, 116–122. [Google Scholar] [CrossRef]
- Trott, O.; Olson, A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 2010, 31, 455–461. [Google Scholar] [CrossRef]
- Hatherley, D.; Lea, S.M.; Johnson, S.; Barclay, A.N. Polymorphisms in the human inhibitory signal-regulatory protein alpha do not affect binding to its ligand CD47. J. Biol. Chem. 2014, 289, 10024–10028. [Google Scholar] [CrossRef]
- Betz, A.G.; Neuberger, M.S.; Milstein, C. Discriminating intrinsic and antigen-selected mutational hotspots in immunoglobulin V genes. Immunol. Today 1993, 14, 405–411. [Google Scholar] [CrossRef]
- Rogozin, I.B.; Kolchanov, N.A. Somatic hypermutagenesis in immunoglobulin genes. II. Influence of neighbouring base sequences on mutagenesis. Biochim. Biophys. Acta 1992, 1171, 11–18. [Google Scholar] [CrossRef]
- Guex, N.; Peitsch, M.C. SWISS-MODEL and the Swiss-PdbViewer: An environment for comparative protein modeling. Electrophoresis 1997, 18, 2714–2723. [Google Scholar] [CrossRef]
- Breslin, C.; Hornyak, P.; Ridley, A.; Rulten, S.L.; Hanzlikova, H.; Oliver, A.W.; Caldecott, K.W. The XRCC1 phosphate-binding pocket binds poly (ADP-ribose) and is required for XRCC1 function. Nucleic Acids Res. 2015, 43, 6934–6944. [Google Scholar] [CrossRef]
Interface Residues Analysis | AGY/RGYW | ||
---|---|---|---|
32 | GLU (E) | 34 | SER (S) |
35 | GLN (Q) | 55 | SER (S) |
36 | ASN (N) | 56 | SER (S) |
54 | ILE (I) | 58 | VAL (V) |
55 | SER (S) | 59 | VAL (V) |
57 | ARG (R) | 100 | ALA (A) |
58 | VAL (V) | 101 | ALA (A) |
60 | GLU (E) | 107 | GLY (G) |
61 | CYS (C) | 108 | THR (T) |
62 | TRP (W) | 109 | SER (S) |
105–113 | - | 110 | PHE (F) |
Res | A | R | N | D | Q | E | G | H | I | L | K | M | F | S | T | W | Y | V |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
S55 | −2.7 | −1.7 | −2.2 | −2.3 | −2.1 | −1.8 | −1.7 | |||||||||||
V58 | −1.7 | −2.1 | −1.9 | −1.7 | −1.7 | −1.7 | −1.7 | |||||||||||
G107 | −2.1 | −1.9 | −1.8 | −1.7 | −2.2 | −1.8 | −1.8 | −1.8 | −1.7 | −1.9 | −2.1 | −2.0 | −1.9 | −1.7 | ||||
T108 | −2.3 | −2.1 | −1.7 | −1.9 | −1.8 | −1.7 | ||||||||||||
S109 | −1.6 | −2.0 | −2.3 | −2.0 | −1.6 | −1.9 | −1.7 | −2.0 | ||||||||||
F110 | −2.0 | −2.2 | −2.0 | −1.7 | −1.6 | −1.7 | −1.9 | −1.6 |
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Cheng, X.; Wang, J.; Kang, G.; Hu, M.; Yuan, B.; Zhang, Y.; Huang, H. Homology Modeling-Based in Silico Affinity Maturation Improves the Affinity of a Nanobody. Int. J. Mol. Sci. 2019, 20, 4187. https://doi.org/10.3390/ijms20174187
Cheng X, Wang J, Kang G, Hu M, Yuan B, Zhang Y, Huang H. Homology Modeling-Based in Silico Affinity Maturation Improves the Affinity of a Nanobody. International Journal of Molecular Sciences. 2019; 20(17):4187. https://doi.org/10.3390/ijms20174187
Chicago/Turabian StyleCheng, Xin, Jiewen Wang, Guangbo Kang, Min Hu, Bo Yuan, Yingtian Zhang, and He Huang. 2019. "Homology Modeling-Based in Silico Affinity Maturation Improves the Affinity of a Nanobody" International Journal of Molecular Sciences 20, no. 17: 4187. https://doi.org/10.3390/ijms20174187