Cited by 342Monoclonal and Polyclonal Antibodies ResearchProtein Structure and Dynamicsvaccines and immunoinformatics approaches

The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.

Computational design of antibody-affinity improvement beyond in vivo maturation

Open access

Lippow, Shaun M, Wittrup, K Dane, Tidor, Bruce·2007·Nature Biotechnology

DOI 10.1038/nbt1336PMC PMC2803018

Open this paper in CoChat — free account

Abstract

Antibodies are used extensively in diagnostics and as therapeutic agents. Achieving high-affinity binding is important for expanding detection limits, extending dissociation half-times, decreasing drug dosages and increasing drug efficacy. However, antibody-affinity maturation in vivo often fails to produce antibody drugs of the targeted potency, making further affinity maturation in vitro by directed evolution or computational design necessary. Here we present an iterative computational design procedure that focuses on electrostatic binding contributions and single mutants. By combining multiple designed mutations, a tenfold affinity improvement to 52 pM was engineered into the anti-epidermal growth factor receptor drug cetuximab (Erbitux), and a 140-fold improvement in affinity to 30 pM was obtained for the anti-lysozyme model antibody D44.1. The generality of the methods was further demonstrated through identification of known affinity-enhancing mutations in the therapeutic antibody bevacizumab (Avastin) and the model anti-fluorescein antibody 4-4-20. These results demonstrate computational capabilities for enhancing and accelerating the development of protein reagents and therapeutics.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1038/nbt1336

Cites

30 of 30 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

1 retracted

Computational Design of a Biologically Active Enzyme
Dwyer, M. A., Looger, L. L., & Hellinga, H. W. (2004) · Science · doi:10.1126/science.1098432 · Cited by 295
Retracted
Design of a Novel Globular Protein Fold with Atomic-Level Accuracy
Kuhlman, B. A., Dantas, G. A., Ireton, G. C., Varani, G., Stoddard, B. L., & Baker, D. A. (2003) · Science · doi:10.1126/science.1089427 · Cited by 1,666
Humanization of an anti-vascular endothelial growth factor monoclonal antibody for the therapy of solid tumors and other disorders.
Presta, L. G., Chen, H., O'Connor, S. J., Chisholm, V., Meng, Y. G., KRUMMEN, L. A., Winkler, M., & Ferrara, N. (1997) · PubMed · Cited by 1,596
De Novo Protein Design: Fully Automated Sequence Selection
Dahiyat, B. I., & Mayo, S. L. (1997) · Science · doi:10.1126/science.278.5335.82 · Cited by 1,156
Directed evolution of antibody fragments with monovalent femtomolar antigen-binding affinity
Boder, E. T., Midelfort, K. S., & Wittrup, K. D. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.170297297 · Cited by 673
Self-citation
Computational design of receptor and sensor proteins with novel functions
Looger, L. L., Dwyer, M. A., Smith, J. J., & Hellinga, H. W. (2003) · Nature · doi:10.1038/nature01556 · Cited by 668
Biological effects in vitro of monoclonal antibodies to human epidermal growth factor receptors.
Sato, J. D., Kawamoto, T., Le, A. D., Mendelsohn, J. M., Polikoff, J., & SATO, G. H. (1983) · PubMed · Cited by 458
Selection and analysis of an optimized anti-VEGF antibody: crystal structure of an affinity-matured fab in complex with antigen 1 1Edited by I. A. Wilson
Chen, Y., Wiesmann, C., Fuh, G., Li, B., Christinger, H. W., McKay, P., de Vos, A. M., & Lowman, H. B. (1999) · Journal of Molecular Biology · doi:10.1006/jmbi.1999.3192 · Cited by 449
Kinetic and affinity limits on antibodies produced during immune responses.
Foote, J., & Eisen, H. N. (1995) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.92.5.1254 · Cited by 375
Computational Thermostabilization of an Enzyme
Korkegian, A., Black, M. E., Baker, D. A., & Stoddard, B. L. (2005) · Science · doi:10.1126/science.1107387 · Cited by 360

Flagged first, then by citations

Show all 30 references

Sorted by citations

Items 101–200 of 340

Toward real-world automated antibody design with combinatorial Bayesian optimization
Khan, A. A., Cowen-Rivers, A. I., Grosnit, A., Deik, D.-G.-X., Robert, P. A., Greiff, V., Smorodina, E., Rawat, P., Akbar, R., Dreczkowski, K., et al. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100374 · Cited by 72
Affinity- and Specificity-Enhancing Mutations Are Frequent in Multispecific Interactions between TIMP2 and MMPs
Sharabi, O., Shirian, J., Grossman, M., Lebendiker, M., Sagi, I., & Shifman, J. M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0093712 · Cited by 47
Rigidity Emerges during Antibody Evolution in Three Distinct Antibody Systems: Evidence from QSFR Analysis of Fab Fragments
Li, T., Tracka, M. B., Uddin, S., Casas‐Finet, J. R., Jacobs, D. J., & Livesay, D. R. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004327 · Cited by 46
Specific Increase in Potency via Structure-Based Design of a TCR
Malecek, K., Grigoryan, A., Shi, Z. L., Gu, W. J., Johnson, L. A., Rosenberg, S. A., Cardozo, T. J., & Krogsgaard, M. (2014) · The Journal of Immunology · doi:10.4049/jimmunol.1302344 · Cited by 45
Decomposing the Energetic Impact of Drug Resistant Mutations in HIV-1 Protease on Binding DRV
Cai, Y., & Schiffer, C. A. (2010) · Journal of Chemical Theory and Computation · doi:10.1021/ct9004678 · Cited by 45
Designing specific protein–protein interactions using computation, experimental library screening, or integrated methods
Chen, T. S., & Keating, A. E. (2012) · Protein Science · doi:10.1002/pro.2096 · Cited by 44
Development of novel humanized anti‐CD20 antibodies based on affinity constant and epitope
Uchiyama, S., Suzuki, Y., Otake, K., Yokoyama, M., Ohta, M., Aikawa, S., Komatsu, M., Sawada, T., Kagami, Y., Morishima, Y., et al. (2009) · Cancer Science · doi:10.1111/j.1349-7006.2009.01392.x · Cited by 45
Systematic placement of structural water molecules for improved scoring of protein-ligand interactions
Huggins, D. J., & Tidor, B. (2011) · Protein Engineering Design and Selection · doi:10.1093/protein/gzr036 · Cited by 48
Self-citation
Roadmap to affinity-tuned antibodies for enhanced chimeric antigen receptor T cell function and selectivity
Mause, E. R. V., Atanackovic, D., Lim, C. S., & Luetkens, T. (2022) · Trends in biotechnology · doi:10.1016/j.tibtech.2021.12.009 · Cited by 44
Structurally mapping antibody repertoires
Krawczyk, K., Kelm, S., Kovaltsuk, A., Galson, J. D., Kelly, D., Trück, J., Regep, C., Leem, J., Wong, W. K., Nowak, J., et al. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.01698 · Cited by 42
Protein engineering by highly parallel screening of computationally designed variants
Sun, M. G. F., Seo, M.-H., Nim, S., Corbi-Verge, C., & Kim, P. M. (2016) · Science Advances · doi:10.1126/sciadv.1600692 · Cited by 41
Affinity Improvement of a Cancer-Targeted Antibody through Alanine-Induced Adjustment of Antigen-Antibody Interface
Yamashita, T., Mizohata, E., Nagatoishi, S., Watanabe, T., Nakakido, M., Iwanari, H., Mochizuki, Y., Nakayama, T., Kado, Y., Yokota, Y., et al. (2018) · Structure · doi:10.1016/j.str.2018.11.002 · Cited by 42
Light and carbon: Synthetic biology toward new cyanobacteria-based living biomaterials
Goodchild-Michelman, I. M., Church, G. M., Schubert, M. G., & Tang, T.-C. (2023) · Materials Today Bio · doi:10.1016/j.mtbio.2023.100583 · Cited by 41
Protein Engineering by Combined Computational and In Vitro Evolution Approaches
Rosenfeld, L., Heyne, M., Shifman, J. M., & Papo, N. (2016) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2016.03.002 · Cited by 41
Assisted Design of Antibody and Protein Therapeutics (ADAPT)
Vivcharuk, V., Baardsnes, J., Deprez, C., Sulea, T. I., Jaramillo, M., Corbeil, C. R., Mullick, A., Magoon, J., Marcil, A., Durocher, Y., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0181490 · Cited by 41
New-Generation High-Potency and Designer Antibodies: Role in HIV-1 Treatment
Gama, L., & Koup, R. A. (2017) · Annual Review of Medicine · doi:10.1146/annurev-med-061016-041032 · Cited by 40
Computational affinity maturation of camelid single-domain intrabodies against the nonamyloid component of alpha-synuclein
Mahajan, S. P., Meksiriporn, B., Waraho‐Zhmayev, D., Weyant, K. B., Koçer, İ., Butler, D. C., Messer, A. C., Escobedo, F. A., & DeLisa, M. P. (2018) · Scientific Reports · doi:10.1038/s41598-018-35464-7 · Cited by 41
Computational design of an epitope-specific Keap1 binding antibody using hotspot residues grafting and CDR loop swapping
Liu, X., Taylor, R. D., Griffin, L., Coker, S.-F., Adams, R., Ceska, T., Shi, J., Lawson, A. D. G., & Baker, T. (2017) · Scientific Reports · doi:10.1038/srep41306 · Cited by 39
Enhanced Plasmonic Biosensor Utilizing Paired Antibody and Label-Free Fe3O4 Nanoparticles for Highly Sensitive and Selective Detection of Parkinson’s α-Synuclein in Serum
Mandala, S. H. S., Liu, T.-J., Chen, C., Liu, K., Januar, M., Chang, Y.-F., Lai, C., Chang, K., & Liu, K. (2021) · Biosensors · doi:10.3390/bios11100402 · Cited by 39
Three-Dimensional Structural Modelling and Calculation of Electrostatic Potentials of HLA Bw4 and Bw6 Epitopes to Explain the Molecular Basis for Alloantibody Binding
Mallon, D. H., Bradley, J. A., Winn, P. J., Taylor, C. J., & Kosmoliaptsis, V. (2015) · Transplantation · doi:10.1097/tp.0000000000000546 · Cited by 38
Application of Assisted Design of Antibody and Protein Therapeutics (ADAPT) improves efficacy of a Clostridium difficile toxin A single-domain antibody
Sulea, T. I., Hussack, G., Ryan, S., Tanha, J., & Purisima, E. O. (2018) · Scientific Reports · doi:10.1038/s41598-018-20599-4 · Cited by 38
Recent Progress in the Discovery and Development of Monoclonal Antibodies against Viral Infections
Mokhtary, P., Pourhashem, Z., Mehrizi, A. A., Sala, C., & Rappuoli, R. (2022) · Biomedicines · doi:10.3390/biomedicines10081861 · Cited by 44
Improving the affinity of an antibody for its antigen via long-range electrostatic interactions
Fukunaga, A., & Tsumoto, K. (2013) · Protein Engineering Design and Selection · doi:10.1093/protein/gzt053 · Cited by 36
In Silico Methods in Antibody Design
Zhao, J., Nussinov, R., Wu, W., & Ma, B. (2018) · Antibodies · doi:10.3390/antib7030022 · Cited by 37
Robust in vitro affinity maturation strategy based on interface-focused high-throughput mutational scanning
Fujino, Y., Fujita, R., Wada, K., Fujishige, K., Kanamori, T., Hunt, L., Shimizu, Y., & Ueda, T. (2012) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2012.10.066 · Cited by 35
Rational engineering of antibody therapeutics targeting multiple oncogene pathways
Fitzgerald, J. B., & Lugovskoy, A. A. (2011) · mAbs · doi:10.4161/mabs.3.3.15299 · Cited by 34
Affinity enhancement of nanobody binding to EGFR: in silico site-directed mutagenesis and molecular dynamics simulation approaches
Farasat, A., Rahbarizadeh, F., Hosseinzadeh, G., Sajjadi, S., Kamali, M., & Keihan, A. H. (2016) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2016.1192065 · Cited by 34
PURE ribosome display and its application in antibody technology
Kanamori, T., Fujino, Y., & Ueda, T. (2014) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2014.04.007 · Cited by 33
Symmetric Signaling by an Asymmetric 1 Erythropoietin: 2 Erythropoietin Receptor Complex
Zhang, Y. L., Radhakrishnan, M. L., Lu, X., Gross, A. W., Tidor, B., & Lodish, H. F. (2009) · Molecular Cell · doi:10.1016/j.molcel.2008.11.026 · Cited by 33
Self-citation
Assessment of human leukocyte antigen immunogenicity: current methods, challenges and opportunities
Copley, H. C., Elango, M., & Kosmoliaptsis, V. (2018) · Current Opinion in Organ Transplantation · doi:10.1097/mot.0000000000000544 · Cited by 31
Binding affinity prediction for antibody–protein antigen complexes: A machine learning analysis based on interface and surface areas
Yang, Y. X., Wang, P., & Zhu, B. T. (2022) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2022.108364 · Cited by 31
Generation of recombinant antibodies and means for increasing their affinity
Altshuler, E. P., Serebryanaya, D. V., & Katrukha, A. (2010) · Biochemistry (Moscow) · doi:10.1134/s0006297910130067 · Cited by 32
Rapid optimization and prototyping for therapeutic antibody-like molecules
Xu, L., Kohli, N., Rennard, R., Jiao, Y., Razlog, M., Zhang, K., Baum, J., Johnson, B. W., Tang, J., Schoeberl, B., et al. (2013) · mAbs · doi:10.4161/mabs.23363 · Cited by 29
High-resolution crystal structures leverage protein binding affinity predictions
Marillet, S., Boudinot, P., & Cazals, F. (2015) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24946 · Cited by 29
Fc-binding antibody-recruiting molecules exploit endogenous antibodies for anti-tumor immune responses
Sasaki, K., Harada, M., Miyashita, Y., Tagawa, H., Kishimura, A., Mori, T., & Katayama, Y. (2020) · Chemical Science · doi:10.1039/d0sc00017e · Cited by 30
Optimizing Properties of Antireceptor Antibodies Using Kinetic Computational Models and Experiments
Harms, B. D., Kearns, J. D., Su, S. V., Kohli, N., Nielsen, U. B., & Schoeberl, B. (2011) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/b978-0-12-416039-2.00004-5 · Cited by 29
Assessment of Solvated Interaction Energy Function for Ranking Antibody–Antigen Binding Affinities
Sulea, T. I., Vivcharuk, V., Corbeil, C. R., Deprez, C., & Purisima, E. O. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00043 · Cited by 29
Recombinant antibodies for cancer therapy
Deonarain, M. P. (2008) · Expert Opinion on Biological Therapy · doi:10.1517/14712598.8.8.1123 · Cited by 29
Antigen–Antibody Binding
Tsumoto, K., & Caaveiro, J. M. M. (2016) · Encyclopedia of Life Sciences · doi:10.1002/9780470015902.a0001117.pub3 · Cited by 29
Computational design of protein therapeutics
Hwang, I., & Park, S. (2008) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2008.11.004 · Cited by 28
Computational tools for aiding rational antibody design
Krawczyk, K., Dunbar, J., & Deane, C. M. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-6637-0_21 · Cited by 26
Antibody affinity maturation by computational design
Kuroda, D., & Tsumoto, K. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8648-4_2 · Cited by 26
Defining the complementarities between antibodies and haptens to refine our understanding and aid the prediction of a successful binding interaction
Qaraghuli, M. M. A., Palliyil, S., Broadbent, G., Cullen, D. C., Charlton, K. A., & Porter, A. J. (2015) · BMC Biotechnology · doi:10.1186/s12896-015-0217-x · Cited by 26
Rational design of thiolase substrate specificity for metabolic engineering applications
Bonk, B. M., Tarasova, Y., Hicks, M. A., Tidor, B., & Prather, K. L. J. (2018) · Biotechnology and Bioengineering · doi:10.1002/bit.26737 · Cited by 24
Self-citation
An improved Protein G with higher affinity for human/rabbit IgG Fc domains exploiting a computationally designed polar network
Jha, R. K., Gaiotto, T., Bradbury, A. M., & Strauss, C. E. M. (2014) · Protein Engineering Design and Selection · doi:10.1093/protein/gzu005 · Cited by 25
Redesigning the stereospecificity of tyrosyl-tRNA synthetase
Simonson, T., Ye, S., Palmai, Z., Amara, N., Wydau-Dematteis, S., Bigan, E., Druart, K., Moch, C., & Plateau, P. (2015) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24972 · Cited by 25
Multistate Protein Design Using CLEVER and CLASSY
Negron, C., & Keating, A. E. (2013) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/b978-0-12-394292-0.00008-4 · Cited by 25
Novel Structural Parameters of Ig–Ag Complexes Yield a Quantitative Description of Interaction Specificity and Binding Affinity
Marillet, S., Lefranc, M., Boudinot, P., & Cazals, F. (2017) · Frontiers in Immunology · doi:10.3389/fimmu.2017.00034 · Cited by 25
Physics-Based Computational Protein Design: An Update
Mignon, D., Druart, K., Michael, E., Opuu, V., Polydorides, S., Villa, F., Gaillard, T., Panel, N., Archontis, G., & Simonson, T. (2020) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.0c07605 · Cited by 25
Research progress on unique paratope structure, antigen binding modes, and systematic mutagenesis strategies of single-domain antibodies
Liu, C., Lin, H., Cao, L., Wang, K., & Sui, J. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1059771 · Cited by 26
Design of nanobody-based bispecific constructs by in silico affinity maturation and umbrella sampling simulations
Bai, Z., Wang, J., Li, J., Yuan, H., Wang, P., Zhang, M., Feng, Y., Cao, X., Cao, X., Kang, G., et al. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.12.021 · Cited by 24
Identifying and Overcoming the Sampling Challenges in Relative Binding Free Energy Calculations of a Model Protein:Protein Complex
Zhang, I., Rufa, D. A., Pulido, I., Henry, M. M., Rosen, L. E., Hauser, K., Singh, S., & Chodera, J. D. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00333 · Cited by 23
Computational protein design with a generalized born solvent model: Application to asparaginyl‐tRNA synthetase
Polydorides, S., Amara, N., Aubard, C., Plateau, P., Simonson, T., & Archontis, G. (2011) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.23042 · Cited by 24
Energetic and Dynamic Aspects of the Affinity Maturation Process: Characterizing Improved Variants from the Bevacizumab Antibody with Molecular Simulations
Corrada, D., & Colombo, G. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400416e · Cited by 23
Rotamer optimization for protein design through MAP estimation and problem‐size reduction
Hong, E., Lippow, S. M., Tidor, B., & Lozano‐Pérez, T. (2009) · Journal of Computational Chemistry · doi:10.1002/jcc.21188 · Cited by 23
Self-citation
Simultaneous affinity maturation and developability enhancement using natural liability-free CDRs
Teixeira, A. A. R., D’Angelo, S., Erasmus, M. F., Leal‐Lopes, C., Ferrara, F., Spector, L. P., Naranjo, L. A., Molina, E., Max, T., DeAguero, A., et al. (2022) · mAbs · doi:10.1080/19420862.2022.2115200 · Cited by 23
High-throughput analysis system of interaction kinetics for data-driven antibody design
Matsunaga, R., Ujiie, K., Inagaki, M., Pérez, J. F., Yasuda, Y., Mimasu, S., Soga, S., & Tsumoto, K. (2023) · Scientific Reports · doi:10.1038/s41598-023-46756-y · Cited by 23
Predicting affinity- and specificity-enhancing mutations at protein–protein interfaces
Sharabi, O., Shirian, J., & Shifman, J. M. (2013) · Biochemical Society Transactions · doi:10.1042/bst20130121 · Cited by 23
Antibody side chain conformations are position-dependent
Leem, J., Georges, G., Shi, J., & Deane, C. M. (2018) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25453 · Cited by 22
Towards an optimal monoclonal antibody with higher binding affinity to the receptor-binding domain of SARS-CoV-2 spike proteins from different variants
Neamțu, A., Mocci, F., Laaksonen, A., & da Silva, F. L. B. (2022) · Colloids and Surfaces B Biointerfaces · doi:10.1016/j.colsurfb.2022.112986 · Cited by 23
Advances in the structural characterization of complexes of therapeutic antibodies with PD-1 or PD-L1
Jiang, M., Liu, M., Liu, G., Ma, J., Zhang, L., & Wang, S. (2023) · mAbs · doi:10.1080/19420862.2023.2236740 · Cited by 23
Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen
Hutchinson, M., Ruffolo, J. A., Haskins, N., Iannotti, M., Vozza, G., Pham, T., Mehzabeen, N., Shandilya, H., Rickert, K., Croasdale-Wood, R., et al. (2024) · mAbs · doi:10.1080/19420862.2024.2362775 · Cited by 22
Protein:Ligand binding free energies: A stringent test for computational protein design
Druart, K., Palmai, Z., Omarjee, E., & Simonson, T. (2015) · Journal of Computational Chemistry · doi:10.1002/jcc.24230 · Cited by 22
MoFvAb: Modeling the Fv region of antibodies
Bujotzek, A., Fuchs, A., Qu, C., Benz, J., Klostermann, S., Antes, I., & Georges, G. J. (2015) · mAbs · doi:10.1080/19420862.2015.1068492 · Cited by 22
Structural features of interfacial tyrosine residue in ROBO1 fibronectin domain‐antibody complex: Crystallographic, thermodynamic, and molecular dynamic analyses
Nakayama, T., Mizohata, E., Yamashita, T., Nagatoishi, S., Nakakido, M., Iwanari, H., Mochizuki, Y., Kado, Y., Yokota, Y., Satoh, R., et al. (2014) · Protein Science · doi:10.1002/pro.2619 · Cited by 23
Structure-based non-canonical amino acid design to covalently crosslink an antibody–antigen complex
Xu, J., Tack, D. S., Hughes, R. A., Ellington, A. D., & Gray, J. J. (2013) · Journal of Structural Biology · doi:10.1016/j.jsb.2013.05.003 · Cited by 22
Expression, Purification and Structural Analysis of Human IL-18 Binding Protein: A Potent Therapeutic Molecule for Allergy
Kimura, T., Kato, Z., Ohnishi, H., Tochio, H., Shirakawa, M., & Kondo, N. (2008) · Allergology International · doi:10.2332/allergolint.o-08-546 · Cited by 22
AntiFormer: graph enhanced large language model for binding affinity prediction
Wang, Q., Feng, Y., Wang, Y., Li, B., Wen, J., Zhou, X., & Song, Q. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae403 · Cited by 21
Assessment of software methods for estimating protein-protein relative binding affinities
Gonzalez, T. R., Martin, K., Barnes, J. E., Patel, J. S., & Ytreberg, F. M. (2020) · PLoS ONE · doi:10.1371/journal.pone.0240573 · Cited by 22
On accurate calculation of the potential of mean force between antigen and antibody: A case of the HyHEL-10-hen egg white lysozyme system
Yamashita, T., & Fujitani, H. (2014) · Chemical Physics Letters · doi:10.1016/j.cplett.2014.06.028 · Cited by 21
Triathlon for energy functions: Who is the winner for design of protein–protein interactions?
Sharabi, O., Dekel, A., & Shifman, J. M. (2011) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22977 · Cited by 21
iCFN: an efficient exact algorithm for multistate protein design
Karimi, M., & Shen, Y. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty564 · Cited by 21
Antibodies: Computer-Aided Prediction of Structure and Design of Function
Sevy, A. M., & Meiler, J. (2014) · Microbiology Spectrum · doi:10.1128/microbiolspec.aid-0024-2014 · Cited by 21
Computational design of protein–ligand interfaces: potential in therapeutic development
Morin, A., Meiler, J., & Mizoue, L. S. (2011) · Trends in biotechnology · doi:10.1016/j.tibtech.2011.01.002 · Cited by 20
Antibody affinity maturation using yeast display with detergent-solubilized membrane proteins as antigen sources
Tillotson, B. J., Fernandez-de-Larrinoa, Í., Skinner, C., Klavas, D. M., & Shusta, E. V. (2012) · Protein Engineering Design and Selection · doi:10.1093/protein/gzs077 · Cited by 20
In vitro affinity maturation to improve the efficacy of a hypoxia-inducible factor 1α single-domain intrabody
Hu, M., Kang, G., Cheng, X. M., Wang, J., Li, R., Bai, Z., Yang, D., & Huang, H. (2020) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2020.06.097 · Cited by 20
Backbone Flexibility of CDR3 and Immune Recognition of Antigens
Haidar, J. N. S., Zhu, W., Lypowy, J., Pierce, B. G., Bari, A., Persaud, K., Luna, X., Snavely, M. D., Ludwig, D. L., & Weng, Z. (2014) · Journal of Molecular Biology · doi:10.1016/j.jmb.2013.12.024 · Cited by 20
Computational design of protein–ligand binding: Modifying the specificity of asparaginyl‐tRNA synthetase
Lopes, A., Busch, M. S. A., & Simonson, T. (2009) · Journal of Computational Chemistry · doi:10.1002/jcc.21414 · Cited by 20
Structure‐based design of combinatorial mutagenesis libraries
Verma, D., Grigoryan, G., & Bailey‐Kellogg, C. (2015) · Protein Science · doi:10.1002/pro.2642 · Cited by 21
Improving antibody affinity through in vitro mutagenesis in complementarity determining regions
Ye, W., Liu, X., He, R., Gou, L., Lü, M., Yang, G., Wen, J., Wang, X., Liu, F., Ma, S., et al. (2022) · Journal of Biomedical Research · doi:10.7555/jbr.36.20220003 · Cited by 17
Structure and Dynamics Guiding Design of Antibody Therapeutics and Vaccines
Fernández‐Quintero, M. L., Pomarici, N. D., Fischer, A.-L. M., Hoerschinger, V. J., Kroell, K. B., Riccabona, J. R., Kamenik, A. S., Loeffler, J. R., Ferguson, J. A., Perrett, H. R., et al. (2023) · Antibodies · doi:10.3390/antib12040067 · Cited by 21
Mapping of the Binding Landscape for a Picomolar Protein-Protein Complex through Computation and Experiment
Aizner, Y., Sharabi, O., Shirian, J., Dakwar, G. R., Risman, M., Avraham, O., & Shifman, J. M. (2014) · Structure · doi:10.1016/j.str.2014.01.012 · Cited by 18
Rational and Computational Design of Stabilized Variants of Cyanovirin-N That Retain Affinity and Specificity for Glycan Ligands
Patsalo, V., Raleigh, D. P., & Green, D. F. (2011) · Biochemistry · doi:10.1021/bi201411c · Cited by 18
Fast gap-free enumeration of conformations and sequences for protein design
Roberts, K. E., Gaínza, P., Hallen, M. A., & Donald, B. R. (2015) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24870 · Cited by 18
Model Building of Antibody–Antigen Complex Structures Using GBSA Scores
Shimba, N., Kamiya, N., & Nakamura, H. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00066 · Cited by 18
Redesigning HVEM Interface for Selective Binding to LIGHT, BTLA, and CD160
Shrestha, R., Garrett-Thomson, S. C., Liu, W., Almo, S. C., & Fiser, A. (2020) · Structure · doi:10.1016/j.str.2020.07.013 · Cited by 20
Multiscale Mathematical Modeling to Support Drug Development
Nordsletten, D., Yankama, B., Umeton, R., Ayyadurai, V. A. S., & Dewey, C. F. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2173245 · Cited by 18
IsAb: a computational protocol for antibody design
Liang, T., Chen, H., Yuan, J., Jiang, C., Hao, Y., Wang, Y., Feng, Z., & Xie, X. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab143 · Cited by 17
IPRO+/−: Computational Protein Design Tool Allowing for Insertions and Deletions
Chowdhury, R., Grisewood, M. J., Boorla, V. S., Yan, Q., Pfleger, B. F., & Maranas, C. D. (2020) · Structure · doi:10.1016/j.str.2020.08.003 · Cited by 17
DisruPPI: structure-based computational redesign algorithm for protein binding disruption
Choi, Y., Furlon, J. M., Amos, R., Griswold, K. E., & Bailey‐Kellogg, C. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty274 · Cited by 16
Establishment of an indirect competitive immunoassay for the detection of dicamba based on a highly specific nanobody
Wang, Y., Zhou, H. H., Fu, Y., Wang, Z., Gao, Q., Yang, D., Kang, J., Chen, L., An, Z., Hammock, B. D., et al. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.170567 · Cited by 16
Accurate prediction of CDR-H3 loop structures of antibodies with deep learning
(2024) · doi:10.7554/elife.91512.4 · Cited by 16
An efficient method for variable region assembly in the construction of scFv phage display libraries using independent strand amplification
Sotelo, P. H., Collazo, N., Zuñiga, R., Gutiérrez-González, M., Catalán, D., Ribeiro, C. H., Aguillón, J. C., & Molina, M. C. (2012) · mAbs · doi:10.4161/mabs.20653 · Cited by 16
Computational Redesign of PD-1 Interface for PD-L1 Ligand Selectivity
Shrestha, R., Garrett-Thomson, S. C., Almo, S. C., & Fiser, A. (2019) · Structure · doi:10.1016/j.str.2019.03.006 · Cited by 16
Suppression of IgE-mediated anaphylaxis and food allergy with monovalent anti-FcεRIα mAbs
Khodoun, M. V., Morris, S. C., Shao, W.-H., Potter, C., Angerman, E. B., Kiselev, A. M., Yarawsky, A. E., Herr, A. B., Klausz, K., Otte, A., et al. (2020) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2020.10.045 · Cited by 16
High-Throughput Antibody Structure Modeling and Design Using ABodyBuilder
Leem, J., & Deane, C. M. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8736-8_21 · Cited by 15
Modeling and fitting protein-protein complexes to predict change of binding energy
Dourado, D. F. A. R., & Flores, S. C. (2016) · Scientific Reports · doi:10.1038/srep25406 · Cited by 15
No free energy lunch
Shoichet, B. (2007) · Nature Biotechnology · doi:10.1038/nbt1007-1109 · Cited by 16
Seeking high-priority mutations enabling successful antibody-breeding: systematic analysis of a mutant that gained over 100-fold enhanced affinity
Oyama, H., Kiguchi, Y., Morita, I., Yamamoto, C., Higashi, Y., Taguchi, M., Tagawa, T., Enami, Y., Takamine, Y., Hasegawa, H., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-61529-7 · Cited by 15
Applying computational protein design to therapeutic antibody discovery - current state and perspectives
Bielska, W., Jaszczyszyn, I., Dudzic, P., Janusz, B., Chomicz, D., Wrobel, S., Greiff, V., Feehan, R., Adolf‐Bryfogle, J., & Krawczyk, K. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1571371 · Cited by 17

Authors: Lippow, S. M., Wittrup, K. D., & Tidor, B.
Affiliations: Massachusetts Institute of Technology (US)
Corresponding author: Wittrup, K Dane
Tidor, Bruce
ORCID: Wittrup, K Dane
Tidor, Bruce
Venue: Nature Biotechnology
Publisher: Springer Science and Business Media LLC
Published: 2007-09-23
Volume: 25
Issue: 10
Pages: 1171-1176
ISSN: 0733-222X (ISSN-L)
1087-0156
1546-1696
DOI: 10.1038/nbt1336
PMC: PMC2803018
OpenAlex: W2083028331
Cited by: 342
References: 30
Citations by year: 2026: 9 · 2025: 11 · 2024: 14 · 2023: 17 · 2022: 24 · 2021: 14 · 2020: 22 · 2019: 15 · 2018: 23 · 2017: 18 · 2016: 15 · 2015: 24 · 2014: 23 · 2013: 21 · 2012: 21
FWCI: 14.05
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
Locations: Nature Biotechnology (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Science Foundation
National Institutes of Health
National Cancer Institute (CA96504, R01CA096504)
National Institute of General Medical Sciences (GM65418, R56GM065418, R01GM065418)
Topics: Life Sciences › Immunology and Microbiology › Immunology › Monoclonal and Polyclonal Antibodies Research
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
Keywords: affinity maturation, computational antibody design, point mutations, cetuximab, anti-EGFR monoclonal antibody, bevacizumab, anti-fluorescein antibody, anti-lysozyme antibody, therapeutic antibodies, directed evolution, binding affinity enhancement
MeSH terms: Antibodies, Antibody Affinity, Antigens, Genetic Engineering, Models, Biological, Models, Molecular, Muramidase, Mutation, Protein Binding, Protein Conformation, Yeasts, Computer-Aided Design

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Computational design of antibody-affinity improvement beyond in vivo maturation

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks