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Gordon GL, Raybould MIJ, Wong A, Deane CM. Prospects for the computational humanization of antibodies and nanobodies. Front Immunol 2024; 15:1399438. [PMID: 38812514 PMCID: PMC11133524 DOI: 10.3389/fimmu.2024.1399438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 05/02/2024] [Indexed: 05/31/2024] Open
Abstract
To be viable therapeutics, antibodies must be tolerated by the human immune system. Rational approaches to reduce the risk of unwanted immunogenicity involve maximizing the 'humanness' of the candidate drug. However, despite the emergence of new discovery technologies, many of which start from entirely human gene fragments, most antibody therapeutics continue to be derived from non-human sources with concomitant humanization to increase their human compatibility. Early experimental humanization strategies that focus on CDR loop grafting onto human frameworks have been critical to the dominance of this discovery route but do not consider the context of each antibody sequence, impacting their success rate. Other challenges include the simultaneous optimization of other drug-like properties alongside humanness and the humanization of fundamentally non-human modalities such as nanobodies. Significant efforts have been made to develop in silico methodologies able to address these issues, most recently incorporating machine learning techniques. Here, we outline these recent advancements in antibody and nanobody humanization, focusing on computational strategies that make use of the increasing volume of sequence and structural data available and the validation of these tools. We highlight that structural distinctions between antibodies and nanobodies make the application of antibody-focused in silico tools to nanobody humanization non-trivial. Furthermore, we discuss the effects of humanizing mutations on other essential drug-like properties such as binding affinity and developability, and methods that aim to tackle this multi-parameter optimization problem.
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Affiliation(s)
| | | | | | - Charlotte M. Deane
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom
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2
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Li J, Kang G, Wang J, Yuan H, Wu Y, Meng S, Wang P, Zhang M, Wang Y, Feng Y, Huang H, de Marco A. Affinity maturation of antibody fragments: A review encompassing the development from random approaches to computational rational optimization. Int J Biol Macromol 2023; 247:125733. [PMID: 37423452 DOI: 10.1016/j.ijbiomac.2023.125733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 07/04/2023] [Accepted: 07/06/2023] [Indexed: 07/11/2023]
Abstract
Routinely screened antibody fragments usually require further in vitro maturation to achieve the desired biophysical properties. Blind in vitro strategies can produce improved ligands by introducing random mutations into the original sequences and selecting the resulting clones under more and more stringent conditions. Rational approaches exploit an alternative perspective that aims first at identifying the specific residues potentially involved in the control of biophysical mechanisms, such as affinity or stability, and then to evaluate what mutations could improve those characteristics. The understanding of the antigen-antibody interactions is instrumental to develop this process the reliability of which, consequently, strongly depends on the quality and completeness of the structural information. Recently, methods based on deep learning approaches critically improved the speed and accuracy of model building and are promising tools for accelerating the docking step. Here, we review the features of the available bioinformatic instruments and analyze the reports illustrating the result obtained with their application to optimize antibody fragments, and nanobodies in particular. Finally, the emerging trends and open questions are summarized.
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Affiliation(s)
- Jiaqi Li
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Guangbo Kang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Jiewen Wang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Haibin Yuan
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Yili Wu
- Zhejiang Provincial Clinical Research Center for Mental Disorders, School of Mental Health and the Affiliated Kangning Hospital, Institute of Aging, Key Laboratory of Alzheimer's Disease of Zhejiang Province, Wenzhou Medical University, Oujiang Laboratory, Wenzhou, Zhejiang 325035, China
| | - Shuxian Meng
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Ping Wang
- New Technology R&D Department, Tianjin Modern Innovative TCM Technology Company Limited, Tianjin 300392, China
| | - Miao Zhang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; China Resources Biopharmaceutical Company Limited, Beijing 100029, China
| | - Yuli Wang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; Tianjin Pharmaceutical Da Ren Tang Group Corporation Limited, Traditional Chinese Pharmacy Research Institute, Tianjin Key Laboratory of Quality Control in Chinese Medicine, Tianjin 300457, China; State Key Laboratory of Drug Delivery Technology and Pharmacokinetics, Tianjin Institute of Pharmaceutical Research, Tianjin 300193, China
| | - Yuanhang Feng
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - He Huang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China.
| | - Ario de Marco
- Laboratory for Environmental and Life Sciences, University of Nova Gorica, Nova Gorica, Slovenia.
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3
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Licari G, Martin KP, Crames M, Mozdzierz J, Marlow MS, Karow-Zwick AR, Kumar S, Bauer J. Embedding Dynamics in Intrinsic Physicochemical Profiles of Market-Stage Antibody-Based Biotherapeutics. Mol Pharm 2022; 20:1096-1111. [PMID: 36573887 PMCID: PMC9906779 DOI: 10.1021/acs.molpharmaceut.2c00838] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Adequate stability, manufacturability, and safety are crucial to bringing an antibody-based biotherapeutic to the market. Following the concept of holistic in silico developability, we introduce a physicochemical description of 91 market-stage antibody-based biotherapeutics based on orthogonal molecular properties of variable regions (Fvs) embedded in different simulation environments, mimicking conditions experienced by antibodies during manufacturing, formulation, and in vivo. In this work, the evaluation of molecular properties includes conformational flexibility of the Fvs using molecular dynamics (MD) simulations. The comparison between static homology models and simulations shows that MD significantly affects certain molecular descriptors like surface molecular patches. Moreover, the structural stability of a subset of Fv regions is linked to changes in their specific molecular interactions with ions in different experimental conditions. This is supported by the observation of differences in protein melting temperatures upon addition of NaCl. A DEvelopability Navigator In Silico (DENIS) is proposed to compare mAb candidates for their similarity with market-stage biotherapeutics in terms of physicochemical properties and conformational stability. Expanding on our previous developability guidelines (Ahmed et al. Proc. Natl. Acad. Sci. 2021, 118 (37), e2020577118), the hydrodynamic radius and the protein strand ratio are introduced as two additional descriptors that enable a more comprehensive in silico characterization of biotherapeutic drug candidates. Test cases show how this approach can facilitate identification and optimization of intrinsically developable lead candidates. DENIS represents an advanced computational tool to progress biotherapeutic drug candidates from discovery into early development by predicting drug properties in different aqueous environments.
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Affiliation(s)
- Giuseppe Licari
- Early
Stage Pharmaceutical Development, Pharmaceutical Development Biologicals
& In silico Team, Boehringer Ingelheim
International GmbH & Co. KG, Biberach/Riss 88397, Germany
| | - Kyle P. Martin
- Biotherapeutics
Discovery & In silico Team, Boehringer
Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut 06877, United States
| | - Maureen Crames
- Biotherapeutics
Discovery & In silico Team, Boehringer
Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut 06877, United States
| | - Joseph Mozdzierz
- Biotherapeutics
Discovery & In silico Team, Boehringer
Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut 06877, United States
| | - Michael S. Marlow
- Biotherapeutics
Discovery & In silico Team, Boehringer
Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut 06877, United States
| | - Anne R. Karow-Zwick
- Early
Stage Pharmaceutical Development, Pharmaceutical Development Biologicals
& In silico Team, Boehringer Ingelheim
International GmbH & Co. KG, Biberach/Riss 88397, Germany
| | - Sandeep Kumar
- Biotherapeutics
Discovery & In silico Team, Boehringer
Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut 06877, United States,
| | - Joschka Bauer
- Early
Stage Pharmaceutical Development, Pharmaceutical Development Biologicals
& In silico Team, Boehringer Ingelheim
International GmbH & Co. KG, Biberach/Riss 88397, Germany,
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4
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Guerin N, Feichtner A, Stefan E, Kaserer T, Donald BR. Resistor: An algorithm for predicting resistance mutations via Pareto optimization over multistate protein design and mutational signatures. Cell Syst 2022; 13:830-843.e3. [PMID: 36265469 PMCID: PMC9589925 DOI: 10.1016/j.cels.2022.09.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 06/29/2022] [Accepted: 09/13/2022] [Indexed: 01/26/2023]
Abstract
Resistance to pharmacological treatments is a major public health challenge. Here, we introduce Resistor-a structure- and sequence-based algorithm that prospectively predicts resistance mutations for drug design. Resistor computes the Pareto frontier of four resistance-causing criteria: the change in binding affinity (ΔKa) of the (1) drug and (2) endogenous ligand upon a protein's mutation; (3) the probability a mutation will occur based on empirically derived mutational signatures; and (4) the cardinality of mutations comprising a hotspot. For validation, we applied Resistor to EGFR and BRAF kinase inhibitors treating lung adenocarcinoma and melanoma. Resistor correctly identified eight clinically significant EGFR resistance mutations, including the erlotinib and gefitinib "gatekeeper" T790M mutation and five known osimertinib resistance mutations. Furthermore, Resistor predictions are consistent with BRAF inhibitor sensitivity data from both retrospective and prospective experiments using KinCon biosensors. Resistor is available in the open-source protein design software OSPREY.
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Affiliation(s)
- Nathan Guerin
- Department of Computer Science, Duke University, Durham, NC 27708, USA
| | - Andreas Feichtner
- Institute of Biochemistry and Center for Molecular Biosciences, University of Innsbruck, Innsbruck, 6020 Tyrol, Austria
| | - Eduard Stefan
- Institute of Biochemistry and Center for Molecular Biosciences, University of Innsbruck, Innsbruck, 6020 Tyrol, Austria; Tyrolean Cancer Research Institute, Innsbruck, 6020 Tyrol, Austria
| | - Teresa Kaserer
- Institute of Pharmacy/Pharmaceutical Chemistry, University of Innsbruck, Innsbruck, 6020 Tyrol, Austria.
| | - Bruce R Donald
- Department of Computer Science, Duke University, Durham, NC 27708, USA; Department of Biochemistry, Duke University Medical Center, Durham, NC 27710, USA; Department of Chemistry, Duke University, Durham, NC 27708, USA; Department of Mathematics, Duke University, Durham, NC 27708, USA.
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5
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Makowski EK, Kinnunen PC, Huang J, Wu L, Smith MD, Wang T, Desai AA, Streu CN, Zhang Y, Zupancic JM, Schardt JS, Linderman JJ, Tessier PM. Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space. Nat Commun 2022; 13:3788. [PMID: 35778381 PMCID: PMC9249733 DOI: 10.1038/s41467-022-31457-3] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 06/20/2022] [Indexed: 11/08/2022] Open
Abstract
Therapeutic antibody development requires selection and engineering of molecules with high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody properties remains a difficult and time-consuming process that impedes drug development. Here we evaluate the use of machine learning to simplify antibody co-optimization for a clinical-stage antibody (emibetuzumab) that displays high levels of both on-target (antigen) and off-target (non-specific) binding. We mutate sites in the antibody complementarity-determining regions, sort the antibody libraries for high and low levels of affinity and non-specific binding, and deep sequence the enriched libraries. Interestingly, machine learning models trained on datasets with binary labels enable predictions of continuous metrics that are strongly correlated with antibody affinity and non-specific binding. These models illustrate strong tradeoffs between these two properties, as increases in affinity along the co-optimal (Pareto) frontier require progressive reductions in specificity. Notably, models trained with deep learning features enable prediction of novel antibody mutations that co-optimize affinity and specificity beyond what is possible for the original antibody library. These findings demonstrate the power of machine learning models to greatly expand the exploration of novel antibody sequence space and accelerate the development of highly potent, drug-like antibodies.
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Affiliation(s)
- Emily K Makowski
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Patrick C Kinnunen
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jie Huang
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Lina Wu
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Matthew D Smith
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Tiexin Wang
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Alec A Desai
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Craig N Streu
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Chemistry, Albion College, Albion, MI, 49224, USA
| | - Yulei Zhang
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jennifer M Zupancic
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - John S Schardt
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jennifer J Linderman
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Peter M Tessier
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, 48109, USA.
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA.
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
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6
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Engineering an Enhanced EGFR Engager: Humanization of Cetuximab for Improved Developability. Antibodies (Basel) 2022; 11:antib11010006. [PMID: 35076451 PMCID: PMC8788544 DOI: 10.3390/antib11010006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 12/12/2021] [Accepted: 01/05/2022] [Indexed: 01/05/2023] Open
Abstract
The epidermal growth factor receptor (EGFR) is a receptor tyrosine kinase whose proliferative effects can contribute to the development of many types of solid tumors when overexpressed. For this reason, EGFR inhibitors such as cetuximab can play an important role in treating cancers such as colorectal cancer and head and neck cancer. Cetuximab is a chimeric monoclonal antibody containing mouse variable regions that bind to EGFR and prevent it from signaling. Although cetuximab has been used clinically since 2004 to successfully control solid tumors, advances in protein engineering have created the opportunity to address some of its shortcomings. In particular, the presence of mouse sequences could contribute to immunogenicity in the form of anti-cetuximab antibodies, and an occupied glycosylation site in FR3 can contribute to hypersensitivity reactions and product heterogeneity. Using simple framework graft or sequence-/structure-guided approaches, cetuximab was humanized onto 11 new frameworks. In addition to increasing humanness and removing the VH glycosylation site, dynamic light scattering revealed increases in stability, and bio-layer interferometry confirmed minimal changes in binding affinity, with patterns emerging across the humanization method. This work demonstrates the potential to improve the biophysical and clinical properties of first-generation protein therapeutics and highlights the advantages of computationally guided engineering.
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7
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Makowski EK, Wu L, Gupta P, Tessier PM. Discovery-stage identification of drug-like antibodies using emerging experimental and computational methods. MAbs 2021; 13:1895540. [PMID: 34313532 PMCID: PMC8346245 DOI: 10.1080/19420862.2021.1895540] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/05/2021] [Accepted: 02/22/2021] [Indexed: 11/30/2022] Open
Abstract
There is intense and widespread interest in developing monoclonal antibodies as therapeutic agents to treat diverse human disorders. During early-stage antibody discovery, hundreds to thousands of lead candidates are identified, and those that lack optimal physical and chemical properties must be deselected as early as possible to avoid problems later in drug development. It is particularly challenging to characterize such properties for large numbers of candidates with the low antibody quantities, concentrations, and purities that are available at the discovery stage, and to predict concentrated antibody properties (e.g., solubility, viscosity) required for efficient formulation, delivery, and efficacy. Here we review key recent advances in developing and implementing high-throughput methods for identifying antibodies with desirable in vitro and in vivo properties, including favorable antibody stability, specificity, solubility, pharmacokinetics, and immunogenicity profiles, that together encompass overall drug developability. In particular, we highlight impressive recent progress in developing computational methods for improving rational antibody design and prediction of drug-like behaviors that hold great promise for reducing the amount of required experimentation. We also discuss outstanding challenges that will need to be addressed in the future to fully realize the great potential of using such analysis for minimizing development times and improving the success rate of antibody candidates in the clinic.
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Affiliation(s)
- Emily K. Makowski
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Lina Wu
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering
| | - Priyanka Gupta
- Department of Biochemistry and Biophysics, Rensselaer Polytechnic Institute, Troy, NY, USA
- Biotherapeutics Discovery Department, Boehringer Ingelheim, Ridgefield, CT, USA
| | - Peter M. Tessier
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
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8
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Kuroda D, Tsumoto K. Engineering Stability, Viscosity, and Immunogenicity of Antibodies by Computational Design. J Pharm Sci 2020; 109:1631-1651. [DOI: 10.1016/j.xphs.2020.01.011] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 12/25/2019] [Accepted: 01/10/2020] [Indexed: 12/18/2022]
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9
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Structural delineation of potent transmission-blocking epitope I on malaria antigen Pfs48/45. Nat Commun 2018; 9:4458. [PMID: 30367064 PMCID: PMC6203815 DOI: 10.1038/s41467-018-06742-9] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Accepted: 09/17/2018] [Indexed: 12/28/2022] Open
Abstract
Interventions that can block the transmission of malaria-causing Plasmodium falciparum (Pf) between the human host and Anopheles vector have the potential to reduce the incidence of malaria. Pfs48/45 is a gametocyte surface protein critical for parasite development and transmission, and its targeting by monoclonal antibody (mAb) 85RF45.1 leads to the potent reduction of parasite transmission. Here, we reveal how the Pfs48/45 6C domain adopts a (SAG1)-related-sequence (SRS) fold. We structurally delineate potent epitope I and show how mAb 85RF45.1 recognizes an electronegative surface with nanomolar affinity. Analysis of Pfs48/45 sequences reveals that polymorphisms are rare for residues involved at the binding interface. Humanization of rat-derived mAb 85RF45.1 conserved the mode of recognition and activity of the parental antibody, while also improving its thermostability. Our work has implications for the development of transmission-blocking interventions, both through improving vaccine designs and the testing of passive delivery of mAbs in humans. Malaria protein Pfs48/45 is a promising transmission-blocking antigen targeted by antibodies. Here, the authors determine the structure of its transmission-blocking epitope I, and generate a humanized monoclonal antibody that binds Pfs48/45 with high affinity.
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10
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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.
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Affiliation(s)
- Daisuke Kuroda
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Kouhei Tsumoto
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan.
- Medical Proteomics Laboratory, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
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11
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Griswold KE, Bailey-Kellogg C. Design and engineering of deimmunized biotherapeutics. Curr Opin Struct Biol 2016; 39:79-88. [PMID: 27322891 DOI: 10.1016/j.sbi.2016.06.003] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Revised: 06/03/2016] [Accepted: 06/06/2016] [Indexed: 12/26/2022]
Abstract
Therapeutic proteins are powerful next-generation drugs able to effectively treat diverse and devastating diseases, but the development and use of biotherapeutics entails unique challenges and risks. In particular, protein drugs are subject to immune surveillance in the human body, and ensuing antidrug immune responses can cause a wide range of problems including altered pharmacokinetics, loss of efficacy, and even life-threating complications. Here we review recent progress in technologies for engineering deimmunized biotherapeutics, placing particular emphasis on deletion of immunogenic antibody and T cell epitopes via experimentally or computationally guided mutagenesis.
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Affiliation(s)
- Karl E Griswold
- Thayer School of Engineering, Dartmouth, Hanover, NH, United States; Stealth Biologics LLC, Lyme, NH, United States.
| | - Chris Bailey-Kellogg
- Stealth Biologics LLC, Lyme, NH, United States; Department of Computer Science, Dartmouth, Hanover, NH, United States.
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