102
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Frese K, Eisenmann M, Ostendorp R, Brocks B, Pabst S. An automated immunoassay for early specificity profiling of antibodies. MAbs 2013; 5:279-87. [PMID: 23412646 DOI: 10.4161/mabs.23539] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Antibody-based therapeutics are of great value for the treatment of human diseases. In addition to functional activity, affinity or physico-chemical properties, antibody specificity is considered to be one of the most crucial attributes for safety and efficacy. Consequently, appropriate studies are required before entering clinical trials. High content protein arrays are widely applied to assess antibody specificity, but this commercial solution can only be applied to final therapeutic antibody candidates because such arrays are expensive and their throughput is limited. A flexible, high-throughput and economical assay that allows specificity testing of IgG or Fab molecules during early discovery is described here. The 384-well microtiter plate assay contains a comprehensive panel of 32 test proteins and uses electrochemiluminescence as readout. The Protein Panel Profiling ( 3P) was used to analyze marketed therapeutic antibodies that all showed highly specific binding profiles. Subsequently, 3P was applied to antibody candidates from early discovery and the results compared well with those obtained with a commercially available high content protein chip. Our results suggest that 3P can be applied as an additional filter for lead selection, allowing the identification of favorable antibody candidates in early discovery and thereby increasing the speed and possibility of success in drug development.
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Affiliation(s)
- Katrin Frese
- Protein Sciences Department, MorphoSys AG, Martinsried/Planegg, Germany
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103
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Sherwood LJ, Hayhurst A. Hapten mediated display and pairing of recombinant antibodies accelerates assay assembly for biothreat countermeasures. Sci Rep 2012; 2:807. [PMID: 23150778 PMCID: PMC3495282 DOI: 10.1038/srep00807] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2012] [Accepted: 10/17/2012] [Indexed: 11/14/2022] Open
Abstract
A bottle-neck in recombinant antibody sandwich immunoassay development is pairing, demanding protein purification and modification to distinguish captor from tracer. We developed a simple pairing scheme using microliter amounts of E. coli osmotic shockates bearing site-specific biotinylated antibodies and demonstrated proof of principle with a single domain antibody (sdAb) that is both captor and tracer for polyvalent Marburgvirus nucleoprotein. The system could also host pairs of different sdAb specific for the 7 botulinum neurotoxin (BoNT) serotypes, enabling recognition of the cognate serotype. Inducible supE co-expression enabled sdAb populations to be propagated as either phage for more panning from repertoires or expressed as soluble sdAb for screening within a single host strain. When combined with streptavidin-g3p fusions, a novel transdisplay system was formulated to retrofit a semi-synthetic sdAb library which was mined for an anti-Ebolavirus sdAb which was immediately immunoassay ready, thereby speeding up the recombinant antibody discovery and utilization processes.
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Affiliation(s)
- Laura J. Sherwood
- Department of Virology and Immunology, Texas Biomedical Research Institute, San Antonio, Texas, USA
| | - Andrew Hayhurst
- Department of Virology and Immunology, Texas Biomedical Research Institute, San Antonio, Texas, USA
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105
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Løset GÅ, Sandlie I. Next generation phage display by use of pVII and pIX as display scaffolds. Methods 2012; 58:40-6. [PMID: 22819858 DOI: 10.1016/j.ymeth.2012.07.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2012] [Accepted: 07/11/2012] [Indexed: 10/28/2022] Open
Abstract
Phage display technology has evolved to become an extremely versatile and powerful platform for protein engineering. The robustness of the phage particle, its ease of handling and its ability to tolerate a range of different capsid fusions are key features that explain the dominance of phage display in combinatorial engineering. Implementation of new technology is likely to ensure the continuation of its success, but has also revealed important short comings inherent to current phage display systems. This is in particular related to the biology of the two most popular display capsids, namely pIII and pVIII. Recent findings using two alternative capsids, pVII and pIX, located to the phage tip opposite that of pIII, suggest how they may be exploited to alleviate or circumvent many of these short comings. This review addresses important aspects of the current phage display standard and then discusses the use of pVII and pIX. These may both complement current systems and be used as alternative scaffolds for display and selection to further improve phage display as the ultimate combinatorial engineering platform.
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Affiliation(s)
- Geir Åge Løset
- Centre for Immune Regulation, University of Oslo, N-316 Oslo, Norway.
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106
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Yu CM, Peng HP, Chen IC, Lee YC, Chen JB, Tsai KC, Chen CT, Chang JY, Yang EW, Hsu PC, Jian JW, Hsu HJ, Chang HJ, Hsu WL, Huang KF, Ma AC, Yang AS. Rationalization and design of the complementarity determining region sequences in an antibody-antigen recognition interface. PLoS One 2012; 7:e33340. [PMID: 22457753 PMCID: PMC3310866 DOI: 10.1371/journal.pone.0033340] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2011] [Accepted: 02/14/2012] [Indexed: 12/01/2022] Open
Abstract
Protein-protein interactions are critical determinants in biological systems. Engineered proteins binding to specific areas on protein surfaces could lead to therapeutics or diagnostics for treating diseases in humans. But designing epitope-specific protein-protein interactions with computational atomistic interaction free energy remains a difficult challenge. Here we show that, with the antibody-VEGF (vascular endothelial growth factor) interaction as a model system, the experimentally observed amino acid preferences in the antibody-antigen interface can be rationalized with 3-dimensional distributions of interacting atoms derived from the database of protein structures. Machine learning models established on the rationalization can be generalized to design amino acid preferences in antibody-antigen interfaces, for which the experimental validations are tractable with current high throughput synthetic antibody display technologies. Leave-one-out cross validation on the benchmark system yielded the accuracy, precision, recall (sensitivity) and specificity of the overall binary predictions to be 0.69, 0.45, 0.63, and 0.71 respectively, and the overall Matthews correlation coefficient of the 20 amino acid types in the 24 interface CDR positions was 0.312. The structure-based computational antibody design methodology was further tested with other antibodies binding to VEGF. The results indicate that the methodology could provide alternatives to the current antibody technologies based on animal immune systems in engineering therapeutic and diagnostic antibodies against predetermined antigen epitopes.
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Affiliation(s)
- Chung-Ming Yu
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
| | - Hung-Pin Peng
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
- Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan
- Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Ing-Chien Chen
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
| | - Yu-Ching Lee
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
| | - Jun-Bo Chen
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
- Department of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan
| | | | - Ching-Tai Chen
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
- Institute of Bioinformatics and Systems Biology, National Chiao-Tung University, Hsinchu, Taiwan
- Institute of Information Sciences, Academia Sinica, Taipei, Taiwan
- Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Jeng-Yih Chang
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
| | - Ei-Wen Yang
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
- Institute of Information Sciences, Academia Sinica, Taipei, Taiwan
| | - Po-Chiang Hsu
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
| | - Jhih-Wei Jian
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
- Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan
- Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Hung-Ju Hsu
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
- Graduate Institute of Life Sciences, National Defense University, Taipei, Taiwan
| | - Hung-Ju Chang
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
- Institute of Biochemical Science, National Taiwan University, Taipei, Taiwan
- Chemical Biology and Molecular Biophysics Program, Taiwan International Graduate Program, Institute of Biological Chemistry, Academia Sinica, Taipei, Taiwan
| | - Wen-Lian Hsu
- Institute of Information Sciences, Academia Sinica, Taipei, Taiwan
| | - Kai-Fa Huang
- Institute of Biological Chemistry, Academia Sinica, Taipei, Taiwan
| | - Alex Che Ma
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
| | - An-Suei Yang
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
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107
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Lee EC, Owen M. The application of transgenic mice for therapeutic antibody discovery. Methods Mol Biol 2012; 901:137-48. [PMID: 22723098 DOI: 10.1007/978-1-61779-931-0_8] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
In 2006, panitumumab, the first fully human antibody generated from transgenic mice, was approved for clinical use by the US Food and Drug Administration (FDA). Since then, a further seven such antibodies have been approved. In this chapter, we discuss how transgenic mice technologies can provide a powerful platform for creating human therapeutic antibodies.
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108
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Geyer CR, McCafferty J, Dübel S, Bradbury ARM, Sidhu SS. Recombinant antibodies and in vitro selection technologies. Methods Mol Biol 2012; 901:11-32. [PMID: 22723092 DOI: 10.1007/978-1-61779-931-0_2] [Citation(s) in RCA: 51] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Over the past decade, the accumulation of detailed knowledge of antibody structure and function has enabled antibody phage display to emerge as a powerful in vitro alternative to hybridoma methods for creating antibodies. Many antibodies produced using phage display technology have unique properties that are not obtainable using traditional hybridoma technologies. In phage display, selections are performed under controlled, in vitro conditions that are tailored to suit demands of the antigen and the sequence encoding the antibody is immediately available. These features obviate many of the limitations of hybridoma methodology, and because the entire process relies on scalable molecular biology techniques, phage display is also suitable for high-throughput applications. Thus, antibody phage display technology is well suited for genome-scale biotechnology and therapeutic applications. This review describes the antibody phage display technology and highlights examples of antibodies with unique properties that cannot easily be obtained by other technologies.
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109
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Production and characterization of peptide antibodies. Methods 2011; 56:136-44. [PMID: 22178691 DOI: 10.1016/j.ymeth.2011.12.001] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2011] [Revised: 11/30/2011] [Accepted: 12/02/2011] [Indexed: 12/18/2022] Open
Abstract
Proteins are effective immunogens for generation of antibodies. However, occasionally the native protein is known but not available for antibody production. In such cases synthetic peptides derived from the native protein are good alternatives for antibody production. These peptide antibodies are powerful tools in experimental biology and are easily produced to any peptide of choice. A widely used approach for production of peptide antibodies is to immunize animals with a synthetic peptide coupled to a carrier protein. Very important is the selection of the synthetic peptide, where factors such as structure, accessibility and amino acid composition are crucial. Since small peptides tend not to be immunogenic, it may be necessary to conjugate them to carrier proteins in order to enhance immune presentation. Several strategies for conjugation of peptide-carriers applied for immunization exist, including solid-phase peptide-carrier conjugation and peptide-carrier conjugation in solution. Upon immunization, adjuvants such as Al(OH)(3) are added together with the immunogenic peptide-carrier conjugate, which usually leads to high-titred antisera. Following immunization and peptide antibody purification, the antibodies are characterized based on their affinity or specificity. An efficient approach for characterization of peptide antibodies is epitope mapping using peptide based assays. This review describes standard solid-phase approaches for generation of peptide antibodies with special emphasis on peptide selection, generation of peptide conjugates for immunization and characterization of the resulting peptide antibodies.
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