1
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Khalek IS, Senji Laxme RR, Nguyen YTK, Khochare S, Patel RN, Woehl J, Smith JM, Saye-Francisco K, Kim Y, Misson Mindrebo L, Tran Q, Kędzior M, Boré E, Limbo O, Verma M, Stanfield RL, Menzies SK, Ainsworth S, Harrison RA, Burton DR, Sok D, Wilson IA, Casewell NR, Sunagar K, Jardine JG. Synthetic development of a broadly neutralizing antibody against snake venom long-chain α-neurotoxins. Sci Transl Med 2024; 16:eadk1867. [PMID: 38381847 DOI: 10.1126/scitranslmed.adk1867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 01/26/2024] [Indexed: 02/23/2024]
Abstract
Snakebite envenoming is a major global public health concern for which improved therapies are urgently needed. The antigenic diversity present in snake venom toxins from various species presents a considerable challenge to the development of a universal antivenom. Here, we used a synthetic human antibody library to find and develop an antibody that neutralizes long-chain three-finger α-neurotoxins produced by numerous medically relevant snakes. Our antibody bound diverse toxin variants with high affinity, blocked toxin binding to the nicotinic acetylcholine receptor in vitro, and protected mice from lethal venom challenge. Structural analysis of the antibody-toxin complex revealed a binding mode that mimics the receptor-toxin interaction. The overall workflow presented is generalizable for the development of antibodies that target conserved epitopes among antigenically diverse targets, and it offers a promising framework for the creation of a monoclonal antibody-based universal antivenom to treat snakebite envenoming.
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Affiliation(s)
- Irene S Khalek
- Department of Immunology and Microbiology, Scripps Research Institute, La Jolla, CA 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research Institute, La Jolla, CA 92037, USA
- IAVI, New York, NY 10004, USA
| | - R R Senji Laxme
- IAVI Neutralizing Antibody Center, Scripps Research Institute, La Jolla, CA 92037, USA
- Evolutionary Venomics Lab, Centre for Ecological Sciences, Indian Institute of Science, Bangalore 560012, Karnataka, India
| | - Yen Thi Kim Nguyen
- Department of Integrative Structural and Computational Biology, Scripps Research Institute, La Jolla, CA 92037, USA
| | - Suyog Khochare
- Evolutionary Venomics Lab, Centre for Ecological Sciences, Indian Institute of Science, Bangalore 560012, Karnataka, India
| | - Rohit N Patel
- Centre for Snakebite Research & Interventions, Department of Tropical Disease Biology, Liverpool School of Tropical Medicine, Liverpool L3 5QA, UK
| | - Jordan Woehl
- Department of Immunology and Microbiology, Scripps Research Institute, La Jolla, CA 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research Institute, La Jolla, CA 92037, USA
- IAVI, New York, NY 10004, USA
| | - Jessica M Smith
- Department of Immunology and Microbiology, Scripps Research Institute, La Jolla, CA 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research Institute, La Jolla, CA 92037, USA
- IAVI, New York, NY 10004, USA
| | - Karen Saye-Francisco
- Department of Immunology and Microbiology, Scripps Research Institute, La Jolla, CA 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research Institute, La Jolla, CA 92037, USA
| | - Yoojin Kim
- Department of Immunology and Microbiology, Scripps Research Institute, La Jolla, CA 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research Institute, La Jolla, CA 92037, USA
- IAVI, New York, NY 10004, USA
| | - Laetitia Misson Mindrebo
- Department of Immunology and Microbiology, Scripps Research Institute, La Jolla, CA 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research Institute, La Jolla, CA 92037, USA
- IAVI, New York, NY 10004, USA
| | - Quoc Tran
- Department of Immunology and Microbiology, Scripps Research Institute, La Jolla, CA 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research Institute, La Jolla, CA 92037, USA
- IAVI, New York, NY 10004, USA
| | - Mateusz Kędzior
- Department of Immunology and Microbiology, Scripps Research Institute, La Jolla, CA 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research Institute, La Jolla, CA 92037, USA
- IAVI, New York, NY 10004, USA
| | - Evy Boré
- Centre for Snakebite Research & Interventions, Department of Tropical Disease Biology, Liverpool School of Tropical Medicine, Liverpool L3 5QA, UK
| | - Oliver Limbo
- Department of Immunology and Microbiology, Scripps Research Institute, La Jolla, CA 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research Institute, La Jolla, CA 92037, USA
- IAVI, New York, NY 10004, USA
| | - Megan Verma
- Department of Immunology and Microbiology, Scripps Research Institute, La Jolla, CA 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research Institute, La Jolla, CA 92037, USA
- IAVI, New York, NY 10004, USA
| | - Robyn L Stanfield
- Department of Integrative Structural and Computational Biology, Scripps Research Institute, La Jolla, CA 92037, USA
| | - Stefanie K Menzies
- Centre for Snakebite Research & Interventions, Department of Tropical Disease Biology, Liverpool School of Tropical Medicine, Liverpool L3 5QA, UK
| | - Stuart Ainsworth
- Centre for Snakebite Research & Interventions, Department of Tropical Disease Biology, Liverpool School of Tropical Medicine, Liverpool L3 5QA, UK
| | - Robert A Harrison
- Centre for Snakebite Research & Interventions, Department of Tropical Disease Biology, Liverpool School of Tropical Medicine, Liverpool L3 5QA, UK
| | - Dennis R Burton
- Department of Immunology and Microbiology, Scripps Research Institute, La Jolla, CA 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research Institute, La Jolla, CA 92037, USA
- Consortium for HIV/AIDS Vaccine Development (CHAVD), Scripps Research Institute, La Jolla, CA 92037, USA
- Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard University, Cambridge, MA 02139, USA
| | - Devin Sok
- Department of Immunology and Microbiology, Scripps Research Institute, La Jolla, CA 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research Institute, La Jolla, CA 92037, USA
- IAVI, New York, NY 10004, USA
- Consortium for HIV/AIDS Vaccine Development (CHAVD), Scripps Research Institute, La Jolla, CA 92037, USA
| | - Ian A Wilson
- Department of Integrative Structural and Computational Biology, Scripps Research Institute, La Jolla, CA 92037, USA
- Skaggs Institute for Chemical Biology, Scripps Research Institute, La Jolla, CA 92037, USA
| | - Nicholas R Casewell
- Centre for Snakebite Research & Interventions, Department of Tropical Disease Biology, Liverpool School of Tropical Medicine, Liverpool L3 5QA, UK
| | - Kartik Sunagar
- Evolutionary Venomics Lab, Centre for Ecological Sciences, Indian Institute of Science, Bangalore 560012, Karnataka, India
| | - Joseph G Jardine
- Department of Immunology and Microbiology, Scripps Research Institute, La Jolla, CA 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research Institute, La Jolla, CA 92037, USA
- IAVI, New York, NY 10004, USA
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2
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Krohl PJ, Fine J, Yang H, VanDyke D, Ang Z, Kim KB, Thomas-Tikhonenko A, Spangler JB. Discovery of antibodies targeting multipass transmembrane proteins using a suspension cell-based evolutionary approach. CELL REPORTS METHODS 2023; 3:100429. [PMID: 37056366 PMCID: PMC10088246 DOI: 10.1016/j.crmeth.2023.100429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 01/06/2023] [Accepted: 02/21/2023] [Indexed: 04/15/2023]
Abstract
Due to their critical functions in cell sensing and signal processing, membrane proteins are highly preferred as pharmacological targets, and antibody drugs constitute the fastest growing category of therapeutic agents on the pharmaceutical market. However, major limitations exist in developing antibodies that recognize complex, multipass transmembrane proteins, such as G-protein-coupled receptors (GPCRs). These challenges, largely due to difficulties with recombinant expression of multipass transmembrane proteins, can be overcome using whole-cell screening techniques, which enable presentation of the functional antigen in its native conformation. Here, we developed suspension cell-based whole-cell panning methodologies to screen for specific binders against GPCRs within a naive yeast-displayed antibody library. We implemented our strategy to discover high-affinity antibodies against four distinct GPCR target proteins, demonstrating the potential for our cell-based screening workflow to advance the discovery of antibody therapeutics targeting membrane proteins.
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Affiliation(s)
- Patrick J. Krohl
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21208, USA
| | - Justyn Fine
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21208, USA
| | - Huilin Yang
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21208, USA
| | - Derek VanDyke
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21208, USA
| | - Zhiwei Ang
- Division of Cancer Pathobiology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Kook Bum Kim
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21208, USA
| | - Andrei Thomas-Tikhonenko
- Division of Cancer Pathobiology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology & Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jamie B. Spangler
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21208, USA
- Department of Biology, Johns Hopkins University, Baltimore, MD 21208, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21208, USA
- Translational Tissue Engineering Center, Johns Hopkins University, Baltimore, MD 21231, USA
- Department of Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
- Bloomberg∼Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University, Baltimore, MD 21287, USA
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University Baltimore, MD 21231, USA
- Department of Ophthalmology, Johns Hopkins University, Baltimore, MD 21287, USA
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3
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Svilenov HL, Arosio P, Menzen T, Tessier P, Sormanni P. Approaches to expand the conventional toolbox for discovery and selection of antibodies with drug-like physicochemical properties. MAbs 2023; 15:2164459. [PMID: 36629855 PMCID: PMC9839375 DOI: 10.1080/19420862.2022.2164459] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/22/2022] [Accepted: 12/29/2022] [Indexed: 01/12/2023] Open
Abstract
Antibody drugs should exhibit not only high-binding affinity for their target antigens but also favorable physicochemical drug-like properties. Such drug-like biophysical properties are essential for the successful development of antibody drug products. The traditional approaches used in antibody drug development require significant experimentation to produce, optimize, and characterize many candidates. Therefore, it is attractive to integrate new methods that can optimize the process of selecting antibodies with both desired target-binding and drug-like biophysical properties. Here, we summarize a selection of techniques that can complement the conventional toolbox used to de-risk antibody drug development. These techniques can be integrated at different stages of the antibody development process to reduce the frequency of physicochemical liabilities in antibody libraries during initial discovery and to co-optimize multiple antibody features during early-stage antibody engineering and affinity maturation. Moreover, we highlight biophysical and computational approaches that can be used to predict physical degradation pathways relevant for long-term storage and in-use stability to reduce the need for extensive experimentation.
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Affiliation(s)
- Hristo L. Svilenov
- Laboratory of General Biochemistry and Physical Pharmacy, Faculty of Pharmaceutical Sciences, Ghent University, Gent, Belgium
| | - Paolo Arosio
- Department of Chemistry and Applied Biosciences, ETH Zürich, Zürich, Switzerland
| | - Tim Menzen
- Coriolis Pharma Research GmbH, Martinsried, 82152, Germany
| | - Peter 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, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Pietro Sormanni
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
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4
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Harvey EP, Shin JE, Skiba MA, Nemeth GR, Hurley JD, Wellner A, Shaw AY, Miranda VG, Min JK, Liu CC, Marks DS, Kruse AC. An in silico method to assess antibody fragment polyreactivity. Nat Commun 2022; 13:7554. [PMID: 36477674 PMCID: PMC9729196 DOI: 10.1038/s41467-022-35276-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 11/22/2022] [Indexed: 12/12/2022] Open
Abstract
Antibodies are essential biological research tools and important therapeutic agents, but some exhibit non-specific binding to off-target proteins and other biomolecules. Such polyreactive antibodies compromise screening pipelines, lead to incorrect and irreproducible experimental results, and are generally intractable for clinical development. Here, we design a set of experiments using a diverse naïve synthetic camelid antibody fragment (nanobody) library to enable machine learning models to accurately assess polyreactivity from protein sequence (AUC > 0.8). Moreover, our models provide quantitative scoring metrics that predict the effect of amino acid substitutions on polyreactivity. We experimentally test our models' performance on three independent nanobody scaffolds, where over 90% of predicted substitutions successfully reduced polyreactivity. Importantly, the models allow us to diminish the polyreactivity of an angiotensin II type I receptor antagonist nanobody, without compromising its functional properties. We provide a companion web-server that offers a straightforward means of predicting polyreactivity and polyreactivity-reducing mutations for any given nanobody sequence.
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Affiliation(s)
- Edward P Harvey
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Jung-Eun Shin
- Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Meredith A Skiba
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Genevieve R Nemeth
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Joseph D Hurley
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Alon Wellner
- Department of Chemistry, University of California, Irvine, CA, 92697, USA
- Department of Molecular Biology & Biochemistry, University of California, Irvine, CA, 92697, USA
- Department of Biomedical Engineering, University of California, Irvine, CA, 92692, USA
| | - Ada Y Shaw
- Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Victor G Miranda
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Joseph K Min
- Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Chang C Liu
- Department of Chemistry, University of California, Irvine, CA, 92697, USA
- Department of Molecular Biology & Biochemistry, University of California, Irvine, CA, 92697, USA
- Department of Biomedical Engineering, University of California, Irvine, CA, 92692, USA
| | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA.
| | - Andrew C Kruse
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA.
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5
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Lim H, No KT. Prediction of polyreactive and nonspecific single-chain fragment variables through structural biochemical features and protein language-based descriptors. BMC Bioinformatics 2022; 23:520. [PMID: 36471239 PMCID: PMC9720949 DOI: 10.1186/s12859-022-05010-4] [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: 09/13/2022] [Accepted: 10/26/2022] [Indexed: 12/09/2022] Open
Abstract
BACKGROUND Monoclonal antibodies (mAbs) have been used as therapeutic agents, which must overcome many developability issues after the discovery from in vitro display libraries. Especially, polyreactive mAbs can strongly bind to a specific target and weakly bind to off-target proteins, which leads to poor antibody pharmacokinetics in clinical development. Although early assessment of polyreactive mAbs is important in the early discovery stage, experimental assessments are usually time-consuming and expensive. Therefore, computational approaches for predicting the polyreactivity of single-chain fragment variables (scFvs) in the early discovery stage would be promising for reducing experimental efforts. RESULTS Here, we made prediction models for the polyreactivity of scFvs with the known polyreactive antibody features and natural language model descriptors. We predicted 19,426 protein structures of scFvs with trRosetta to calculate the polyreactive antibody features and investigated the classifying performance of each factor for polyreactivity. In the known polyreactive features, the net charge of the CDR2 loop, the tryptophan and glycine residues in CDR-H3, and the lengths of the CDR1 and CDR2 loops, importantly contributed to the performance of the models. Additionally, the hydrodynamic features, such as partial specific volume, gyration radius, and isoelectric points of CDR loops and scFvs, were newly added to improve model performance. Finally, we made the prediction model with a robust performance ([Formula: see text]) with an ensemble learning of the top 3 best models. CONCLUSION The prediction models for polyreactivity would help assess polyreactive scFvs in the early discovery stage and our approaches would be promising to develop machine learning models with quantitative data from high throughput assays for antibody screening.
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Affiliation(s)
- Hocheol Lim
- The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon, 21983, Republic of Korea
- Bioinformatics and Molecular Design Research Center (BMDRC), Incheon, 21983, Republic of Korea
| | - Kyoung Tai No
- The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon, 21983, Republic of Korea.
- Bioinformatics and Molecular Design Research Center (BMDRC), Incheon, 21983, Republic of Korea.
- Baobab AiBIO Co., Ltd., Incheon, 21983, Republic of Korea.
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6
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Saksena SD, Liu G, Banholzer C, Horny G, Ewert S, Gifford DK. Computational counterselection identifies nonspecific therapeutic biologic candidates. CELL REPORTS METHODS 2022; 2:100254. [PMID: 35880012 PMCID: PMC9308162 DOI: 10.1016/j.crmeth.2022.100254] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 04/22/2022] [Accepted: 06/17/2022] [Indexed: 01/13/2023]
Abstract
Effective biologics require high specificity and limited off-target binding, but these properties are not guaranteed by current affinity-selection-based discovery methods. Molecular counterselection against off targets is a technique for identifying nonspecific sequences but is experimentally costly and can fail to eliminate a large fraction of nonspecific sequences. Here, we introduce computational counterselection, a framework for removing nonspecific sequences from pools of candidate biologics using machine learning models. We demonstrate the method using sequencing data from single-target affinity selection of antibodies, bypassing combinatorial experiments. We show that computational counterselection outperforms molecular counterselection by performing cross-target selection and individual binding assays to determine the performance of each method at retaining on-target, specific antibodies and identifying and eliminating off-target, nonspecific antibodies. Further, we show that one can identify generally polyspecific antibody sequences using a general model trained on affinity data from unrelated targets with potential affinity for a broad range of sequences.
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Affiliation(s)
- Sachit Dinesh Saksena
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ge Liu
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | | | - Geraldine Horny
- Novartis Institute of BioMedical Research (NIBR), Basel, Switzerland
| | - Stefan Ewert
- Novartis Institute of BioMedical Research (NIBR), Basel, Switzerland
| | - David K Gifford
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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7
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Gao B, Han J, Reddy ST. Learning what not to select for in antibody drug discovery. CELL REPORTS METHODS 2022; 2:100258. [PMID: 35880020 PMCID: PMC9308151 DOI: 10.1016/j.crmeth.2022.100258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Identifying antibodies with high affinity and target specificity is crucial for drug discovery and development; however, filtering out antibody candidates with nonspecific or polyspecific binding profiles is also important. In this issue of Cell Reports Methods, Saksena et al. report a computational counterselection method combining deep sequencing and machine learning for identifying nonspecific antibody candidates and demonstrate that it has advantages over more established molecular counterselection methods.
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Affiliation(s)
- Beichen Gao
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
| | - Jiami Han
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
| | - Sai T. Reddy
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
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8
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Krohl PJ, Spangler JB. A Hybrid Adherent/Suspension Cell-Based Selection Strategy for Discovery of Antibodies Targeting Membrane Proteins. Methods Mol Biol 2022; 2491:195-216. [PMID: 35482192 PMCID: PMC9667817 DOI: 10.1007/978-1-0716-2285-8_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Membrane proteins are favored drug targets and antibody therapeutics represent the fastest-growing category of pharmaceuticals. However, there remains a need for rapid and effective approaches for the discovery of antibodies that recognize membrane proteins to develop a robust clinical pipeline for targeted therapeutics. The challenges associated with recombinant expression of membrane proteins make whole cell screening techniques desirable, as these strategies allow presentation of the target membrane proteins in their native conformations. Here, we describe a workflow that employs both adherent cell-based and suspension cell-based whole cell panning methodologies to enrich for specific binders within a yeast-displayed antibody library. The first round of selection consists of an adherent cell-based approach, wherein a diverse library is panned over target-expressing mammalian cell monolayers in order to debulk the naïve library. Subsequent rounds involve the use of suspension cell-based approaches, facilitated with magnetic-activated cell sorting (MACS) or fluorescence-activated cell sorting (FACS), to achieve further library enrichment. Finally, we describe a high-throughput approach to screen target binding and specificity of individual clones isolated from selection campaigns.
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Affiliation(s)
- Patrick J Krohl
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jamie B Spangler
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Translational Tissue Engineering Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Bloomberg~Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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9
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Kanduc D. From Genetics to Epigenetics: Top 4 Aspects for Improved SARS-CoV-2 Vaccine Designs as Paradigmatic Examples. Glob Med Genet 2021; 9:14-17. [PMID: 35169778 PMCID: PMC8837413 DOI: 10.1055/s-0041-1739495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 09/29/2021] [Indexed: 11/03/2022] Open
Abstract
AbstractThis literature review described the genetic and biochemical factors that may have been overlooked in the formulation of vaccines and that most likely underlie possible issues with mass vaccination.
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Affiliation(s)
- Darja Kanduc
- Department of Biosciences, Biotechnologies and Biopharmaceutics, University of Bari, Bari, Italy
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10
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Shin JE, Riesselman AJ, Kollasch AW, McMahon C, Simon E, Sander C, Manglik A, Kruse AC, Marks DS. Protein design and variant prediction using autoregressive generative models. Nat Commun 2021; 12:2403. [PMID: 33893299 PMCID: PMC8065141 DOI: 10.1038/s41467-021-22732-w] [Citation(s) in RCA: 130] [Impact Index Per Article: 43.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 03/26/2021] [Indexed: 12/11/2022] Open
Abstract
The ability to design functional sequences and predict effects of variation is central to protein engineering and biotherapeutics. State-of-art computational methods rely on models that leverage evolutionary information but are inadequate for important applications where multiple sequence alignments are not robust. Such applications include the prediction of variant effects of indels, disordered proteins, and the design of proteins such as antibodies due to the highly variable complementarity determining regions. We introduce a deep generative model adapted from natural language processing for prediction and design of diverse functional sequences without the need for alignments. The model performs state-of-art prediction of missense and indel effects and we successfully design and test a diverse 105-nanobody library that shows better expression than a 1000-fold larger synthetic library. Our results demonstrate the power of the alignment-free autoregressive model in generalizing to regions of sequence space traditionally considered beyond the reach of prediction and design.
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Affiliation(s)
- Jung-Eun Shin
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Adam J Riesselman
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- insitro, South San Francisco, CA, USA
| | - Aaron W Kollasch
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Conor McMahon
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
- Vertex Pharmaceuticals, Boston, MA, USA
| | - Elana Simon
- Harvard College, Cambridge, MA, USA
- Reverie Labs, Cambridge, MA, USA
| | - Chris Sander
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Aashish Manglik
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA
- Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA, USA
| | - Andrew C Kruse
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA.
| | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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11
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Lecerf M, Kanyavuz A, Rossini S, Dimitrov JD. Interaction of clinical-stage antibodies with heme predicts their physiochemical and binding qualities. Commun Biol 2021; 4:391. [PMID: 33758329 PMCID: PMC7988133 DOI: 10.1038/s42003-021-01931-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 02/23/2021] [Indexed: 11/09/2022] Open
Abstract
Immunoglobulin repertoires contain a fraction of antibodies that recognize low molecular weight compounds, including some enzymes' cofactors, such as heme. Here, by using a set of 113 samples with variable region sequences matching clinical-stage antibodies, we demonstrated that a considerable number of these antibodies interact with heme. Antibodies that interact with heme possess specific sequence traits of their antigen-binding regions. Moreover they manifest particular physicochemical and functional qualities i.e. increased hydrophobicity, higher propensity of self-binding, higher intrinsic polyreactivity and reduced expression yields. Thus, interaction with heme is a strong predictor of different molecular and functional qualities of antibodies. Notably, these qualities are of high importance for therapeutic antibodies, as their presence was associated with failure of drug candidates to reach clinic. Our study reveled an important facet of information about relationship sequence-function in antibodies. It also offers a convenient tool for detection of liabilities of therapeutic antibodies.
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Affiliation(s)
- Maxime Lecerf
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, F-75006, Paris, France
| | - Alexia Kanyavuz
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, F-75006, Paris, France
| | - Sofia Rossini
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, F-75006, Paris, France
| | - Jordan D Dimitrov
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, F-75006, Paris, France.
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12
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Sawant MS, Streu CN, Wu L, Tessier PM. Toward Drug-Like Multispecific Antibodies by Design. Int J Mol Sci 2020; 21:E7496. [PMID: 33053650 PMCID: PMC7589779 DOI: 10.3390/ijms21207496] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 10/02/2020] [Accepted: 10/02/2020] [Indexed: 12/18/2022] Open
Abstract
The success of antibody therapeutics is strongly influenced by their multifunctional nature that couples antigen recognition mediated by their variable regions with effector functions and half-life extension mediated by a subset of their constant regions. Nevertheless, the monospecific IgG format is not optimal for many therapeutic applications, and this has led to the design of a vast number of unique multispecific antibody formats that enable targeting of multiple antigens or multiple epitopes on the same antigen. Despite the diversity of these formats, a common challenge in generating multispecific antibodies is that they display suboptimal physical and chemical properties relative to conventional IgGs and are more difficult to develop into therapeutics. Here we review advances in the design and engineering of multispecific antibodies with drug-like properties, including favorable stability, solubility, viscosity, specificity and pharmacokinetic properties. We also highlight emerging experimental and computational methods for improving the next generation of multispecific antibodies, as well as their constituent antibody fragments, with natural IgG-like properties. Finally, we identify several outstanding challenges that need to be addressed to increase the success of multispecific antibodies in the clinic.
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Affiliation(s)
- Manali S. Sawant
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA; (M.S.S.); (C.N.S.)
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Craig N. Streu
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA; (M.S.S.); (C.N.S.)
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA;
- Department of Chemistry, Albion College, Albion, MI 49224, 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
| | - Peter M. Tessier
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA; (M.S.S.); (C.N.S.)
- 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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13
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Krohl PJ, Kim KB, Lew L, VanDyke D, Ludwig SD, Spangler JB. A suspension cell‐based interaction platform for interrogation of membrane proteins. AIChE J 2020. [DOI: 10.1002/aic.16995] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Patrick J. Krohl
- Department of Chemical & Biomolecular Engineering Johns Hopkins University Baltimore Maryland USA
| | - Kook Bum Kim
- Department of Chemical & Biomolecular Engineering Johns Hopkins University Baltimore Maryland USA
| | - Lance Lew
- Department of Biophysics Johns Hopkins University Baltimore Maryland USA
| | - Derek VanDyke
- Department of Chemical & Biomolecular Engineering Johns Hopkins University Baltimore Maryland USA
| | - Seth D. Ludwig
- Department of Chemical & Biomolecular Engineering Johns Hopkins University Baltimore Maryland USA
| | - Jamie B. Spangler
- Department of Chemical & Biomolecular Engineering Johns Hopkins University Baltimore Maryland USA
- Department of Biomedical Engineering Johns Hopkins University School of Medicine Baltimore Maryland USA
- Translational Tissue Engineering Center Johns Hopkins University School of Medicine Baltimore Maryland USA
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14
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Zhang Y, Wu L, Gupta P, Desai AA, Smith MD, Rabia LA, Ludwig SD, Tessier PM. Physicochemical Rules for Identifying Monoclonal Antibodies with Drug-like Specificity. Mol Pharm 2020; 17:2555-2569. [PMID: 32453957 PMCID: PMC7936472 DOI: 10.1021/acs.molpharmaceut.0c00257] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The ability of antibodies to recognize their target antigens with high specificity is fundamental to their natural function. Nevertheless, therapeutic antibodies display variable and difficult-to-predict levels of nonspecific and self-interactions that can lead to various drug development challenges, including antibody aggregation, abnormally high viscosity, and rapid antibody clearance. Here we report a method for predicting the overall specificity of antibodies in terms of their relative risk for displaying high levels of nonspecific or self-interactions at physiological conditions. We find that individual and combined sets of chemical rules that limit the maximum and minimum numbers of certain solvent-exposed amino acids in antibody variable regions are strong predictors of specificity for large panels of preclinical and clinical-stage antibodies. We also demonstrate how the chemical rules can be used to identify sites that mediate nonspecific interactions in suboptimal antibodies and guide the design of targeted sublibraries that yield variants with high antibody specificity. These findings can be readily used to improve the selection and engineering of antibodies with drug-like specificity.
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Affiliation(s)
- Yulei Zhang
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Lina Wu
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Priyanka Gupta
- Department of Biochemistry and Biophysics, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
- Biotherapeutics Discovery Department, Boehringer Ingelheim, Ridgefield, CT 06877
| | - Alec A. Desai
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Matthew D. Smith
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Lilia A. Rabia
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
- Isermann Department of Chemical & Biological Engineering, Troy, NY 12180, USA
| | - Seth D. Ludwig
- Isermann Department of Chemical & Biological Engineering, Troy, NY 12180, USA
| | - Peter M. Tessier
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
- Isermann Department of Chemical & Biological Engineering, Troy, NY 12180, USA
- Department of Biochemistry and Biophysics, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
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15
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Lecerf M, Kanyavuz A, Lacroix-Desmazes S, Dimitrov JD. Sequence features of variable region determining physicochemical properties and polyreactivity of therapeutic antibodies. Mol Immunol 2019; 112:338-346. [DOI: 10.1016/j.molimm.2019.06.012] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 06/17/2019] [Accepted: 06/17/2019] [Indexed: 12/17/2022]
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16
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Rabia LA, Zhang Y, Ludwig SD, Julian MC, Tessier PM. Net charge of antibody complementarity-determining regions is a key predictor of specificity. Protein Eng Des Sel 2019; 31:409-418. [PMID: 30770934 DOI: 10.1093/protein/gzz002] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 12/23/2018] [Accepted: 01/18/2019] [Indexed: 11/14/2022] Open
Abstract
Specificity is one of the most important and complex properties that is central to both natural antibody function and therapeutic antibody efficacy. However, it has proven extremely challenging to define robust guidelines for predicting antibody specificity. Here we evaluated the physicochemical determinants of antibody specificity for multiple panels of antibodies, including >100 clinical-stage antibodies. Surprisingly, we find that the theoretical net charge of the complementarity-determining regions (CDRs) is a strong predictor of antibody specificity. Antibodies with positively charged CDRs have a much higher risk of low specificity than antibodies with negatively charged CDRs. Moreover, the charge of the entire set of six CDRs is a much better predictor of antibody specificity than the charge of individual CDRs, variable domains (VH or VL) or the entire variable fragment (Fv). The best indicators of antibody specificity in terms of CDR amino acid composition are reduced levels of arginine and lysine and increased levels of aspartic and glutamic acid. Interestingly, clinical-stage antibodies with negatively charged CDRs also have a lower risk for poor biophysical properties in general, including a reduced risk for high levels of self-association. These findings provide powerful guidelines for predicting antibody specificity and for identifying safe and potent antibody therapeutics.
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Affiliation(s)
- Lilia A Rabia
- Isermann Department of Chemical & Biological Engineering, Center for Biotechnology & Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA.,Department of Pharmaceutical Sciences.,Department of Chemical Engineering
| | | | - Seth D Ludwig
- Isermann Department of Chemical & Biological Engineering, Center for Biotechnology & Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Mark C Julian
- Isermann Department of Chemical & Biological Engineering, Center for Biotechnology & Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Peter M Tessier
- Isermann Department of Chemical & Biological Engineering, Center for Biotechnology & Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA.,Department of Pharmaceutical Sciences.,Department of Chemical Engineering.,Department of Biomedical Engineering, Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
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17
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Julian MC, Rabia LA, Desai AA, Arsiwala A, Gerson JE, Paulson HL, Kane RS, Tessier PM. Nature-inspired design and evolution of anti-amyloid antibodies. J Biol Chem 2019; 294:8438-8451. [PMID: 30918024 DOI: 10.1074/jbc.ra118.004731] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Revised: 03/21/2019] [Indexed: 12/17/2022] Open
Abstract
Antibodies that recognize amyloidogenic aggregates with high conformational and sequence specificity are important for detecting and potentially treating a wide range of neurodegenerative disorders, including Alzheimer's and Parkinson's diseases. However, these types of antibodies are challenging to generate because of the large size, hydrophobicity, and heterogeneity of protein aggregates. To address this challenge, we developed a method for generating antibodies specific for amyloid aggregates. First, we grafted amyloidogenic peptide segments from the target polypeptide [Alzheimer's amyloid-β (Aβ) peptide] into the complementarity-determining regions (CDRs) of a stable antibody scaffold. Next, we diversified the grafted and neighboring CDR sites using focused mutagenesis to sample each WT or grafted residue, as well as one to five of the most commonly occurring amino acids at each site in human antibodies. Finally, we displayed these antibody libraries on the surface of yeast cells and selected antibodies that strongly recognize Aβ-amyloid fibrils and only weakly recognize soluble Aβ. We found that this approach enables the generation of monovalent and bivalent antibodies with nanomolar affinity for Aβ fibrils. These antibodies display high conformational and sequence specificity as well as low levels of nonspecific binding and recognize a conformational epitope at the extreme N terminus of human Aβ. We expect that this systematic approach will be useful for generating antibodies with conformational and sequence specificity against a wide range of peptide and protein aggregates associated with neurodegenerative disorders.
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Affiliation(s)
- Mark C Julian
- Isermann Department of Chemical and Biological Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York 12180
| | - Lilia A Rabia
- Isermann Department of Chemical and Biological Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York 12180; Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, Michigan 48109; Department of Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan 48109; Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan 48109
| | - Alec A Desai
- Department of Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan 48109; Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan 48109
| | - Ammar Arsiwala
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
| | - Julia E Gerson
- Department of Neurology, University of Michigan, Ann Arbor, Michigan 48109
| | - Henry L Paulson
- Department of Neurology, University of Michigan, Ann Arbor, Michigan 48109; Department of Protein Folding Disease Initiative, University of Michigan, Ann Arbor, Michigan 48109; Department of Michigan Alzheimer's Disease Center, University of Michigan, Ann Arbor, Michigan 48109
| | - Ravi S Kane
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
| | - Peter M Tessier
- Isermann Department of Chemical and Biological Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York 12180; Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, Michigan 48109; Department of Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan 48109; Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan 48109; Department of Protein Folding Disease Initiative, University of Michigan, Ann Arbor, Michigan 48109; Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan 48109.
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18
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Starr CG, Tessier PM. Selecting and engineering monoclonal antibodies with drug-like specificity. Curr Opin Biotechnol 2019; 60:119-127. [PMID: 30822699 DOI: 10.1016/j.copbio.2019.01.008] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Revised: 11/16/2018] [Accepted: 01/19/2019] [Indexed: 11/19/2022]
Abstract
Despite the recent explosion in the use of monoclonal antibodies (mAbs) as drugs, it remains a significant challenge to generate antibodies with a combination of physicochemical properties that are optimal for therapeutic applications. We argue that one of the most important and underappreciated drug-like antibody properties is high specificity - defined here as low levels of antibody non-specific and self-interactions - which is linked to low off-target binding and slow antibody clearance in vivo and high solubility and low viscosity in vitro. Here, we review the latest advances in characterizing antibody specificity and elucidating its molecular determinants as well as using these findings to improve the selection and engineering of antibodies with extremely high, drug-like specificity.
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Affiliation(s)
- Charles G Starr
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA; Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Peter M Tessier
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA; Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
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19
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Nguyen TD, Nagamune T, Kawahara M. A Suicide Switch Directly Eliminates Intracellular scFv Oligomers in the Cytoplasm of Mammalian Cells. Biotechnol J 2018; 14:e1800350. [PMID: 30171736 DOI: 10.1002/biot.201800350] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2018] [Revised: 08/25/2018] [Indexed: 11/10/2022]
Abstract
As intracellular antibodies (intrabodies) are highly promising tools for drug discovery, an innovative antibody screening platform in mammalian cells was previously developed by a single-chain Fv (scFv)-c-kit growth sensor, which successfully selected rabies nucleoprotein and phosphoprotein-specific intrabodies from a synthetic scFv library. Since the scFv-c-kit growth sensor releases a growth signal after forming oligomers due to binding to an oligomeric antigen, it is critical to use a library which does not contain self-oligomeric scFvs to avoid the off-target signal of the growth sensor. Here, a novel method to eliminate self-oligomeric scFvs directly in the cytoplasm of mammalian cells is presented. A suicide switch by fusing an scFv with a cell-death signaling domain to eliminate scFv oligomers is developed. It is found that among four cell-death signaling domains, a suicide switch by fusing scFv with Fas-associated death domain (FADD) can selectively reduce oligomeric scFvs. Furthermore, the library after eliminating scFv oligomers results in higher efficiency in the intrabody selection platform with a growth sensor. Collectively, the scFv-FADD suicide switch can be applied to eliminate oligomeric scFvs from a library, which can consequently improve the quality of intracellular scFv libraries and accelerate the discovery of intrabodies in the future.
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Affiliation(s)
- Thuy Duong Nguyen
- Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan
| | - Teruyuki Nagamune
- Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan.,Department of Chemistry and Biotechnology, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan
| | - Masahiro Kawahara
- Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan.,Department of Chemistry and Biotechnology, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan
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20
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Rabia LA, Desai AA, Jhajj HS, Tessier PM. Understanding and overcoming trade-offs between antibody affinity, specificity, stability and solubility. Biochem Eng J 2018; 137:365-374. [PMID: 30666176 DOI: 10.1016/j.bej.2018.06.003] [Citation(s) in RCA: 89] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The widespread use of monoclonal antibodies for therapeutic applications has led to intense interest in optimizing several of their natural properties (affinity, specificity, stability, solubility and effector functions) as well as introducing non-natural activities (bispecificity and cytotoxicity mediated by conjugated drugs). A common challenge during antibody optimization is that improvements in one property (e.g., affinity) can lead to deficits in other properties (e.g., stability). Here we review recent advances in understanding trade-offs between different antibody properties, including affinity, specificity, stability and solubility. We also review new approaches for co-optimizing multiple antibody properties and discuss how these methods can be used to rapidly and systematically generate antibodies for a wide range of applications.
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Affiliation(s)
- Lilia A Rabia
- Center for Biotechnology & Interdisciplinary Studies, Isermann Dept. of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180
| | - Alec A Desai
- Department of Chemical Engineering, Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Harkamal S Jhajj
- Department of Biomedical Engineering, Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Peter M Tessier
- Center for Biotechnology & Interdisciplinary Studies, Isermann Dept. of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180
- Department of Chemical Engineering, Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biomedical Engineering, Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Pharmaceutical Sciences, Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
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