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Thalén NB, Karlander M, Lundqvist M, Persson H, Hofström C, Turunen SP, Godzwon M, Volk AL, Malm M, Ohlin M, Rockberg J. Mammalian cell display with automated oligo design and library assembly allows for rapid residue level conformational epitope mapping. Commun Biol 2024; 7:805. [PMID: 38961245 PMCID: PMC11222437 DOI: 10.1038/s42003-024-06508-8] [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] [Received: 11/29/2023] [Accepted: 06/25/2024] [Indexed: 07/05/2024] Open
Abstract
Precise epitope determination of therapeutic antibodies is of great value as it allows for further comprehension of mechanism of action, therapeutic responsiveness prediction, avoidance of unwanted cross reactivity, and vaccine design. The golden standard for discontinuous epitope determination is the laborious X-ray crystallography method. Here, we present a combinatorial method for rapid mapping of discontinuous epitopes by mammalian antigen display, eliminating the need for protein expression and purification. The method is facilitated by automated workflows and tailored software for antigen analysis and oligonucleotide design. These oligos are used in automated mutagenesis to generate an antigen receptor library displayed on mammalian cells for direct binding analysis by flow cytometry. Through automated analysis of 33930 primers an optimized single condition cloning reaction was defined allowing for mutation of all surface-exposed residues of the receptor binding domain of SARS-CoV-2. All variants were functionally expressed, and two reference binders validated the method. Furthermore, epitopes of three novel therapeutic antibodies were successfully determined followed by evaluation of binding also towards SARS-CoV-2 Omicron BA.2. We find the method to be highly relevant for rapid construction of antigen libraries and determination of antibody epitopes, especially for the development of therapeutic interventions against novel pathogens.
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Affiliation(s)
- Niklas Berndt Thalén
- Department Protein science, KTH-Royal Institute of Technology, Stockholm, SE-106 91, Sweden
| | - Maximilian Karlander
- Department Protein science, KTH-Royal Institute of Technology, Stockholm, SE-106 91, Sweden
| | - Magnus Lundqvist
- Department Protein science, KTH-Royal Institute of Technology, Stockholm, SE-106 91, Sweden
| | - Helena Persson
- Science for Life Laboratory, Drug Discovery and Development Platform & School of Biotechnology, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Camilla Hofström
- Science for Life Laboratory, Drug Discovery and Development Platform & School of Biotechnology, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - S Pauliina Turunen
- Science for Life Laboratory, Drug Discovery and Development Platform & School of Biotechnology, KTH-Royal Institute of Technology, Stockholm, Sweden
| | | | - Anna-Luisa Volk
- Department Protein science, KTH-Royal Institute of Technology, Stockholm, SE-106 91, Sweden
| | - Magdalena Malm
- Department Protein science, KTH-Royal Institute of Technology, Stockholm, SE-106 91, Sweden
| | - Mats Ohlin
- Department of Immunotechnology, Lund University, Lund, Sweden
| | - Johan Rockberg
- Department Protein science, KTH-Royal Institute of Technology, Stockholm, SE-106 91, Sweden.
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Gomes DEB, Yang B, Vanella R, Nash MA, Bernardi RC. Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.08.579577. [PMID: 38370725 PMCID: PMC10871313 DOI: 10.1101/2024.02.08.579577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure is a long standing goal with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost intensive. Computational methods have potential to accelerate epitope predictions, however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologs. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2 Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis alone each proved ineffectual in differentiating between two putative binding models referred to as parallel and perpendicular. However, our integrated approach based on dynamic network analysis showed that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols including cross linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Our research highlights the potential of deploying dynamic network analysis to refine AI-based structure predictions for precise predictions of protein-protein interaction interfaces.
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Gallo E. Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances. Mol Biotechnol 2024:10.1007/s12033-024-01064-2. [PMID: 38308755 DOI: 10.1007/s12033-024-01064-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 01/02/2024] [Indexed: 02/05/2024]
Abstract
Synthetic antibodies (Abs) represent a category of engineered proteins meticulously crafted to replicate the functions of their natural counterparts. Such Abs are generated in vitro, enabling advanced molecular alterations associated with antigen recognition, paratope site engineering, and biochemical refinements. In a parallel realm, deep sequencing has brought about a paradigm shift in molecular biology. It facilitates the prompt and cost-effective high-throughput sequencing of DNA and RNA molecules, enabling the comprehensive big data analysis of Ab transcriptomes, including specific regions of interest. Significantly, the integration of artificial intelligence (AI), based on machine- and deep- learning approaches, has fundamentally transformed our capacity to discern patterns hidden within deep sequencing big data, including distinctive Ab features and protein folding free energy landscapes. Ultimately, current AI advances can generate approximations of the most stable Ab structural configurations, enabling the prediction of de novo synthetic Abs. As a result, this manuscript comprehensively examines the latest and relevant literature concerning the intersection of deep sequencing big data and AI methodologies for the design and development of synthetic Abs. Together, these advancements have accelerated the exploration of antibody repertoires, contributing to the refinement of synthetic Ab engineering and optimizations, and facilitating advancements in the lead identification process.
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Affiliation(s)
- Eugenio Gallo
- Avance Biologicals, Department of Medicinal Chemistry, 950 Dupont Street, Toronto, ON, M6H 1Z2, Canada.
- RevivAb, Department of Protein Engineering, Av. Ipiranga, 6681, Partenon, Porto Alegre, RS, 90619-900, Brazil.
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Levin I, Štrajbl M, Fastman Y, Baran D, Twito S, Mioduser J, Keren A, Fischman S, Zhenin M, Nimrod G, Levitin N, Mayor MB, Gadrich M, Ofran Y. Accurate profiling of full-length Fv in highly homologous antibody libraries using UMI tagged short reads. Nucleic Acids Res 2023; 51:e61. [PMID: 37014016 PMCID: PMC10287906 DOI: 10.1093/nar/gkad235] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/14/2023] [Accepted: 03/29/2023] [Indexed: 04/05/2023] Open
Abstract
Deep parallel sequencing (NGS) is a viable tool for monitoring scFv and Fab library dynamics in many antibody engineering high-throughput screening efforts. Although very useful, the commonly used Illumina NGS platform cannot handle the entire sequence of scFv or Fab in a single read, usually focusing on specific CDRs or resorting to sequencing VH and VL variable domains separately, thus limiting its utility in comprehensive monitoring of selection dynamics. Here we present a simple and robust method for deep sequencing repertoires of full length scFv, Fab and Fv antibody sequences. This process utilizes standard molecular procedures and unique molecular identifiers (UMI) to pair separately sequenced VH and VL. We show that UMI assisted VH-VL matching allows for a comprehensive and highly accurate mapping of full length Fv clonal dynamics in large highly homologous antibody libraries, as well as identification of rare variants. In addition to its utility in synthetic antibody discovery processes, our method can be instrumental in generating large datasets for machine learning (ML) applications, which in the field of antibody engineering has been hampered by conspicuous paucity of large scale full length Fv data.
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Affiliation(s)
| | | | | | | | | | | | - Adi Keren
- Biolojic Design, Ltd, Rehovot, Israel
| | | | | | | | | | | | | | - Yanay Ofran
- Biolojic Design, Ltd, Rehovot, Israel
- The Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan, Israel
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5
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Understanding and Modulating Antibody Fine Specificity: Lessons from Combinatorial Biology. Antibodies (Basel) 2022; 11:antib11030048. [PMID: 35892708 PMCID: PMC9326607 DOI: 10.3390/antib11030048] [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: 06/14/2022] [Revised: 07/08/2022] [Accepted: 07/11/2022] [Indexed: 02/01/2023] Open
Abstract
Combinatorial biology methods such as phage and yeast display, suitable for the generation and screening of huge numbers of protein fragments and mutated variants, have been useful when dissecting the molecular details of the interactions between antibodies and their target antigens (mainly those of protein nature). The relevance of these studies goes far beyond the mere description of binding interfaces, as the information obtained has implications for the understanding of the chemistry of antibody–antigen binding reactions and the biological effects of antibodies. Further modification of the interactions through combinatorial methods to manipulate the key properties of antibodies (affinity and fine specificity) can result in the emergence of novel research tools and optimized therapeutics.
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Kang BH, Lax BM, Wittrup KD. Yeast Surface Display for Protein Engineering: Library Generation, Screening, and Affinity Maturation. Methods Mol Biol 2022; 2491:29-62. [PMID: 35482183 DOI: 10.1007/978-1-0716-2285-8_2] [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] [Indexed: 06/14/2023]
Abstract
Yeast surface display is a powerful directed evolution method for developing and engineering protein molecules to attain desired properties. Here, updated protocols are presented for purposes of identification of lead binders and their affinity maturation. Large libraries are screened by magnetic bead selections followed by flow cytometric selections. Upon identification and characterization of single clones, their affinities are improved by an iterative process of mutagenesis and fluorescence-activated cell sorting.
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Affiliation(s)
- Byong H Kang
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Brianna M Lax
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - K Dane Wittrup
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
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