Discovery of Antibodies Against Endemic Coronaviruses with NGS-Based Human Fab Phage Display Platform
<b>Background:</b> There is an unmet medical need to develop a vaccine targeting endemic coronaviruses. Antigen-specific monoclonal antibodies (mAbs) are crucial for many assays to support vaccine development. <b>Objective:</b> In this study, we used the HuCal Fab phage display library with a diversity of 4.5 × 10<sup>10</sup> to identify antibodies specific to the spike proteins of the four endemic coronaviruses: OC43, NL63, 229E, and HKU1. <b>Methods:</b> As proof of concept, we established a newly designed platform using a long-read NGS workflow for antibody discovery and compared the results against the traditional workflow using Sanger sequencing consisting of lengthy and laborious benchwork. <b>Results:</b> The long-read NGS workflow identified most of the antibodies seen from the Sanger sequencing workflow, and many more additional antigen-specific antibodies against the endemic coronaviruses. Overall efficiency improved up to three times, comparing the traditional workflow with the NGS workflow. Of the 113 NGS-derived mAbs isolated to bind the four endemic coronavirus spike proteins, 107/113 (94.7%) had potent ELISA binding affinities (EC50 < 150 ng/mL, or <1 nM), and 61/113 (54%) had extremely potent ELISA binding affinities (EC50 of <15 ng/mL, or <0.1 nM). <b>Conclusions:</b> We successfully developed and incorporated the long-read NGS workflow to generate target-specific antibodies with many antibodies at sub-nanomolar affinities that are likely missed by a traditional workflow. We identified strong neutralizing antibodies, proving that our endemic spike proteins are capable of generating antibodies that could offer protection against the endemic HCoVs.
Authors
- Eberhard Dürr (ORCID: https://orcid.org/0000-0003-0601-1117)
- Arthur Fridman
- Oscar Pan
- Rakesh P. Patel (ORCID: https://orcid.org/0000-0002-1526-4303)
- Emmanuel Ndashimye (ORCID: https://orcid.org/0000-0002-9112-9402)
- Kalpit A. Vora (ORCID: https://orcid.org/0000-0001-9844-634X)
- Lan Zhang (ORCID: https://orcid.org/0000-0002-8893-3029)
- J. W. T. Hessels (ORCID: https://orcid.org/0000-0003-2317-1446)
- Sean Miller
- Christopher Warren (ORCID: https://orcid.org/0000-0002-8924-3329)
- Jennifer Galli (ORCID: https://orcid.org/0000-0001-8577-4790)
- Shreya Mukhopadhyay (ORCID: https://orcid.org/0000-0002-2419-1409)
- Zhifeng Chen (ORCID: https://orcid.org/0000-0002-9390-6527)
- Zhiyun Wen
- Gwenny Go
- Giancarlo Sarullo
- Barbara Schlingmann-Molina
Institutions
- Merck & Co., Inc., Rahway, NJ, USA (United States) (US)
- Quantitative BioSciences (US)
Publication Details
- Journal
- Antibodies
- Published
- 2025-03-27
- DOI
- https://doi.org/10.3390/antib14020028
- Citations
- 1
- Primary Topic
- Monoclonal and Polyclonal Antibodies Research
- Type
- article
- Field-Weighted Citation Impact
- 1.36