Journal Article

Nanobody screening and machine learning guided identification of cross-variant anti-SARS-CoV-2 neutralizing heavy-chain only antibodies

Won, Stephen, Sumner, Christopher A., Harmon, Brooke, Sinha, Anupama, Bruneau, Ryan C., Whelan, Sean, Jones, Iris K. A., Sale, Kenneth L., Weilhammer, Dina R., Negrete, Oscar A., Liu, Zhuoming, Pham, Le Thanh Mai, McIlroy, Peter R., Jaryenneh, James, Stefan, Maxwell A., Thatcher, Christine E., Schwedler, Jennifer L., Sheffield, Thomas

2025

Overview

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) continues to persist, demonstrating the risks posed by emerging infectious diseases to national security, public health, and the economy. Development of new vaccines and antibodies for emerging viral threats requires substantial resources and time, and traditional development platforms for vaccines and antibodies are often too slow to combat continuously evolving immunological escape variants, reducing their efficacy over time. Previously, we designed a next-generation synthetic humanized nanobody (Nb) phage display library and demonstrated that this library could be used to rapidly identify highly specific and potent neutralizing heavy chain-only antibodies (HCAbs) with prophylactic and therapeutic efficacy in vivo against the original SARS-CoV-2. In this study, we used a combination of high throughput screening and machine learning (ML) models to identify HCAbs with potent efficacy against SARS-CoV-2 viral variants of interest (VOIs) and concern (VOCs). To start, we screened our highly diverse Nb phage display library against several pre-Omicron VOI and VOC receptor binding domains (RBDs) to identify panels of cross-reactive HCAbs. Using HCAb affinity for SARS-CoV-2 VOI and VOCs (pre-Omicron variants) and model features from other published data, we were able to develop a ML model that successfully identified HCAbs with efficacy against Omicron variants, independent of our experimental biopanning workflow. This biopanning informed ML approach reduced the experimental screening burden by 78% to 90% for the Omicron BA.5 and Omicron BA.1 variants, respectively. The combined approach can be applied to other emerging viruses with pandemic potential to rapidly identify effective therapeutic antibodies against emerging variants.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Amino acids / Animals / Antibodies / Antibodies, Neutralizing - immunology / Antibodies, Viral - immunology / Antigens / Biology and life sciences / Coronaviruses / COVID-19 / COVID-19 - immunology / COVID-19 - virology / Effectiveness / Health aspects / High-throughput screening / High-Throughput Screening Assays / Humans / Identification and classification / Immunoglobulin Heavy Chains - immunology / Immunology / In vivo methods and tests / Infectious diseases / Laboratories / Learning algorithms / Libraries / Machine Learning / Medical research / Medical screening / Medicine and health sciences / Medicine, Experimental / Mutation / Nanobodies / Neutralizing / Pandemics / Peptide Library / Phage display / Phages / Proteins / Public health / R&D / Research & development / Research and Analysis Methods / SARS-CoV-2 - immunology / Severe acute respiratory syndrome coronavirus 2 / Single-Domain Antibodies - immunology / Spike Glycoprotein, Coronavirus - immunology / Testing / Vaccines / Viral antibodies / Viral diseases