Communications in Information and Systems

Volume 22 (2022)

Number 3

Special issue on bioinformatics and biophysics in honor of professor Michael Waterman on his 80th birthday

Guest Editors: Fengzhu Sun (University of Southern California), Guowei Wei (Michigan State University), Stephen S.-T. Yau (Tsinghua University), and Shan Zhao (University of Alabama)

Mathematical artificial intelligence design of mutation-proof COVID-19 monoclonal antibodies

Pages: 339 – 361



Jiahui Chen (Department of Mathematics, Michigan State University, East Lansing, Mich., U.S.A.)

Guo-Wei Wei (Department of Mathematics, Michigan State University, East Lansing, Mich., U.S.A.)


Emerging severe acute respiratory syndrome coronavirus 2 (SARSCoV- 2) variants have compromised existing vaccines and posed a grand challenge to coronavirus disease 2019 (COVID-19) prevention, control, and global economic recovery. For COVID-19 patients, one of the most effective COVID-19 medications is monoclonal antibody (mAb) therapies. The United States Food and Drug Administration (U.S. FDA) has given the emergency use authorization (EUA) to a few mAbs, including those from Regeneron, Eli Elly, etc. However, they are also undermined by SARS-CoV- 2 mutations. It is imperative to develop effective mutation-proof mAbs for treating COVID-19 patients infected by all emerging variants and/or the original SARS-CoV-2. We carry out a deep mutational scanning to present the blueprint of such mAbs using algebraic topology and artificial intelligence (AI). To reduce the risk of clinical trial-related failure, we select five mAbs either with FDA EUA or in clinical trials as our starting point. We demonstrate that topological AI-designed mAbs are effective for variants of concerns and variants of interest designated by the World Health Organization (WHO), as well as the original SARS-CoV- 2. Our topological AI methodologies have been validated by tens of thousands of deep mutational data and their predictions have been confirmed by results from tens of experimental laboratories and population-level statistics of genome isolates from hundreds of thousands of patients.

The authors’ research was supported in part by NIH grants GM126189 and AI164266, NSF grants DMS-2052983, DMS-1761320, and IIS-1900473, NASA grant 80NSSC21M0023, Michigan Economic Development Corporation, MSU Foundation, Bristol-Myers Squibb 65109, and Pfizer.

Received 4 March 2022

Published 22 July 2022