Comprehensive fitness landscape of SARS-CoV-2 Mpro reveals insights into viral resistance mechanisms
- University of Massachusetts Medical School, United States
- Novartis, United States
Abstract
With the continual evolution of new strains of SARS-CoV-2 that are more virulent, transmissible, and able to evade current vaccines, there is an urgent need for effective anti-viral drugs SARS-CoV-2 main protease (Mpro) is a leading target for drug design due to its conserved and indispensable role in the viral life cycle. Drugs targeting Mpro appear promising but will elicit selection pressure for resistance. To understand resistance potential in Mpro, we performed a comprehensive mutational scan of the protease that analyzed the function of all possible single amino acid changes. We developed three separate high-throughput assays of Mpro function in yeast, based on either the ability of Mpro variants to cleave at a defined cut-site or on the toxicity of their expression to yeast. We used deep sequencing to quantify the functional effects of each variant in each screen. The protein fitness landscapes from all three screens were strongly correlated, indicating that they captured the biophysical properties critical to Mpro function. The fitness landscapes revealed a non-active site location on the surface that is extremely sensitive to mutation making it a favorable location to target with inhibitors. In addition, we found a network of critical amino acids that physically bridge the two active sites of the Mpro dimer. The clinical variants of Mpro were predominantly functional in our screens, indicating that Mpro is under strong selection pressure in the human population. Our results provide predictions of mutations that will be readily accessible to Mpro evolution and that are likely to contribute to drug resistance. This complete mutational guide of Mpro can be used in the design of inhibitors with reduced potential of evolving viral resistance.
Data availability
Next generation sequencing data has been deposited to the NCBI short read archive (PRJNA842255). Tabulated raw counts of all variants in all replicates are included in Figure 2 - source data 1. Figure 2 - source data 1, Figure 4 - source data 1, Figure 4 - source data 2, and Figure 5 - source data 1 contain the data used to generate all the figures.
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Comprehensive fitness landscape of SARS-CoV-2 Mpro in S. cerevisiae - raw sequence readsNCBI Short Read Archive, PRJNA842255.
Article and author information
Author details
Funding
Novartis Institutes for BioMedical Research
- Julia M Flynn
- Neha Samant
- Gily Schneider-Nachum
- Nese Kurt Yilmaz
- Celia A Schiffer
- Daniel NA Bolon
DTB, SAM, and DD are employees of Novartis Institutes for Biomedical Research and were involved in study design, data interpretation, and preparation of this manuscript.
Copyright
© 2022, Flynn et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Comprehensive fitness landscape of SARS-CoV-2 Mpro reveals insights into viral resistance mechanisms
eLife 11:e77433.
https://doi.org/10.7554/eLife.77433
Further reading
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- Epidemiology and Global Health
- Medicine
- Microbiology and Infectious Disease
eLife has published the following articles on SARS-CoV-2 and COVID-19.
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As pathogens spread in a population of hosts, immunity is built up, and the pool of susceptible individuals are depleted. This generates selective pressure, to which many human RNA viruses, such as influenza virus or SARS-CoV-2, respond with rapid antigenic evolution and frequent emergence of immune evasive variants. However, the host’s immune systems adapt, and older immune responses wane, such that escape variants only enjoy a growth advantage for a limited time. If variant growth dynamics and reshaping of host-immunity operate on comparable time scales, viral adaptation is determined by eco-evolutionary interactions that are not captured by models of rapid evolution in a fixed environment. Here, we use a Susceptible/Infected model to describe the interaction between an evolving viral population in a dynamic but immunologically diverse host population. We show that depending on strain cross-immunity, heterogeneity of the host population, and durability of immune responses, escape variants initially grow exponentially, but lose their growth advantage before reaching high frequencies. Their subsequent dynamics follows an anomalous random walk determined by future escape variants and results in variant trajectories that are unpredictable. This model can explain the apparent contradiction between the clearly adaptive nature of antigenic evolution and the quasi-neutral dynamics of high-frequency variants observed for influenza viruses.
