| Autor: |
Rodrigues, João P. G. L. M., Barrera-Vilarmau, Susana, M. C. Teixeira, João, Sorokina, Marija, Seckel, Elizabeth, Kastritis, Panagiotis L., Levitt, Michael |
| Předmět: |
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| Zdroj: |
PLoS Computational Biology; 12/3/2020, Vol. 16 Issue 12, p1-19, 19p, 5 Diagrams, 1 Chart, 1 Graph |
| Abstrakt: |
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the ongoing global pandemic that has infected more than 31 million people in more than 180 countries worldwide. Like other coronaviruses, SARS-CoV-2 is thought to have been transmitted to humans from wild animals. Given the scale and widespread geographical distribution of the current pandemic and confirmed cases of cross-species transmission, the question of the extent to which this transmission is possible emerges, as well as what molecular features distinguish susceptible from non-susceptible animal species. Here, we investigated the structural properties of several ACE2 orthologs bound to the SARS-CoV-2 spike protein. We found that species known not to be susceptible to SARS-CoV-2 infection have non-conservative mutations in several ACE2 amino acid residues that disrupt key polar and charged contacts with the viral spike protein. Our models also allow us to predict affinity-enhancing mutations that could be used to design ACE2 variants for therapeutic purposes. Finally, our study provides a blueprint for modeling viral-host protein interactions and highlights several important considerations when designing these computational studies and analyzing their results. Author summary: SARS-CoV-2 infects multiple animal species, including humans. Like many other viruses, the first step in its infection cycle is the interaction between a viral protein and a receptor protein on the host cell membrane. Characterizing the three-dimensional structure of such protein interactions, at the atomic level, is very important to understand the infection process, to help develop therapeutics against it, and to predict which other animal species are at risk. Experimentally, this characterization is usually difficult, expensive, and not applicable on a large scale. Here, we show that computational modeling can fill in some of the gaps, namely provide a structural framework to explain why humans are susceptible to SARS-CoV-2 infection, while mice and chicken are not. Our models also map, with reasonable accuracy, key amino acids of the host receptor, which can help guide the development of antiviral therapeutics. Our work serves as a blueprint for studying viral-host protein interactions using computational modeling, providing a quick and inexpensive complement to experiments, and benefits both our basic understanding of viral infections and drug development. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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