tetano
Editor, Senior Moderator
Biochem Biophys Res Commun
. 2020 Sep 18;S0006-291X(20)31743-5.
doi: 10.1016/j.bbrc.2020.09.010. Online ahead of print.
Machine learning methods accurately predict host specificity of coronaviruses based on spike sequences alone
Kiril Kuzmin[SUP] 1 [/SUP], Ayotomiwa Ezekiel Adeniyi[SUP] 2 [/SUP], Arthur Kevin DaSouza Jr[SUP] 3 [/SUP], Deuk Lim[SUP] 3 [/SUP], Huyen Nguyen[SUP] 3 [/SUP], Nuria Ramirez Molina[SUP] 3 [/SUP], Lanqiao Xiong[SUP] 3 [/SUP], Irene T Weber[SUP] 3 [/SUP], Robert W Harrison[SUP] 4 [/SUP]
Affiliations
Abstract
Coronaviruses infect many animals, including humans, due to interspecies transmission. Three of the known human coronaviruses: MERS, SARS-CoV-1, and SARS-CoV-2, the pathogen for the COVID-19 pandemic, cause severe disease. Improved methods to predict host specificity of coronaviruses will be valuable for identifying and controlling future outbreaks. The coronavirus S protein plays a key role in host specificity by attaching the virus to receptors on the cell membrane. We analyzed 1238 spike sequences for their host specificity. Spike sequences readily segregate in t-SNE embeddings into clusters of similar hosts and/or virus species. Machine learning with SVM, Logistic Regression, Decision Tree, Random Forest gave high average accuracies, F[SUB]1[/SUB] scores, sensitivities and specificities of 0.95-0.99. Importantly, sites identified by Decision Tree correspond to protein regions with known biological importance. These results demonstrate that spike sequences alone can be used to predict host specificity.
Keywords: Machine learning; Sequence clustering; Spike protein; Viral host specificity; coronaviruses; t-SNE.
. 2020 Sep 18;S0006-291X(20)31743-5.
doi: 10.1016/j.bbrc.2020.09.010. Online ahead of print.
Machine learning methods accurately predict host specificity of coronaviruses based on spike sequences alone
Kiril Kuzmin[SUP] 1 [/SUP], Ayotomiwa Ezekiel Adeniyi[SUP] 2 [/SUP], Arthur Kevin DaSouza Jr[SUP] 3 [/SUP], Deuk Lim[SUP] 3 [/SUP], Huyen Nguyen[SUP] 3 [/SUP], Nuria Ramirez Molina[SUP] 3 [/SUP], Lanqiao Xiong[SUP] 3 [/SUP], Irene T Weber[SUP] 3 [/SUP], Robert W Harrison[SUP] 4 [/SUP]
Affiliations
- PMID: 32981683
- DOI: 10.1016/j.bbrc.2020.09.010
Abstract
Coronaviruses infect many animals, including humans, due to interspecies transmission. Three of the known human coronaviruses: MERS, SARS-CoV-1, and SARS-CoV-2, the pathogen for the COVID-19 pandemic, cause severe disease. Improved methods to predict host specificity of coronaviruses will be valuable for identifying and controlling future outbreaks. The coronavirus S protein plays a key role in host specificity by attaching the virus to receptors on the cell membrane. We analyzed 1238 spike sequences for their host specificity. Spike sequences readily segregate in t-SNE embeddings into clusters of similar hosts and/or virus species. Machine learning with SVM, Logistic Regression, Decision Tree, Random Forest gave high average accuracies, F[SUB]1[/SUB] scores, sensitivities and specificities of 0.95-0.99. Importantly, sites identified by Decision Tree correspond to protein regions with known biological importance. These results demonstrate that spike sequences alone can be used to predict host specificity.
Keywords: Machine learning; Sequence clustering; Spike protein; Viral host specificity; coronaviruses; t-SNE.