RT Journal Article
SR Electronic
T1 Using machine learning to detect coronaviruses potentially infectious to humans
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.12.11.520008
DO 10.1101/2022.12.11.520008
A1 Georgina Gonzalez-Isunza
A1 M. Zaki Jawaid
A1 Pengyu Liu
A1 Daniel L. Cox
A1 Mariel Vazquez
A1 Javier Arsuaga
YR 2022
UL http://biorxiv.org/content/early/2022/12/12/2022.12.11.520008.abstract
AB Establishing the host range for novel viruses remains a challenge. Here, we address the challenge of identifying non-human animal coronaviruses that may infect humans by creating an artificial neural network model that learns from the binding of the spike protein of alpha and beta coronaviruses to their host receptor. The proposed method produces a human-Binding Potential (h-BiP) score that distinguishes, with high accuracy, the binding potential among human coronaviruses. Two viruses, previously unknown to bind human receptors, were identified: Bat coronavirus BtCoV/133/2005 (a MERS related virus) and Rhinolophus affinis coronavirus isolate LYRa3 a SARS related virus. We further analyze the binding properties of these viruses using molecular dynamics. To test whether this model can be used for surveillance of novel coronaviruses, we re-trained the model on a set that excludes SARS-COV-2 viral sequences. The results predict the binding of SARS-CoV-2 with a human receptor, indicating that machine learning methods are an excellent tool for the prediction of host expansion events.Competing Interest StatementThe authors have declared no competing interest.