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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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Using machine learning to detect coronaviruses potentially infectious to humans.

Georgina Gonzalez-Isunza1, M Zaki Jawaid2, Pengyu Liu1

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Summary
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Scientists developed an artificial neural network to predict if animal coronaviruses can infect humans. This model identifies potential zoonotic threats by analyzing spike protein sequences, aiding in early detection and surveillance of novel viruses.

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Area of Science:

  • Virology
  • Computational Biology
  • Epidemiology

Background:

  • Identifying the host range of novel viruses is crucial for pandemic preparedness.
  • Coronaviruses, particularly alpha and beta types, pose a significant threat due to their potential for cross-species transmission.

Purpose of the Study:

  • To develop a predictive model for identifying animal coronaviruses with the potential to infect humans.
  • To assess the utility of machine learning in predicting viral host expansion events.

Main Methods:

  • An artificial neural network was trained on spike protein sequences and host receptor binding data of alpha and beta coronaviruses.
  • A human-Binding Potential (h-BiP) score was developed to quantify binding affinity to human receptors.
  • Molecular dynamics simulations were used to analyze the binding properties of specific identified viruses.

Main Results:

  • The model accurately distinguished binding potential among coronaviruses, yielding a human-Binding Potential (h-BiP) score.
  • Three previously unknown human-binding coronaviruses were identified: Bat coronavirus BtCoV/133/2005, Pipistrellus abramus bat coronavirus HKU5-related, and Rhinolophus affinis coronavirus isolate LYRa3.
  • The model successfully predicted SARS-CoV-2's binding to human receptors even when trained on pre-SARS-CoV-2 data.

Conclusions:

  • Machine learning, specifically artificial neural networks, is a powerful tool for predicting viral host expansion and identifying potential zoonotic threats.
  • The developed h-BiP score and model can aid in the surveillance of novel coronaviruses for pandemic risk assessment.