SARS2020: an integrated platform for identification of novel coronavirus by a consensus sequence-function model

Area of Science:

• Virology
• Bioinformatics
• Drug Discovery

Background:

• The 2019 novel coronavirus outbreak poses a significant global health challenge.
• Predicting pathogen biological function from genomic sequences is crucial for effective response.
• Identifying viruses based on their encoded enzymes, essential for propagation, remains underexplored.

Purpose of the Study:

• To develop a comprehensive resource for coronavirus research.
• To establish a sequence-based method for virus identification.
• To aid in the rapid identification of future epidemic agents.

Main Methods:

• Integrated coronavirus research, genomic sequences, and antiviral drug trial results into the SARS2020 resource.
• Developed a consensus sequence-catalytic function model.
• Utilized a data-driven, sequence-based strategy for virus identification.

Main Results:

• The SARS2020 resource provides a centralized platform for coronavirus data.
• The sequence-catalytic function model identified the novel coronavirus as encoding the same proteinase as SARS-CoV.
• The study demonstrates a novel strategy for rapid pathogen identification.

Conclusions:

• The SARS2020 resource and the developed model offer a valuable tool for virological research.
• Sequence-based identification of viral enzymes is a promising strategy for future epidemic preparedness.
• This approach facilitates rapid identification and characterization of novel viral threats.