RAPID: Motif-based Classification and Systems Analysis of CoV-2 Virulence Mechanism
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: unknown
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Key facts
Disease
COVID-19Start & end year
20202021Known Financial Commitments (USD)
$198,043Funder
National Science Foundation (NSF)Principal Investigator
Paul O'MailleResearch Location
United States of AmericaLead Research Institution
SRI InternationalResearch Priority Alignment
N/A
Research Category
Pathogen: natural history, transmission and diagnostics
Research Subcategory
Pathogen morphology, shedding & natural history
Special Interest Tags
N/A
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Not Applicable
Vulnerable Population
Not applicable
Occupations of Interest
Not applicable
Abstract
The COVID-19 pandemic demands intensive fundamental research to decipher how the SARS-CoV-2 virus has adapted to infect humans and spread globally. The research supported through this RAPID award uncovers new insights into how SARS-CoV-2 and other viruses mimic human biology to facilitate infection and adaptation strategies. Previous research has revealed that host mimicry is essential for viral infection mechanisms yet currently there is a deficiency of tools to comprehensively identify and classify ?signatures? of host mimicry in viral sequence data. Identifying and classifying these sequence signatures (motifs) enable researchers to more accurately predict host targets based on viral adaptation strategies that underly the transmission and spread of COVID-19. This project delivers new data on human mimicry by the SARS-CoV-2 virus and open-source algorithms with supporting information to the scientific community through web-based resources. Discoveries from this project have potential use to guide the development of novel treatment strategies for SARS-CoV-2 and other viruses. This project also offers a training opportunity for an undergraduate student in systems virology.
A growing body of literature documents how viruses mimic host motifs to hijack cells, particularly through evasion of host immune functions. Motifs are small in size (3-10 amino acids in disordered proteins or regions) and hence require only a limited number of mutations for rapidly evolving viruses to acquire host motifs. Against this backdrop, this project is designed to develop a viral classification system based on motifs to identify host mimicry elements in SARS-CoV-2 and other viruses. The project then infers target host pathways used in viral mechanisms that are illustrated in a use case. Curation of existing data from viral sequence databases, newly published sequences, along with the collection of motif types from the Eukaryotic Linear Motif (ELM) resource, provides training data for machine-learning (ML) algorithm development. The algorithm in turn provides a classifier that i) makes predictions of host motifs in viruses, ii) defines enrichment and co-occurrence of motif types, iii) defines association of motifs with structured and disordered viral protein domains, and iv) predicts host targets and pathways. In the use case, evaluation of precision and recall against selected viruses with known mechanisms of action, supported by a knowledge base of experimental evidence, provides a test of the classification method.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
A growing body of literature documents how viruses mimic host motifs to hijack cells, particularly through evasion of host immune functions. Motifs are small in size (3-10 amino acids in disordered proteins or regions) and hence require only a limited number of mutations for rapidly evolving viruses to acquire host motifs. Against this backdrop, this project is designed to develop a viral classification system based on motifs to identify host mimicry elements in SARS-CoV-2 and other viruses. The project then infers target host pathways used in viral mechanisms that are illustrated in a use case. Curation of existing data from viral sequence databases, newly published sequences, along with the collection of motif types from the Eukaryotic Linear Motif (ELM) resource, provides training data for machine-learning (ML) algorithm development. The algorithm in turn provides a classifier that i) makes predictions of host motifs in viruses, ii) defines enrichment and co-occurrence of motif types, iii) defines association of motifs with structured and disordered viral protein domains, and iv) predicts host targets and pathways. In the use case, evaluation of precision and recall against selected viruses with known mechanisms of action, supported by a knowledge base of experimental evidence, provides a test of the classification method.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.