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Post-Doctoral Research Topic: In silico reconstruction of the COVID-19 replication machinery: Bases for Antiviral Identification

ABG-91665 Emploi Niveau d'expérience indifférent
23/04/2020 CDD 12 Mois Salaire à négocier
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INSA de Rouen
ROUEN - Normandie - France
Informatique
Knowledge representation and modelling, Spatio/Temporal reasoning, Ontologies, Text-mining; Biomedical Applications, Open data
Recherche et Développement

Employeur

INSA de Rouen

MIND - LITIS (EA 4108 / FR CNRS 3638)

Poste et missions

Antiviral strategies targeting replication machines have proven their worth, with for example the success stories of the cure of Hepatitis C Virus-infected patients or Human Immunodeficiency Virus treatments. One of the prerequisite is a detailed knowledge of the structure and function of these multi-protein complexes allowing the RNA genome replication. The coronavirus (CoV) genome is a positive and single-stranded RNA, the largest among RNA viruses (~30-kb) and paradoxically, with a genetic stability superior to the other RNA viruses. It is now established that it is the 3'-5' exonuclease activity encoded by the CoVs that enables correcting errors during the genome replication.
This proofreading activity partly explains the absence of effect of ribavirin on patients infected with SARS-CoV or MERS-CoV. More generally, future anti-CoV strategies will need to incorporate this unique property for RNA viruses (+). The PullCoVapart project, funded by ANR, the French National Research Agency, is an interdisciplinary project that combines methods in artificial intelligence with protein biochemistry in order to formally model the RNA polymerase behavior of this new coronavirus to be able to predict it, by simulation.

The three main objectives of the PullCoVapart project are to (1) reconstitute, in vitro, the replication complex of the COVID-19; (2) model its replication activity in silico and finally (3) compare the in silico results with the in vitro experimentations to validate them (or not). In particular, this post-doc project will need to address the 2nd main objective, by proposing a unified formal model of the available knowledge in published resources about COVID-19, and to enrich them by eliciting knowledge from state-of-the-art scientific literature using text mining techniques founded on the new corpus (LitCovid) from the National Library of Medicine  and the newly published "Coronavirus Infectious Disease Ontology".
Moreover, it will also be necessary to identify the dynamic properties of the COVID-19 RNA polymerase, which is more complex than just representing simple biomedical concepts. Thus, temporal logics for reasoning will also need to be taken into account. Verification and validation of the obtained models will also need to be performed.

 

Mobilité géographique :

Pas de déplacement

Profil

  • PhD in computer science with relevant skills in semantic technologies (including the development of ontologies and reasoning models).
  • Excellent communications skills, able to discuss with scientists with different backgrounds (mainly the researchers of other two partners of the project, biologists and computer scientists expert in simulation).
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