In vitro and in silico reconstruction of the COVID-19 replication machinery: Bases for Antiviral Identification
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 project is an interdisciplinary project that combines methods in artificial intelligence with protein biochemistry in order to model and predict (by simulation) the RNA polymerase behavior of this new coronavirus.
Discrete event simulation model; verification and validation of the conceptual models
The objectives of this project are to (1) reconstitute, in vitro, the replication complex of the COVID-19; (2) model its replication activity in silico and finally (3) the in silico results will be confronted with the in vitro experimentations to validate them (or not). In particular, the post-Doc project will be to address the 2 last objectives, by proposing an adaptation/extension of the DEVS formalism in order to describe COVID-19 models.
Background and skills required:
• PhD in computer science with relevant skills in semantic technologies (including the development of ontologies and reasoning models) and /or modeling & simulation.
• Excellent communications skills, able to discuss with scientists with different backgrounds.
Continuation of an existing project
This project is in line with an initiative funding obtained recently from REACTing/INSERM:
« Reconstruction in vitro et in silico de la machinerie réplicative du COVID-19: Bases pour l’identification d’antiviraux ». This funding is dedicated to the biological part.
Articles related to the project
• Subissi L, Posthuma CC, Collet A, Zevenhoven-Dobbe JC, Gorbalenya AE, Decroly E, Snijder EJ, Canard B, Imbert I. 2014. One severe acute respiratory syndrome coronavirus protein complex integrates processive RNA polymerase and exonuclease activities. Proc Natl Acad Sci U S A 111:E3900-3909.
• Ferron F, Subissi L, Silveira De Morais AT, Le NTT, Sevajol M, Gluais L, Decroly E, Vonrhein C, Bricogne G, Canard B, Imbert I. 2018. Structural and molecular basis of mismatch correction and ribavirin excision from coronavirus RNA. Proc Natl Acad Sci U S A 115:E162–E171.
• Diego Hollmann, Maximiliano Cristia, Claudia Frydman. CML-DEVS: A specification language for DEVS conceptual models. Simulation Modelling Practice and Theory, Elsevier, 2015, 57, pp.100-117
• Maximiliano Cristiá, Diego Hollmann, Claudia Frydman. A multi-target compiler for CML-DEVS SIMULATION, SAGE Publications, 2019, ⟨10.1177/0037549718765080⟩