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Scientific project
Interactions between proteins and nucleic acids have strong biological relevance and are often perturbed in diseases. This project aims to better understand the molecular mechanisms of protein-nucleic acid interactions. A large gap exists between vast amounts of high-throughput protein-nucleic acid interaction data and the scarcity of 3D structures of protein-nucleic acid complexes, triggering the need for computational methods to analyze and predict these structures. Recently, artificial intelligence (in particular deep learning in AlphaFold and related methods) has emerged as a revolutionary approach to predict protein and protein-protein interaction structures.
The present project aims to develop computational approaches for the analysis and prediction of protein-nucleic acid interactions, by building upon deep learning methodologies and integrating complementary data sources. They will also use an evolutionary perspective to provide crucial insights into the exquisite regulation of complex processes driven by protein-nucleic acid interactions.
Host team and environment for the PhD project
The doctoral research will take place in the “Molecular assemblies and genome integrity” team of I2BC (Institute for Integrative Biology of the Cell, UMR 9198 CEA/CNRS/Université Paris-Saclay). I2BC is a research institute of Université Paris-Saclay that gathers 60 research teams covering a wide range of integrative biology projects. The “Molecular assemblies and genome integrity” team relies on a strong coupling between computational and experimental approaches to characterize, predict and inhibit macromolecular interactions. I2BC is localized in Gif sur Yvette (less than one hour from the center of Paris).
The bioinformatics team has a strong expertise in macromolecular structure and evolution, macromolecular interaction prediction and heterogeneous data integration. In recent years, our team developed original approaches for the structural prediction of protein interactions using evolutionary information (Quignot et al, NAR 2021; Quignot et al, Bioinformatics 2021). They also have numerous collaborations with wet-lab biologists. They have access to relevant computational resources, including GPU cluster nodes.
Funding and benefits
CEA will provide 3 years PhD funding for this project to an excellent PhD candidate. The gross monthly salary for CEA doctoral students is €2,290 (as of October 2022). CEA doctoral students have access to the same benefits as other CEA employees (social security contributions, training opportunities, company canteen, paid leave, social benefits…). Expected start of the PhD position: October 2023.
Expected profile
They are looking for a highly motivated candidate with a Master's degree in bioinformatics, data science, machine learning or a related field. Proficiency in Python programming is necessary. Previous experience in deep learning and data science is highly desirable. (Structural) bioinformatics experience is a plus but not strictly required, provided the candidate has a strong interest in life sciences and molecular aspects.
Application process
Please send a CV, a motivation letter and contact details of two references (ideally, researchers who have supervised the candidate for relevant research projects) to Jessica Andreani: jessica.andreani@cea.fr
Deadline for application: 30 April 2023
