Mots-Clés

Protein design host-pathogen interaction surface interactome

Description

Context

Antimicrobial resistance (AMR) is one of the top ten global public health threats facing humanity and is predicted to cause 10 million deaths yearly by 2050. A high rate of resistance against commonly used antibiotics have been increasingly observed world-wide, showing an essential need for designing novel therapeutics to increase the effectiveness of fighting pathogens. Accordingly, the detailed understanding of the molecular interactions between pathogens and their hosts is crucial.

The main goal of this postdoc project is to study the surface interactome of an important human-specific pathogen in interaction with human/mouse plasma to elucidate the protein-protein interactions that help the pathogen to evade the immune responses. Such information will be used then to design protein binders to inhibit those interactions. To achieve this goal, the candidate will study the surface virulence factors and develop computational tools to predict their specific function to further prioritized best targets. The next step is to design specific binders in silico for selected targets. The candidate will be involved further in collaboration with international teams expert in protein design to validate these designs in vitro.

The candidate will be hosted in the CAPSID team, LORIA, Inria, Nancy Grand Est, and will be supervised by Hamed Khakzad (Inria Junior Professor) with expertise in integrative structural biology, host-pathogen interactions, protein design, and deep learning [1,2,3]. The team consists of several permanent researchers with expertise in macromolecular interactions and docking, structural biology, and deep learning, together with several PhD and master students.

References

1. S. Hauri and H. Khakzad et al., "Rapid determination of quaternary protein structures in complex biological samples," Nature Communications, vol. 10, no. 192, 2019.
2. JK. Leman et. al., “Macromolecular modeling and design in Rosetta: recent methods and frameworks,” Nature Methods, vol. 17, no. 7, 2020
3. C. Goverde et. al., “De novo protein design by inversion of the AlphaFold structure prediction network,” Protein science, 2023.

Main activities

1. Implementing large-scale methods and preparing a software using Python
2. Validating the method and analyzing the results
3. Computational design of de novo proteins
4. Collaborating with microbiology and protein design teams.
5. Writing scientific articles and presenting the work in international conferences

Skills

• PhD degree in Computer Science, Bioinformatics, Chemoinformatics or a related program
• Proficiency in programming languages (Python) and good coding practices is a must
• Skills in protein design
• Experience in machine learning and/or deep learning (scikit, PyTorch) is a plus
• Ability to work independently and also to work in a team
• Excellent oral and written English skills

Benefits package

• Fully funded position (1.5 years) (renewable up to 6 more months)
• Subsidized meals
• Partial reimbursement of public transport costs
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Social security coverage

Duration

Duration: 18 months

Application

Send a CV and a motivation letter to Hamed Khakzad: hamed.khakzad@inria.fr