Learning host-pathogen surface interactome to design novel therapeutics
CDD · Thèse · 36 mois Bac+5 / Master INRIA Grand Est Nancy · Villers-les-Nancy (France)
Date de prise de poste : 1 septembre 2023
Protein-protein interaction protein design deep learning
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 PhD 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 bacteria to evade the immune responses. Such information will be used then to design protein binders to inhibit such interactions. To achieve this goal, the candidate will develop a deep learning model to predict the binding sites and the potential binders within the search space. This position will provide international collaboration with microbiologists and protein design groups with the possibility of in-site internships for 1-3 months.
The PhD candidate will be hosted in the CAPSID team, LORIA, Inria, Nancy Grand Est. The candidate will be supervised by Hamed Khakzad (Inria Junior Professor) with expertise in integrative structural biology, host-pathogen interactions, protein design, and deep learning [1,2,3,4], and Marie-Dominique Devignes (CNRS, HDR), expert in data integration and knowledge discovery from biological databases . The team consists of 7 permanent researchers with expertise in macromolecular interactions and docking, structural biology, and deep learning, together with several PhD and master students.
Background: Streptococcus Pyogenes (Group A Streptococcus; GAS) is an important human-specific antibiotic-resistant pathogen causing both mucosal and systemic infections. It produces several secreted and surface-attached virulence factors to target host proteins, localizing and initiating infections. GAS has evolved multiple immune camouflage strategies including scavenging host proteins by its virulence factors to build a dense layer of protein-protein interactions (PPI) on its surface . The surface interactome of GAS is largely unknown reflected in the large number of uncharacterized proteins in its proteome. This lack of knowledge is one of the major limitations in therapeutic/vaccine design strategies against GAS.
Main task: This PhD position aims to provide a comprehensive picture of the surface interactome of GAS including the interactome of its major virulence factors by using integrative structural biology approaches enhanced with machine learning methods. Such interactome has been previously addressed for the M1 protein, the most important GAS antigen through several integrative approaches combining cross-linking mass spectrometry, cryo-EM, and electron microscopy [1, 4, 6, 7], however, here the goal is to develop a deep learning model to predict such interactions in large scale and further approve them through collaboration with leading microbiology labs in this field. The candidate will then incorporate the obtained knowledge to start targeting the selected interactions for the design of inhibitory proteins and/or new therapeutics.
- S. Hauri and H. Khakzad et al., "Rapid determination of quaternary protein structures in complex biological samples," Nature Communications, vol. 10, no. 192, 2019.
- JK. Leman et. al., “Macromolecular modeling and design in Rosetta: recent methods and frameworks,” Nature Methods, vol. 17, no. 7, 2020
- C. Goverde et. al., “De novo protein design by inversion of the AlphaFold structure prediction network,” biorxiv, 2023.
- H. Khakzad et al., "Structural determination of Streptococcus M1 protein interaction with human IgGs using targeted cross-linking mass spectrometry," PLoS Computational Biology, vol. 17, no. 1, p. e1008169, 2021.
- S. Z. Alborzi et al., "PPIDomainMiner: Inferring domain-domain interactions from multiple sources of protein-protein interactions," PLoS Computational Biology, 17 (8), 2021.
- L. Happonen et al., "A quantitative Streptococcus pyogenes–human protein–protein interaction map reveals localization of opsonizing antibodies," Nature Communications, vol. 10, no. 2727, 2019.
- S. Chowdhury et al., "Streptococcus pyogenes Forms Serotype- and Local Environment- Dependent Interspecies Protein Complexes," mSystems, vol. 6, no. 5, 2021.
- Literature review (being reported through monthly journal clubs)
- Developing deep learning models
- Implementing the method and preparing a software using Python
- Validating the method and analysing the results
- Participating to experimental validation of the results by external microbiologist partners (in-site internships).
- Writing dissertation, scientific articles and presenting the work in international conferences
- Master's degree in Computer Science, Bioinformatics, Chemoinformatics or a related master program
- Proficiency in programming languages (Python) and good coding practices is a must
- Skills in algorithm design
- Experience in machine learning and/or deep learning (scikit, PyTorch)
- Ability to work independently and also to work in a team
- Excellent oral and written English skills
Procédure : Send a CV and a motivation letter to Hamed Khakzad: firstname.lastname@example.org Before May 31st.
Date limite : 31 août 2026
Offre publiée le 28 avril 2023, affichage jusqu'au 31 août 2026