Learning host-pathogen surface interactome to design novel therapeutics

Learning host-pathogen surface interactome to design novel therapeuticsDescriptionContext: 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 has been increasingly observed worldwide, showing an essential need for designing novel therapeutics to increase the effectiveness of fighting pathogens. Accordingly, a 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 evade immune responses. Such information will be used to design protein binders to inhibit these interactions. To achieve this goal, the candidate will develop a deep learning model to predict the binding sites and potential binders within the search space. This position will provide opportunities for 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, 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, along with several PhD and master students.AssignmentBackground: 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; however, here the goal is to develop a deep learning model to predict such interactions on a large scale and further validate 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.Main activitiesLiterature review (reported through monthly journal clubs)Implementing the method and preparing software using PythonValidating the method and analyzing the resultsParticipating in experimental validation of the results by external microbiologist partners (in-site internships)Writing dissertation, scientific articles, and presenting the work at international conferencesSkillsMaster's degree in Computer Science, Bioinformatics, Chemoinformatics, or a related master's programProficiency in programming languages (Python) and good coding practices is a mustSkills in algorithm designExperience in machine learning and/or deep learning (scikit, PyTorch)Ability to work independently and also as part of a teamExcellent oral and written English skillsCandidatureProcedure: Send a CV and a motivation letter to Hamed Khakzad: [email protected] before May 31st. #J-18808-Ljbffr