Computational analysis of host-pathogen surface interactome to design novel therapeutics

Computational analysis of host-pathogen surface interactome to design novel therapeuticsDescriptionContextAntimicrobial 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 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 evade immune responses. Such information will be used 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 prioritize the best targets. The next step is to design specific binders in silico for selected targets. The candidate will also collaborate with international teams of experts 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. 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.ResponsibilitiesStudy the surface interactome of pathogens and their interactions with hosts.Develop computational tools for predicting protein functions.Design protein binders in silico.Collaborate with international teams to validate designs in vitro.Write scientific articles and present work at international conferences.SkillsPhD degree in Computer Science, Bioinformatics, Chemoinformatics, or a related program.Proficiency in programming languages (Python) and good coding practices is a must.Experience in machine learning and/or deep learning (scikit, PyTorch) is a plus.Ability to work independently and collaboratively.Excellent oral and written English skills.Benefits packageFully funded position (1.5 years, renewable up to 6 more months).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.DurationDuration: 18 monthsApplicationSend a CV and a motivation letter to Hamed Khakzad: [email protected]: • a detailed recent CV • a list of publications, if any • a cover letter describing the candidate’s research interest and expertise relevant to the subject • the name of at least 2 scientists willing to provide a letter of recommendation, including PhD supervisor • the PhD thesis reports if available • links to the PhD thesis if available • links to personal code repositories (e.g., GitHub), if anyContacts #J-18808-Ljbffr