In partnership with Biophoundry, the PhD candidate will train ML models to identify highly conserved regions on the bacterial surface. Using AI-based protein design methods, the candidate will design novel Receptor Binding Domains (RBDs) for the phage tail fibre, which will be assembled using Biophoundry\u2019s proprietary “Trinity” technology. This will be used to generate synthetic phages with a broad host range to develop effective antibacterial therapies. These engineered phages will help in reducing the risk of resistance evolution.<\/p>\n\n\n\n
This project will work on pre-existing data from Biophoundry, as well as publicly available data, including 1) Genomic and structural data for model phages T7 and K1f. 2) Genomic data from large panels of K. pneumoniae (100 strains) and Uropathogenic E. coli provided by the Gally lab. 3) Results from a 100×100 cross-infection experiment mapping host range provided by Biophoundry. The student will develop an ML methodology for Dataset Generation\u00a0Predictive Modelling.<\/p>\n","protected":false},"excerpt":{"rendered":"
In partnership with Biophoundry, the PhD candidate will train ML models to identify highly conserved regions on the bacterial surface. Using AI-based protein design methods, the candidate will design novel Receptor Binding Domains (RBDs) for the phage tail fibre, which will be assembled using Biophoundry\u2019s proprietary “Trinity” technology. This will be used to generate synthetic […]<\/p>\n","protected":false},"author":5,"featured_media":8852,"template":"","job_type":[436],"job_discipline":[444,476],"job_location":[449],"class_list":["post-8851","job","type-job","status-publish","has-post-thumbnail","hentry","job_type-phd","job_discipline-bioinformatics","job_discipline-biotechnology","job_location-uk"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t