###Discovery and analysis of novel microbial nanocompartments from metagenome databases
Project ID: 2228bd1180 (You will need this ID for your application)
Research Theme: Engineering
UCL Lead department: Biochemical Engineering
Lead Supervisor: Stefanie Frank
Project Summary:
Self-assembling virus-related protein nanocompartments, called encapsulins, have high potential for industrial (enzyme catalysis and stability) and biomedical applications (drug delivery, antigen display) evidenced by an increasing number of successful proof-of-concept studies (https://doi.org/10.1016/j.synbio.2021.09.001). Around 6,000 encapsulin-like systems with high functional and structural diversity have been predicted across bacterial and archaeal phyla, including functions in natural product biosynthesis, stress resistance, carbon metabolism and anaerobic hydrogen production. To date only a handful of these systems have been characterised, leaving thousands of protein structures and associated enzyme reactions to be explored, promising new insights into these novel diverse biological assemblies.
Building on the supervisory team’s combined expertise in bacterial compartment engineering, structural analysis and enzyme discovery, we aim to combine metagenomics, AI structure prediction, synthetic biology and automation to generate a curated library of new encapsulin systems with novel protein folds/functions for applications in nano-scale biocatalysis and bioengineering.
Objectives: • Sourcing unknown HK97-fold encapsulin-like systems combining metagenome analysis (existing metagenome databases in the department) and AI structure prediction tools (AlphaFold, MetaAI). • Establishing high-throughput cloning and expression/characterisation strategies (in vivo and cell-free protein synthesis) using automation technologies. Building an encapsulin library, sampling from different encapsulin families and across bacterial phyla. • Analysis of structure and function of selected systems, identification of novel protein folds and enzyme encapsulation mechanisms. • Gaining new insights into structure and assembly principles. • Applications in enzyme catalysis, antigen display and other potential applications
The successful candidate will have the opportunity to develop skills in bioinformatics, metagenome mining, AI structure prediction, synthetic biology, and biochemical/biophysical/structural analysis of multiprotein assemblies.
The preferred candidate will have some experience in bioinformatics, molecular biology and/or automation. The candidate will be expected to have at least an upper second-class Bachelor’s degree or equivalent at Masters level in Biochemistry, Bioinformatics, Biochemical Engineering or related degree.