Student research project
Supervisor(s): David Ascher
The recent explosion in the power of cryo-electron microscopy has revolutionised the structural biology field, especially the characterisation of large protein complexes. This is helping us tackle very important biological problems in a way that they could never before. There are, however, inherent limitations that not only pose difficulties to the structure solving stage, i.e. properly positioning a protein chain within an electron density map, but also potentially introducing errors that might be propagated to the refined structure. This is especially relevant for medium resolution structures (4–8 Å). The problem is analogous to looking through blurry (or drunk) glasses and, without good points of reference, not being able to orientate yourself. To improve this procedure we can leverage the power of our existing structural and evolutionary knowledge accumulated over decades and deposited in structural databases in order to help guide the proposal of a more effective methods for this molecule placement. This project will use structural bioinformatics and machine learning to develop novel computational tools to aid cryo-EM and low resolution crystal structure solving, analysing protein residue environments, protein interaction interfaces, and protein functional sites. These methods will be brought together into an integrated platform for the evaluation and validation of medium resolution protein structures.