Exploiting advanced methods for protein structure prediction

Protein structural information is crucial for an understanding of protein function and evolution. Currently, there is only experimental data for a tiny fraction of the protein universe. Homology modelling allows structure predictions for many more proteins, but there is no structural information at all for a large number of families. Membrane protein families are particularly poorly understood due to the extra experimental difficulties they bring.

The project will entail application of ab initio methods for protein structure prediction including the state-of-the-art fragment assembly program Rosetta and methods based on the availablity of contact predictions derived from evolutionary covariance (Simkovic et al., 2018). Such models have many uses and the student will be able to choose whether to use the models for function annotation or for Molecular Replacement (MR). Function annotation efforts will address currently mysterious protein families of proven medical or biotechnological interest and will entail the application of a battery of structure-based function prediction methods (Rigden, 2017) and other sequence- and genome-based methods. Molecular Replacement development will involve the software pipeline AMPLE, a joint Liverpool-CCP4 program for unconventional MR (Bibby et al., 2012; http://ample.readthedocs.io).

Training:
The project focuses on the fast-moving cutting edge of protein modelling and will provide extensive training in the current leading program Rosetta. Ab initio and contact prediction-based modelling are among the most exciting areas of contemporary bioinformatics and the student will be well placed on completion of the project to exploit the ever-increasing possibilities for its application. A full exploration of the predictive power of ab initio models for protein function requires an integrated approach to understand structure-function and their co-evolution with time. The student will therefore also be exposed to the full spectrum of protein sequence- and structure-based methods and to techniques for inferring functional relationships from genomic data.

The project is suited to a student with at least a good B.Sc. Upper Second in Biological or Life Sciences and a strong interest in bioinformatics. Other graduates may be suitable and enquiries are encouraged.