3-D Structural Analysis

Computational Biology and Chemogenomics Team

Section: Cancer Research UK Cancer Therapeutics Unit

Protein 3D structural data is an extremely powerful tool in drug design and development. The abundance of structural data in the public domain provides researches with much useful structural knowledge. The ICR boasts some of the best structural biology teams in the world which are solving large numbers clinically relevant structures and complexes, enhancing our ability to use 3D structure information for drug discovery.

A curated database of annotated 3D structures will be developed from public domain and existing ICR structural biology sources, to include full annotation of important target families, enumerating different conformational states as well as 2D and 3D structures of complexed ligands. This will involve the design of a relational database and associated tools to populate the database, together with a utility to enable weekly updates. A web-based interface to interrogate and visualise the data in the database will also be developed.  This will facilitate our understanding of drivers of conformational change within a family of related proteins and the drivers of selectivity in small molecule-protein interactions.

Ligand binding footprints will be computationally derived and mapped to the chemical structures of small molecule ligands. Algorithms will be developed to predict the likely binding mode of virtual compounds to targets and to optimally select compound sets for biochemical screening.

Protein modelling to support drug design is a complex exercise and requires very different heuristics and rules compared to standard protein modelling. The understanding gleaned from the components described above will enable high quality, conformationally accurate models to support drug design and molecular modelling activities within the drug discovery programs in The Centre for Cancer Therapeutics.

The understanding of ligand binding coupled with the ability to build accurate models will be used to rationalise and predict the impact of coding SNPs and resistance mutants on drug binding.