Proteins, nucleic acids and other biological macromolecules take part virtually in all processes within living organisms. Knowledge of their 3D structures is essential for understanding how they work. X-ray Crystallography is a powerful experimental technique that gives 3D structures with high accuracy. According to the Protein Data Bank more than 85% of all structures have been analysed using this technique.
Our research is centred on the development of efficient mathematical, statistical, and computational algorithms for Macromolecular X-ray Crystal (MX) structure analysis. We implement the developed algorithms in software tools and distribute them to the structural biology community. Most of our software is distributed to the community via the UK-based crystallographic software initiative CCP4.
We use the Bayesian method, which combines prior structural and chemical knowledge with MX data, and allows extraction of biologically relevant information from noisy diffraction data. Bayesian Statistics has two components: likelihood, through which new information from the experimental data is transferred to the model; and a probability distribution that ensures the model's consistency with the assumed prior knowledge.
Examples of software developed in our group are: a maximum likelihood refinement program - REFMAC, an automatic structure solution pipeline - BALBES, and a structural comparison and restraint generation program - ProSMART.