About the Computational Biology and Clinical Informatics laboratory
On-going technological advancements have led to dramatic increases in the amounts of biological data being generated. Along with the evolution of high performance computing and computational tools, this has provided us with a wealth of information, analytical power and the opportunity to investigate fundamental health and biotechnological problems of a different magnitude and kind, complementary to and able to guide conventional approaches. Our laboratory is interested in developing and experimentally validating novel computational methods to exploit this data, enhancing the impact of genome sequencing, structural genomics, and functional genomics on biology and medicine.
One of our main areas of interest is in the development of predictive and analytical tools and databases to investigate and understand the relationship between protein sequence, structure and function and phenotype, allowing us to gain unique insights into:
- The molecular basis of genetic diseases, including cancer.
- Understanding the molecular mechanisms behind drug resistance, to guide personalised patient treatment and the development of resistance resistant drugs.
- Evolutionary insights derived from the analysis of protein structure and function.
- Small molecule activity and toxicity as an aid to the design of novel drugs.
We have developed and host a wide range of widely used and freely-available tools, including:
Calculation and visualisation of all molecular interactions.
Reliable and open source virtual screening and clustering.
Predicting the protein binding affinity of small molecules.
Quantification of the extent of localised purifying selection in protein-coding sequences.
Predicting effects of mutations on protein stability.
An integrated method for predicting effects of mutations on protein stability.
Analysis and visualisation of protein dynamics using normal mode analysis. Quantitative prediction of the effects of missense mutations on protein dynamics and stability.
An optimised knowledge based method for predicting effects of mutations on protein stability.
Predicting the effects of mutations on membrane proteins.
Predicting effects of mutations on the affinity of protein-protein interactions.
Optimised predictions of the effects of mutations on the affinity of protein-protein interactions.
Predicting effects of mutations on antibody-antigen binding affinity.
Optimised predictions of the effects of mutations on antibody-antigen binding affinity.
Predicting the effects of introducing multiple point mutations on antibody-antigen binding affinity.
Predicting effects of mutations on the affinity of protein-DNA interactions.
Optimised predictions of the effects of mutations on the affinity of protein-nucleic acid interactions.
Predicting effects of protein mutations on affinity for small molecules.
Identification of protein kinase activating mutations.
Predicting the effect of mutations in AtpE on Bedaquiline sensitivity.
Predicting the effect of mutations in pncA on Pyrazinamide sensitivity.
Predicting the effect of mutations in rpoB on Rifampicin sensitivity
Predicting small molecule pharmacokinetic and toxicity properties.
Prediction and optimisation of dendrimer intravenous pharmacokinetic profiles.
Structural database of experimentally measured effects of missense mutations on protein-ligand complexes.
Optimisation of Botulinum and Tetnus neurotoxins for medicinal purposes.
Classification of VHL missense mutations according to risk of clear cell Renal carcinoma.