Alzheimer’s Disease is caused by the aggregation of the beta-amyloid peptide into toxic oligomers. We design and discover new drugs to interfere with beta-amyloid production and deposition, and reduce its toxicity. For example, we have rationally designed modified peptides to act as inhibitors of beta-amyloid aggregation and toxicity. Drugs are tested with a variety of biophysical and toxicity assays in Manchester, and in vivo by collaborators, to characterise in detail how they work. We are currently screening compound libraries to find new drugs.
Proteomics and Metabolomics
We are using qualitative and quantitative proteomics and metabolomics methods to discover how cells are affected by beta-amyloid. We have found dozens of proteins that respond to beta-amyloid, giving new insights into the biology of Alzheimer’s Disease, and new potential biomarkers and drug targets.
Type 2 diabetes appears to be caused by the toxic amylin peptide killing islets cells in the pancreas that make insulin. We are therefore also seeing how the proteome of cultured islets cells is affected by amylin.
Green Flurorescent Protein (GFP) is very widely used to report on gene expression. Its use is hindered, however, by causing an unwanted immune response in commonly used mouse strains. We are therefore rationally modifying GFP to remove its immunogenicity while maintaining its fluorescent properties.
Protein Vibrational Spectroscopy
Infra-red, Raman and Raman Optical Activity spectra are rich in information about protein structure. We use bioinformatics methods to determine protein structure from these spectra. We have found that secondary structure contents can be determined to unprecedented accuracy.
Drug Target Proteins
Drug discovery typically requires the identification of protein targets capable of eliciting a desired biological response. We are studying drug target proteins to identify properties that are desirable in human drug targets, considering target proteins for different diseases.
Part of this work requires generating non-redundant sets of proteins, where no two proteins have a pairwise sequence similarity above a set threshold. We generate substantially larger sets than previously used, using novel Graph Theory methods.