Abstract
The recent development of the AlphaFold2 method by DeepMind, has led to a massive expansion in high quality predicted protein structure data. Our group have developed computational protocols (Chainsaw, CATH-AlphaFlow) to classify these structural data into evolutionary families. In collaboration with the group of David Jones, also at UCL, we have classified >200 million predicted structures in the AlphaFold database into evolutionary families. This information is available in a new resource – TED – The Encyclopaedia of Domains. My group have also developed methods for subclassifying proteins in evolutionary families into functional families. In the talk I will present some insights from TED and describe how we are using the TED data and our functional families for drug repurposing and to analyse the impacts of genetic variations in lung cancer.
Short BioChristine Orengo is a computational biologist, whose core research has been the development of robust algorithms to capture relationships between protein structures, sequences and functions. She has built one of the most comprehensive protein classifications, CATH, used worldwide by tens of thousands of biologists, and central to many pioneering structural and evolutionary studies.
CATH structural and functional data for hundreds of millions of proteins has enabled studies that revealed essential universal proteins and their biological roles, and extended characterisation of biological systems implicated in disease e.g. in cell division, cancer and ageing. CATH functional sites have revealed protein residues implicated in enzyme efficiency and bacterial antibiotic resistance. This data also identified genetic variations likely to be driving human diseases and the drugs that can be repurposed to offset the pathogenic effects.
Christine is a Vice President of the International Society of Computational Biology (ISCB). She is a Fellow of the Royal Society of Biology and Elected member of EMBO since 2014, and a Fellow of ISCB since 2016. She is a founder of ELIXIR 3DBioInfo.