Computational biology of proteins: structure, function and evolution
The goal of our research is to understand more about how biology works at the molecular level, with a particular focus on proteins and their 3D structure and evolution. We use computational techniques to gain a theoretical understanding of how enzymes perform catalysis. We gather relevant data from the literature and develop novel software tools, which allow us to characterise enzyme mechanisms and navigate the catalytic and substrate space. In parallel, we investigate the evolution of these enzymes to discover how they can evolve new mechanisms and specificities. This involves integrating heterogeneous data with phylogenetic relationships within protein families, which are based on protein structure classification data derived by colleagues at University College London (UCL). The practical goal of this research is to improve the prediction of function from sequence and structure and to enable the design of new proteins or small molecules with novel functions.
We also explore sequence variation between individuals in different contexts and for different species. To understand more about the molecular basis of ageing in different organisms, we participate in a strong collaboration with experimental biologists at UCL. Our role is to analyse functional genomics data from flies, worms and mice and, by developing new software tools, relate these observations to effects on life span.
Our work on understanding enzymes and their mechanisms using structural and chemical information will include a study of how enzymes, their families and pathways have evolved. We will study sequence variation in different individuals, including humans, flies and bacteria, and explore how genetic variations impact on the structure and function of a protein and sometimes cause disease. We will seek to gain a better understanding of reaction space and its impact on pathways, and to use this new knowledge to improve chemistry queries across our databases. Using evolutionary approaches, we hope to improve our prediction of protein function from sequence and structure. We will also improve our analyses of survival curves and combine data with network analysis for flies, worms and mice in order to compare the different pathways and ultimately explore effects related to human variation and age. This information should be of great use to mankind, although, as with any knowledge, will require careful consideration to avoid any possibility of abuse.
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