UCL News


Professor Christine Orengo elected member of EMBO

30 May 2014

Professor Orengo (UCL Division of Biosciences) was one of 106 "outstanding researchers in the life sciences" that were elected to be

embo.org/news/press-releases/press-releases-2014/embo-enlarges-its-membership-for-50th-anniversary" target="_self">European Molecular Biology Organisation (EMBO) members in 2014.

Christine Orengo's research has focused on how proteins evolve - how do relatives in a family diverge in structure and function and how do they evolve to operate in different biological contexts. Nearly 20 years ago she established the CATH classification of protein domains, together with Janet Thornton, which is widely accessed (nearly 2 million webpage accesses and 10,000 unique visitors per month) and a partner resource in InterPro. CATH was enabled by the development of robust protein structure comparison algorithms, (SSAP - JMB 1989, CATHEDRAL - PloS Comp Biol 2007) still used by the structural biology and structural genomics communities. Her group participate in two large structural genomics initiatives which exploit CATH for targeting pathogenic proteins (JMB 2005, Structure 2009).

Analysis of CATH led to important insights into the population of structural families and folds (Nature 1994) and revealed the bias in protein domain families whereby less than 5% account for two thirds of all known domains (Annual Reviews Biochem. 2005, NAR 2006). CATH analyses also revealed expansions of functional repertoires in bacteria (TIG 2005), novel structural motifs (Structure 1993) and structural mechanisms mediating changes in protein functions (JMB 2006, TIBS 2009, Structure 2010). More recently, with the assignment of 20 million domains to CATH, sequence analysis has enabled the recognition of functional subfamilies possessing distinct sequence patterns that can aid function prediction (NAR 2010, Nature Methods 2012). The group also study evolution of functions in CATH superfamilies (TIBS 2002, PNAS 2013).

CATH domain families have also been exploited in the prediction and analysis of protein networks (PLoS Comp Biol 2009, Pain 2013) and the group collaborate with several experimental groups characterising signalling networks implicated in development, neuropathic pain and cancer.