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Supercomputer helps define role of PARP enzyme

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An Ohio State University molecular biologist has used a supercomputer to help better define the family tree of a group of enzymes that have been implicated in a wide range of human diseases and are important targets for anti-cancer therapies. Along with several OSU colleagues, Rebecca S. Lamb, an assistant professor of Molecular Genetics, recently analysed the evolutionary history of the poly (ADP-ribose) polymerase (PARP) superfamily.

These proteins are found in eukaryotes, a wide range of organisms – animals, plants, moulds, fungi, algae and protozoa – whose cells contain a distinct nucleus, and often other structures, enclosed within their own membranes. While PARP proteins can be found with any of these ‘supergroups,’ they have been most extensively studied in mammals. ‘In these organisms, PARPs have key functions in DNA repair, genome integrity and epigenetic regulation, said Lamb. ‘More recently it has been found that proteins within the PARP superfamily have a broader range of functions that initially predicted.’

The researchers used computers to identify 236 PARP proteins from 77 species across five of the six supergroups. Lamb then accessed the Glen Cluster at the Ohio Supercomputer Center (OSC) to perform extensive phylogenetic analyses of the identified PARP regions. ‘This is computationally intensive work that would have been impossible without the computer resources provided at OSC,’ Lamb said. ‘In particular, the ability to try a variety of tools that require a great deal of CPU and memory capabilities was essential for success.’

Amongst other tools, she employed the PhyML3.0 software package, which fit a statistical model to the aligned sequence data and provided estimates for the model’s parameters. The study, ‘Evolutionary history of the poly (ADP-ribose) polymerase gene family in eukaryotes,’ was authored by Lamb and OSU colleagues Matteo Citarelli and Sachin Teotia, and appeared in a recent issue of the journal BMC Evolutionary Biology. The work was supported by a grant from the Ohio Plant Biotechnology Consortium and by funds from the Ohio State University.