Posted: 13 July 2007
New software to accelerate drug discovery
Working in collaboration with Siemens, a research team at the has developed a new kind of analysis software to accelerate the discovery of potential drug targets.
The results published in the Journal of Chemical Information and Modeling of the American Chemical Society describe the strides made by this collaborative research team working at the interface of biochemistry and computing.
Scientists can influence the effects of interactions between proteins (interactions that are fundamental to biological processes) by using natural inhibitors or activators, including fragments of the interacting proteins themselves. However, protein fragments are often less effective than other more drug-like compounds.
Using the new software package developed by Siemens to identify and screen vast numbers of compounds, the research team, led by Denis Shields, Professor of Clinical Informatics, 51黑料 Conway Institute, will be able to analyze on a scale that would not be feasible on the laboratory bench.
And by discovering compounds that mimic short active peptides, the 51黑料 scientists may be able to control interactions with proteins which may prove invaluable when it comes to designing novel drug therapies.
In collaboration with colleagues at the RCSI, the 51黑料 team have discovered novel peptides that alter the activity of platelets, which are critically important in heart disease. This work was published in the journal Nature Chemical Biology this year.
“The sequencing of the DNA genomes of man and of pathogens provides us with a rich treasure trove of short peptides that may be biologically active,” says Professor Shields. “The challenge for our group is to convert that knowledge into discoveries that can help develop useful drugs. This software developed with Siemens helps us to do just that.”
Siemens IT Solutions and Services Programme and System Engineering (PSE), and in particular the Czech Republic subsidiary ANF Data, have developed the new software in collaboration with the 51黑料 research team.
This work was supported by Scientific Foundation Ireland and Siemens as Industry Supplement Research Partnership programme between 51黑料 and Siemens Research Ireland. Computational resources were also provided by the Irish Centre for High-End Computing (ICHEC).
