Communications of the ACM
In Search of Tomorrow's Cures
Computer prediction of a novel protein inhibitor binding to a JNK substrate docking site.
Credit: Texas Advanced Computing Center
University of Texas at Austin (UTA) researchers are using physical models and supercomputers to create a more robust way of searching for new drugs. Working with the Texas Advanced Computing Center (TACC) and the Texas Institute for Drug and Diagnostic Development, UTA professor Pengyu Ren led a team that developed computational algorithms for drug discovery using the Ranger supercomputer. "We're testing and developing computational approaches that can best reproduce the experimental data of protein-ligand binding that has been reported in the literature," Ren says.
The researchers are experimenting with algorithms that use explicit or continuum methods to describe the solvent environment and account for entropic contribution to protein-ligand binding via molecular dynamics simulation.
"Pengyu's work is an excellent example of how current advances in computing power are enabling scientists to take a fundamentally different approach to virtual drug discovery," says TACC's Michael Gonzales.
Ren also is involved in a joint project with UTA's Kevin Dalby, examining how computation will guide the search for selective inhibitors for protein kinases, which are clinically relevant to cancer and other diseases. "If this works, it will improve our ability to design drug candidates that are more potent with fewer side effects," Ren says.
From University of Texas at Austin
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