Motivation: Structure based methods for drug design offer great potential for in-silico discovery of novel drugs but require accurate models of the target protein. Because many proteins, in particular transmembrane proteins, are difficult to characterize experimentally, methods of protein structure prediction are required to close the gap between sequence and structure information. Established methods for protein structure prediction work well only for targets of high homology to known proteins, while biophysics based simulation methods are restricted to small systems and require enormous computational resources. Results: Here we investigate the performance of a world-wide distributed computing network, POEM@HOME, which implements a biophysical model for protein modeling, as a robust computational infrastructure for protein structure prediction. We demonstrate the use of this network for the time-consuming energy relaxations for decoy sets and two targets of the 2010 protein structure prediction assessment (CASP). Conclusion: We demonstrated the use of the POEM@HOME network as a robust computational resource for protein structure prediction based on relaxation in biophysical models. Efforts to implement a web-interface to make this resource available to lifescience researchers are presently under way.
['Timo Strunk', 'Priya Anand', 'Martin Brieg', 'Moritz Wolf', 'Konstantin V. Klenin', 'Irene Meliciani', 'Frank Tristram', 'Ivan Kondov', 'Wolfgang Wenzel']
Benchmarking the POEM@HOME Network for Protein Structure Prediction