The protein folding problem is to study how a protein dynamically folds to its so-called native state – an energetically
stable, three-dimensional configuration. Understanding this process is of great practical importance since some devastating diseases such as Alzheimer’s and bovine spongiform encephalopathy (Mad Cow) are associated with the misfolding of proteins. In our group, we have developed a new computational technique for studying protein folding
that is based on probabilistic roadmap methods for motion planning. Our technique yields an approximate map of
a protein’s potential energy landscape that contains thousands of feasible folding pathways. We have validated our
method against known experimental results. Other simulation techniques, such as molecular dynamics or Monte Carlo methods, require many orders of magnitude more time to produce a single, partial, trajectory.
In this paper we report on our experiences parallelizing
our method using STAPL (the Standard Template Adaptive Parallel Library), that is being developed in the Parasol Lab at Texas A&M. An efficient parallel version will enable us to study larger proteins with increased accuracy. We demonstrate how STAPL enables portable efficiency across multiple platforms without user code modification. We show
performance gains on two systems: a dedicated Linux cluster and an extremely heterogeneous multiuser Linux cluster.