We investigate a novel approach for studying protein folding that has evolved
from robotics motion planning techniques called probabilistic roadmap methods
(prms). Our focus is to study issues related to the folding process, such as the
formation of secondary and tertiary structure, assuming we know the native fold.
A feature of our prm-based framework is that the large sets of folding pathways
in the roadmaps it produces, in a few hours on a desktop PC, provide global
information about the protein's energy landscape. This is an advantage over other
simulation methods such as molecular dynamics or Monte Carlo methods which
require more computation and produce only a single trajectory in each run. In
our initial studies, we obtained encouraging results for several small proteins. In
this paper, we investigate more sophisticated techniques for analyzing the folding
pathways in our roadmaps. In addition to more formally revalidating our previous
results, we present a case study showing our technique captures known folding
dierences between the structurally similar proteins G and L.