While structurally very different, protein and RNA molecules share an important
attribute. The motions they undergo are strongly related to the function they perform. For example,
many diseases such as Mad Cow disease or Alzheimer’s disease are associated with protein misfolding
and aggregation. Similarly, RNA folding velocity may regulate the plasmid copy number, and RNA
folding kinetics can regulate gene expression at the translational level. Knowledge of the stability,
folding, kinetics and detailed mechanics of the folding process may help provide insight into how
proteins and RNAs fold. In this paper, we present an overview of our work with a computational
method we have adapted from robotic motion planning to study molecular motions. We have validated
against experimental data and have demonstrated that our method can capture biological
results such as stochastic folding pathways, population kinetics of various conformations, and relative
folding rates. Thus, our method provides both a detailed view (e.g., individual pathways) and a
global view (e.g., population kinetics, relative folding rates, and reaction coordinates) of energy landscapes
of both proteins and RNAs. We have validated these techniques by showing that we observe
the same relative folding rates as shown in experiments for structurally similar protein molecules
that exhibit different folding behaviors. Our analysis has also been able to predict the same relative
gene expression rate for wild-type MS2 phage RNA and three of its mutants.