  Nested sampling is a Bayesian sampling technique developed to explore
probability distributions lo- calised in an exponentially small area of the
parameter space. The algorithm provides both posterior samples and an estimate
of the evidence (marginal likelihood) of the model. The nested sampling algo-
rithm also provides an efficient way to calculate free energies and the
expectation value of thermodynamic observables at any temperature, through a
simple post-processing of the output. Previous applications of the algorithm
have yielded large efficiency gains over other sampling techniques, including
parallel tempering (replica exchange). In this paper we describe a parallel
implementation of the nested sampling algorithm and its application to the
problem of protein folding in a Go-type force field of empirical potentials
that were designed to stabilize secondary structure elements in
room-temperature simulations. We demonstrate the method by conducting folding
simulations on a number of small proteins which are commonly used for testing
protein folding procedures: protein G, the SH3 domain of Src tyrosine kinase
and chymotrypsin inhibitor 2. A topological analysis of the posterior samples
is performed to produce energy landscape charts, which give a high level
description of the potential energy surface for the protein folding
simulations. These charts provide qualitative insights into both the folding
process and the nature of the model and force field used.
