Nested sampling is a technique developed to explore probability distributions localised 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. Previous applications of the algorithm have yielded large efficiency gains over other sampling techniques  including parallel tempering. In this paper we apply the nested sampling algorithm to the problem of protein folding in a  force field of empirical potentials that were designed to stabilize secondary structure elements in room-temperature simulations. 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.  The topology of the protein molecule emerges as a major determinant of the shape of the energy landscape . The algorithm was tested with protein G. The tertiary structure of the protein was predicted to a reasonably high level of accuracy. The best conformation found had an RMSD of 3.12A from the native structure. Two other proteins, chymotrypsin inhibitor 2 and Src tyrosine kinase SH3 domain, were also tested, with the best conformations found having RMSD 4.75A and 5.33A from their respective native structures . The nested sampling algorithm 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.
Nested sampling is a technique developed to explore probability distributions localised 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. Previous applications of the algorithm have yielded large efficiency gains over other sampling techniques , including parallel tempering. In this paper we apply the nested sampling algorithm 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. 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. 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. We compare our results for protein G to those obtained using parallel tempering with the same model. The topology of the protein molecule emerges as a major determinant of the shape of the energy landscape . The nested sampling algorithm 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.
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Go-type
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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. We compare our results for protein G to those obtained using parallel tempering with the same model.
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. The algorithm was tested with protein G. The tertiary structure of the protein was predicted to a reasonably high level of accuracy. The best conformation found had an RMSD of 3.12A from the native structure. Two other proteins, chymotrypsin inhibitor 2 and Src tyrosine kinase SH3 domain, were also tested, with the best conformations found having RMSD 4.75A and 5.33A from their respective native structures