Exploration of compact protein conformations using the guided replication Monte Carlo method.

We have studied the use of a new Monte Carlo (MC) chain generation algorithm, introduced by T. Garel and H. Orland [(1990) Journal of Physics A, Vol. 23, pp. L621-L626], for examining the thermodynamics of protein folding transitions and for generating candidate C(alpha) backbone structures as starting points for a de novo protein structure paradigm. This algorithm, termed the guided replication Monte Carlo method, allows a rational approach to the introduction of known "native" folded characteristics as constraints in the chain generation process . We have shown this algorithm to be computationally very efficient in generating large ensembles of candidate C(alpha) chains on the face centered cubic lattice, and illustrate its use by calculating a number of thermodynamic quantities related to protein folding characteristics. In particular, we have used this static MC algorithm to compare such temperature-dependent quantities as the ensemble mean energy, ensemble mean free energy, the heat capacity, and the mean-square radius of gyration. We also demonstrate the use of several simple "guide fields" for introducing protein-specific constraints into the ensemble generation process. Several extensions to our current model are suggested, and applications of the method to other folding related problems are discussed.