Abstract:
: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.
journal_name
Biopolymersjournal_title
Biopolymersauthors
Solomon JE,Liney Ddoi
10.1002/bip.360360504subject
Has Abstractpub_date
1995-11-01 00:00:00pages
579-97issue
5eissn
0006-3525issn
1097-0282journal_volume
36pub_type
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