Abstract
The configuration space of many complex physical systems presents a rough energy landscape consisting of tremendous number of local minima separated by high energy barriers. One way to overcome these barriers is to perform the simulation in a generalized ensemble where each state is weighted by a non-Boltzmann probability weight factor. The multicanonical ensemble approach (MUCA) overcomes this difficulty by performing a random walk in one-dimensional energy space. Our attempts to design hybrid generalized ensemble algorithms will be reported. The folding of a protein into its native structure involves one or more transitions between distinct phases. The representation of the energy landscape would be useful for the determination of the conformational transition temperatures. Such a study would lead to clear indications of the equilibrium conformations of proteins and provide a detailed picture of the folding pathway. The topographic structure of energy landscape of short peptides is presented.
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