A stochastic algorithm for global optimization and for best populations: a test case of side chains in proteins.

ABSTRACT: The problem of global optimization is pivotal in a variety of scientific fields. Here, we present a robust stochastic search method that is able to find the global minimum for a given cost function, as well as, in most cases, any number of best solutions for very large combinatorial "explosive" systems. The algorithm iteratively eliminates variable values that contribute consistently to the highest end of a cost function's spectrum of values for the full system. Values that have not been eliminated are retained for a full, exhaustive search, allowing the creation of an ordered population of best solutions, which includes the global minimum. We demonstrate the ability of the algorithm to explore the conformational space of side chains in eight proteins, with 54 to 263 residues, to reproduce

SUBMITTER: Glick M 

PROVIDER: S-EPMC117369 | biostudies-literature | 2002 Jan

REPOSITORIES: biostudies-literature