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The self-consistent mean field (SCMF) method is an adaptation of mean field theory used in protein structure prediction to determine the optimal amino acid side chain packing given a fixed protein backbone. It is faster but less accurate than dead-end elimination and is generally used in situations where the protein of interest is too large for the problem to be tractable by DEE. ==General principles== Like dead-end elimination, the SCMF method explores conformational space by discretizing the dihedral angles of each side chain into a set of rotamers for each position in the protein sequence. The method iteratively develops a probabilistic description of the relative population of each possible rotamer at each position, and the probability of a given structure is defined as a function of the probabilities of its individual rotamer components. The basic requirements for an effective SCMF implementation are: # A well-defined finite set of discrete independent variables # A precomputed numerical value (considered the "energy") associated with each element in the set of variables, and associated with each binary element pair # An initial probability distribution describing the starting population of each individual rotamer # A way of updating rotamer energies and probabilities as a function of the mean-field energy The process is generally initialized with a uniform probability distribution over the rotamers — that is, if there are rotamers at the position in the protein, then the probability of any individual rotamer is . The conversion between energies and probabilities is generally accomplished via the Boltzmann distribution, which introduces a temperature factor (thus making the method amenable to simulated annealing). Lower temperatures increase the likelihood of converging to a single solution, rather than to a small subpopulation of solutions. 抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)』 ■ウィキペディアで「Self-consistent mean field (biology)」の詳細全文を読む スポンサード リンク
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