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Every amino acid has a side chain consisting of part of the side chain of the previous residue. The relative disposition of the side chain depends on the side chain parameters of the first residue. Because of this, it is more convenient to describe side chain interactions as a function of the order parameter of the current residue, mk. The side chain parameter of the current residue mk is conditioned on the side chain parameters and the known order parameters of the three preceding residues (all of whose parameters are chosen to minimize the free energy). This conditional distribution is used in place of the unconditional distribution so that side chain parameters can be propagated in an optimal manner. This section contains a description of a general algorithm for computing the conditional probability, derived from the message passing of the belief propagation algorithm. The conditional probability is written as a product of a set of individual message calculations, and the message calculations are obtained from the likelihood function. The message calculations are computed recursively, starting from the most coarse-grain feature to the most fine-grain feature.
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