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Graphical models have become powerful frameworks for protein structure prediction, protein–protein interaction and free energy calculations for protein structures. Using a graphical model to represent the protein structure allows the solution of many problems including secondary structure prediction, protein protein interactions, protein-drug interaction, and free energy calculations. There are two main approaches to use graphical models in protein structure modeling. The first approach uses discrete variables for representing coordinates or dihedral angles of the protein structure. The variables are originally all continuous values and, to transform them into discrete values, a discretization process is typically applied. The second approach uses continuous variables for the coordinates or dihedral angles. ==Discrete graphical models for protein structure== Markov random fields, also known as undirected graphical models are common representations for this problem. Given an undirected graph ''G'' = (''V'', ''E''), a set of random variables ''X'' = (''X''''v'')''v'' ∈ ''V'' indexed by ''V'', form a Markov random field with respect to ''G'' if they satisfy the pairwise Markov property: *any two non-adjacent variables are conditionally independent given all other variables: : In the discrete model, the continuous variables are discretized into a set of favorable discrete values. If the variables of choice are dihedral angles, the discretization is typically done by mapping each value to the corresponding rotamer conformation. 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「Graphical models for protein structure」の詳細全文を読む スポンサード リンク
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