GOR method

<p>The GOR method (short for Garnier–Osguthorpe–Robson) is an <a href="/facts/Information_theory/qNLH3U5P">information theory</a>-based method for the <a href="/facts/Protein_structure_prediction/sj6AbLjI">prediction</a> of <a href="/facts/Secondary_structure/VaN6MEYh">secondary structures</a> in <a href="/facts/Protein/Jxmagu3E">proteins</a>. It was developed in the late 1970s shortly after the simpler <a href="/facts/Chou%25E2%2580%2593Fasman_method/IaamXq3C">Chou–Fasman method</a>. Like Chou–Fasman, the GOR method is based on <a href="/facts/Probability/fgYvMhwb">probability</a> parameters derived from empirical studies of known protein <a href="/facts/Tertiary_structure/VaN6MEYh">tertiary structures</a> solved by <a href="/facts/X-ray_crystallography/WtI0F5DN">X-ray crystallography</a>. However, unlike Chou–Fasman, the GOR method takes into account not only the propensities of individual <a href="/facts/Amino_acid/WDxYwBny">amino acids</a> to form particular secondary structures, but also the <a href="/facts/Conditional_probability/QcN2UERV">conditional probability</a> of the amino acid to form a secondary structure given that its immediate neighbors have already formed that structure. The method is therefore essentially <a href="/facts/Bayesian_analysis/tEpK3zLx">Bayesian</a> in its analysis.
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GOR method open-in-new

GOR method