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Absolute quality evaluation of protein model structures using statistical potentials with respect to the native and reference states
Author(s) -
Shirota Matsuyuki,
Ishida Takashi,
Kinoshita Kengo
Publication year - 2011
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.22982
Subject(s) - absolute (philosophy) , statistics , quality (philosophy) , econometrics , mathematics , physics , quantum mechanics , philosophy , epistemology
In protein structure prediction, it is crucial to evaluate the degree of native‐likeness of given model structures. Statistical potentials extracted from protein structure data sets are widely used for such quality assessment problems, but they are only applicable for comparing different models of the same protein. Although various other methods, such as machine learning approaches, were developed to predict the absolute similarity of model structures to the native ones, they required a set of decoy structures in addition to the model structures. In this paper, we tried to reformulate the statistical potentials as absolute quality scores, without using the information from decoy structures. For this purpose, we regarded the native state and the reference state, which are necessary components of statistical potentials, as the good and bad standard states, respectively, and first showed that the statistical potentials can be regarded as the state functions, which relate a model structure to the native and reference states. Then, we proposed a standardized measure of protein structure, called native‐likeness, by interpolating the score of a model structure between the native and reference state scores defined for each protein. The native‐likeness correlated with the similarity to the native structures and discriminated the native structures from the models, with better accuracy than the raw score. Our results show that statistical potentials can quantify the native‐like properties of protein structures, if they fully utilize the statistical information obtained from the data set. Proteins 2011; © 2011 Wiley‐Liss, Inc.

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