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Methods of protein structure comparison.

ABSTRACT: Despite its apparent simplicity, the problem of quantifying the differences between two structures of the same protein or complex is nontrivial and continues evolving. In this chapter, we described several methods routinely used to compare computational models to experimental answers in several modeling assessments. The two major classes of measures, positional distance-based and contact-based, are presented, compared, and analyzed. The most popular measure of the first class, the global RMSD, is shown to be the least representative of the degree of structural similarity because it is dominated by the largest error. Several distance-dependent algorithms designed to attenuate the drawbacks of RMSD are described. Measures of the second class, contact-based, are shown to be more robust and relevant. We also illustrate the importance of using combined measures, utility-based measures, and the role of the distributions derived from the pairs of experimental structures in interpreting the results.

SUBMITTER: Kufareva I

PROVIDER: S-EPMC4321859 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Methods of protein structure comparison.

Kufareva Irina I Abagyan Ruben R

Methods in molecular biology (Clifton, N.J.) 20120101

Despite its apparent simplicity, the problem of quantifying the differences between two structures of the same protein or complex is nontrivial and continues evolving. In this chapter, we described several methods routinely used to compare computational models to experimental answers in several modeling assessments. The two major classes of measures, positional distance-based and contact-based, are presented, compared, and analyzed. The most popular measure of the first class, the global RMSD, i ...[more]

PMID: 22323224

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Project description:BackgroundCurrent classification of protein folds are based, ultimately, on visual inspection of similarities. Previous attempts to use computerized structure comparison methods show only partial agreement with curated databases, but have failed to provide detailed statistical and structural analysis of the causes of these divergences.ResultsWe construct a map of similarities/dissimilarities among manually defined protein folds, using a score cutoff value determined by means of the Receiver Operating Characteristics curve. It identifies folds which appear to overlap or to be "confused" with each other by two distinct similarity measures. It also identifies folds which appear inhomogeneous in that they contain apparently dissimilar domains, as measured by both similarity measures. At a low (1%) false positive rate, 25 to 38% of domain pairs in the same SCOP folds do not appear similar. Our results suggest either that some of these folds are defined using criteria other than purely structural consideration or that the similarity measures used do not recognize some relevant aspects of structural similarity in certain cases. Specifically, variations of the "common core" of some folds are severe enough to defeat attempts to automatically detect structural similarity and/or to lead to false detection of similarity between domains in distinct folds. Structures in some folds vary greatly in size because they contain varying numbers of a repeating unit, while similarity scores are quite sensitive to size differences. Structures in different folds may contain similar substructures, which produce false positives. Finally, the common core within a structure may be too small relative to the entire structure, to be recognized as the basis of similarity to another.ConclusionA detailed analysis of the entire available protein fold space by two automated similarity methods reveals the extent and the nature of the divergence between the automatically determined similarity/dissimilarity and the manual fold type classifications. Some of the observed divergences can probably be addressed with better structure comparison methods and better automatic, intelligent classification procedures. Others may be intrinsic to the problem, suggesting a continuous rather than discrete protein fold space.

Project description:Understanding functions of proteins is one of the most important challenges in many studies of biological processes. The function of a protein can be predicted by analyzing the functions of structurally similar proteins, thus finding structurally similar proteins accurately and efficiently from a large set of proteins is crucial. A protein structure can be represented as a vector by 3D-Zernike Descriptor (3DZD) which compactly represents the surface shape of the protein tertiary structure. This simplified representation accelerates the searching process. However, computing the similarity of two protein structures is still computationally expensive, thus it is hard to efficiently process many simultaneous requests of structurally similar protein search. This paper proposes indexing techniques which substantially reduce the search time to find structurally similar proteins. In particular, we first exploit two indexing techniques, i.e., iDistance and iKernel, on the 3DZDs. After that, we extend the techniques to further improve the search speed for protein structures. The extended indexing techniques build and utilize an reduced index constructed from the first few attributes of 3DZDs of protein structures. To retrieve top-k similar structures, top-10 × k similar structures are first found using the reduced index, and top-k structures are selected among them. We also modify the indexing techniques to support θ-based nearest neighbor search, which returns data points less than θ to the query point. The results show that both iDistance and iKernel significantly enhance the searching speed. In top-k nearest neighbor search, the searching time is reduced 69.6%, 77%, 77.4% and 87.9%, respectively using iDistance, iKernel, the extended iDistance, and the extended iKernel. In θ-based nearest neighbor serach, the searching time is reduced 80%, 81%, 95.6% and 95.6% using iDistance, iKernel, the extended iDistance, and the extended iKernel, respectively.

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Methods of protein structure comparison.

Publications

Methods of protein structure comparison.

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