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Detection of protein fold similarity based on correlation of amino acid properties.

ABSTRACT: An increasing number of proteins with weak sequence similarity have been found to assume similar three-dimensional fold and often have similar or related biochemical or biophysical functions. We propose a method for detecting the fold similarity between two proteins with low sequence similarity based on their amino acid properties alone. The method, the proximity correlation matrix (PCM) method, is built on the observation that the physical properties of neighboring amino acid residues in sequence at structurally equivalent positions of two proteins of similar fold are often correlated even when amino acid sequences are different. The hydrophobicity is shown to be the most strongly correlated property for all protein fold classes. The PCM method was tested on 420 proteins belonging to 64 different known folds, each having at least three proteins with little sequence similarity. The method was able to detect fold similarities for 40% of the 420 sequences. Compared with sequence comparison and several fold-recognition methods, the method demonstrates good performance in detecting fold similarities among the proteins with low sequence identity. Applied to the complete genome of Methanococcus jannaschii, the method recognized the folds for 22 hypothetical proteins.

SUBMITTER: Grigoriev IV

PROVIDER: S-EPMC24434 | biostudies-literature | 1999 Dec

REPOSITORIES: biostudies-literature

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Detection of protein fold similarity based on correlation of amino acid properties.

Grigoriev I V IV Kim S H SH

Proceedings of the National Academy of Sciences of the United States of America 19991201 25

An increasing number of proteins with weak sequence similarity have been found to assume similar three-dimensional fold and often have similar or related biochemical or biophysical functions. We propose a method for detecting the fold similarity between two proteins with low sequence similarity based on their amino acid properties alone. The method, the proximity correlation matrix (PCM) method, is built on the observation that the physical properties of neighboring amino acid residues in sequen ...[more]

PMID: 10588703

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Project description:BACKGROUND: A large number of studies have been carried out to obtain amino acid propensities for ?-helices and ?-sheets. The obtained propensities for ?-helices are consistent with each other, and the pair-wise correlation coefficient is frequently high. On the other hand, the ?-sheet propensities obtained by several studies differed significantly, indicating that the context significantly affects ?-sheet propensity. RESULTS: We calculated amino acid propensities for ?-helices and ?-sheets for 39 and 24 protein folds, respectively, and addressed whether they correlate with the fold. The propensities were also calculated for exposed and buried sites, respectively. Results showed that ?-helix propensities do not differ significantly by fold, but ?-sheet propensities are diverse and depend on the fold. The propensities calculated for exposed sites and buried sites are similar for ?-helix, but such is not the case for the ?-sheet propensities. We also found some fold dependence on amino acid frequency in ?-strands. Folds with a high Ser, Thr and Asn content at exposed sites in ?-strands tend to have a low Leu, Ile, Glu, Lys and Arg content (correlation coefficient = -0.90) and to have flat ?-sheets. At buried sites in ?-strands, the content of Tyr, Trp, Gln and Ser correlates negatively with the content of Val, Ile and Leu (correlation coefficient = -0.93). "All-?" proteins tend to have a higher content of Tyr, Trp, Gln and Ser, whereas "?/?" proteins tend to have a higher content of Val, Ile and Leu. CONCLUSIONS: The ?-helix propensities are similar for all folds and for exposed and buried residues. However, ?-sheet propensities calculated for exposed residues differ from those for buried residues, indicating that the exposed-residue fraction is one of the major factors governing amino acid composition in ?-strands. Furthermore, the correlations we detected suggest that amino acid composition is related to folding properties such as the twist of a ?-strand or association between two ? sheets.

Project description:BACKGROUND: Owing to rapid expansion of protein structure databases in recent years, methods of structure comparison are becoming increasingly effective and important in revealing novel information on functional properties of proteins and their roles in the grand scheme of evolutionary biology. Currently, the structural similarity between two proteins is measured by the root-mean-square-deviation (RMSD) in their best-superimposed atomic coordinates. RMSD is the golden rule of measuring structural similarity when the structures are nearly identical; it, however, fails to detect the higher order topological similarities in proteins evolved into different shapes. We propose new algorithms for extracting geometrical invariants of proteins that can be effectively used to identify homologous protein structures or topologies in order to quantify both close and remote structural similarities. RESULTS: We measure structural similarity between proteins by correlating the principle components of their secondary structure interaction matrix. In our approach, the Principle Component Correlation (PCC) analysis, a symmetric interaction matrix for a protein structure is constructed with relationship parameters between secondary elements that can take the form of distance, orientation, or other relevant structural invariants. When using a distance-based construction in the presence or absence of encoded N to C terminal sense, there are strong correlations between the principle components of interaction matrices of structurally or topologically similar proteins. CONCLUSION: The PCC method is extensively tested for protein structures that belong to the same topological class but are significantly different by RMSD measure. The PCC analysis can also differentiate proteins having similar shapes but different topological arrangements. Additionally, we demonstrate that when using two independently defined interaction matrices, comparison of their maximum eigenvalues can be highly effective in clustering structurally or topologically similar proteins. We believe that the PCC analysis of interaction matrix is highly flexible in adopting various structural parameters for protein structure comparison.

Dataset Information

Detection of protein fold similarity based on correlation of amino acid properties.

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Detection of protein fold similarity based on correlation of amino acid properties.

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