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Predicting structurally conserved contacts for homologous proteins using sequence conservation filters.

ABSTRACT: The prediction of intramolecular contacts has a useful application in predicting the three-dimensional structures of proteins. The accuracy of the template-based contact prediction methods depends on the quality of the template structures. To reduce the false positive predictions associated with using the entire set of template-derived contacts, we develop selection filters that use sequence conservation information to predict subsets of contacts more likely to be structurally conserved between the template and the target. The method is developed specifically for protein families with few available templates such as the G protein-coupled receptor (GPCR) family. It is validated on a test set of 342 template-target pairs from three protein families, and applied to one template-target pair from the GPCR family. We find that the filter selection method increases the accuracy of contact prediction with sufficient coverage for structure prediction.

SUBMITTER: Michino M

PROVIDER: S-EPMC2740814 | biostudies-literature | 2009 Nov

REPOSITORIES: biostudies-literature

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Predicting structurally conserved contacts for homologous proteins using sequence conservation filters.

Michino Mayako M Brooks Charles L CL

Proteins 20091101 2

The prediction of intramolecular contacts has a useful application in predicting the three-dimensional structures of proteins. The accuracy of the template-based contact prediction methods depends on the quality of the template structures. To reduce the false positive predictions associated with using the entire set of template-derived contacts, we develop selection filters that use sequence conservation information to predict subsets of contacts more likely to be structurally conserved between ...[more]

PMID: 19475704

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Project description:Structure comparison tools can be used to align related protein structures to identify structurally conserved and variable regions and to infer functional and evolutionary relationships. While the conserved regions often superimpose well, the variable regions appear non superimposable. Differences in homologous protein structures are thought to be due to evolutionary plasticity to accommodate diverged sequences during evolution. One of the kinds of differences between 3-D structures of homologous proteins is rigid body displacement. A glaring example is not well superimposed equivalent regions of homologous proteins corresponding to ?-helical conformation with different spatial orientations. In a rigid body superimposition, these regions would appear variable although they may contain local similarity. Also, due to high spatial deviation in the variable region, one-to-one correspondence at the residue level cannot be determined accurately. Another kind of difference is conformational variability and the most common example is topologically equivalent loops of two homologues but with different conformations. In the current study, we present a refined view of the "structurally variable" regions which may contain local similarity obscured in global alignment of homologous protein structures. As structural alphabet is able to describe local structures of proteins precisely through Protein Blocks approach, conformational similarity has been identified in a substantial number of 'variable' regions in a large data set of protein structural alignments; optimal residue-residue equivalences could be achieved on the basis of Protein Blocks which led to improved local alignments. Also, through an example, we have demonstrated how the additional information on local backbone structures through protein blocks can aid in comparative modeling of a loop region. In addition, understanding on sequence-structure relationships can be enhanced through our approach. This has been illustrated through examples where the equivalent regions in homologous protein structures share sequence similarity to varied extent but do not preserve local structure.

Project description:BACKGROUND:Single-molecule microscopic experiments can measure the mechanical response of proteins to pulling forces applied externally along different directions (inducing different residue pairs in the proteins by uniaxial tension). This response to external forces away from equilibrium should in principle, correlate with the flexibility or stiffness of proteins in their folded states. Here, a simple topology-based atomistic anisotropic network model (ANM) is shown which captures the protein flexibility as a fundamental property that determines the collective dynamics and hence, the protein conformations in native state. METHODS:An all-atom ANM is used to define two measures of protein flexibility in the native state. One measure quantifies overall stiffness of the protein and the other one quantifies protein stiffness along a particular direction which is effectively the mechanical resistance of the protein towards external pulling force exerted along that direction. These measures are sensitive to the protein sequence and yields reliable values through computations of normal modes of the protein. RESULTS:ANM at an atomistic level (heavy atoms) explains the experimental (atomic force microscopy) observations viz., different mechanical stability of structurally similar but sequentially distinct proteins which, otherwise were implied to possess similar mechanical properties from analytical/theoretical coarse-grained (backbone only) models. The results are exclusively demonstrated for human fibronectin (FN) protein domains. CONCLUSIONS:The topology of interatomic contacts in the folded states of proteins essentially determines the native flexibility. The mechanical differences of topologically similar proteins are captured from a high-resolution (atomic level) ANM at a low computational cost. The relative trend in flexibility of such proteins is reflected in their stability differences that they exhibit while unfolding in atomic force microscopic (AFM) experiments.

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Predicting structurally conserved contacts for homologous proteins using sequence conservation filters.

Publications

Predicting structurally conserved contacts for homologous proteins using sequence conservation filters.

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