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Qgrid: clustering tool for detecting charged and hydrophobic regions in proteins.

ABSTRACT: We have developed a simple but powerful method and web server to quickly locate charged and hydrophobic clusters in proteins (http://www.netasa.org/qgrid/index.html). For the charged clusters, each atom in the protein is first assigned a charge according to a standard force field. Then a box is created with dimensions corresponding to the range of atomic coordinates. This box is then divided into cubic grids of selected size, which now have one or more charged atoms in them. This leaves each grid with a certain amount of charge. Cubic grids with more than a cutoff charge are then clustered using a hierarchical clustering method based on Euclidean distance. A tree diagram made from the resulting clusters indicates the distribution of charged and hydrophobic regions of the protein. Hydrophobic clusters are developed by grouping the positions of C(alpha) atoms of such residues. We propose that such a tree representation will be helpful in detecting protein-protein interfaces, structure similarity and motif detection.

SUBMITTER: Ahmad S

PROVIDER: S-EPMC441501 | biostudies-literature | 2004 Jul

REPOSITORIES: biostudies-literature

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Qgrid: clustering tool for detecting charged and hydrophobic regions in proteins.

Ahmad Shandar S Sarai Akinori A

Nucleic acids research 20040701 Web Server issue

We have developed a simple but powerful method and web server to quickly locate charged and hydrophobic clusters in proteins (http://www.netasa.org/qgrid/index.html). For the charged clusters, each atom in the protein is first assigned a charge according to a standard force field. Then a box is created with dimensions corresponding to the range of atomic coordinates. This box is then divided into cubic grids of selected size, which now have one or more charged atoms in them. This leaves each gri ...[more]

PMID: 15215360

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Project description:BackgroundThe gene-specific sweep is a selection process where an advantageous mutation along with the nearby neutral sites in a gene region increases the frequency in the population. It has been demonstrated to play important roles in ecological differentiation or phenotypic divergence in microbial populations. Therefore, identifying gene-specific sweeps in microorganisms will not only provide insights into the evolutionary mechanisms, but also unravel potential genetic markers associated with biological phenotypes. However, current methods were mainly developed for detecting selective sweeps in eukaryotic data of sparse genotypes and are not readily applicable to prokaryotic data. Furthermore, some challenges have not been sufficiently addressed by the methods, such as the low spatial resolution of sweep regions and lack of consideration of the spatial distribution of mutations.ResultsWe proposed a novel gene-centric and spatial-aware approach for identifying gene-specific sweeps in prokaryotes and implemented it in a python tool SweepCluster. Our method searches for gene regions with a high level of spatial clustering of pre-selected polymorphisms in genotype datasets assuming a null distribution model of neutral selection. The pre-selection of polymorphisms is based on their genetic signatures, such as elevated population subdivision, excessive linkage disequilibrium, or significant phenotype association. Performance evaluation using simulation data showed that the sensitivity and specificity of the clustering algorithm in SweepCluster is above 90%. The application of SweepCluster in two real datasets from the bacteria Streptococcus pyogenes and Streptococcus suis showed that the impact of pre-selection was dramatic and significantly reduced the uninformative signals. We validated our method using the genotype data from Vibrio cyclitrophicus, the only available dataset of gene-specific sweeps in bacteria, and obtained a concordance rate of 78%. We noted that the concordance rate could be underestimated due to distinct reference genomes and clustering strategies. The application to the human genotype datasets showed that SweepCluster is also applicable to eukaryotic data and is able to recover 80% of a catalog of known sweep regions.ConclusionSweepCluster is applicable to a broad category of datasets. It will be valuable for detecting gene-specific sweeps in diverse genotypic data and provide novel insights on adaptive evolution.

Dataset Information

Qgrid: clustering tool for detecting charged and hydrophobic regions in proteins.

Publications

Qgrid: clustering tool for detecting charged and hydrophobic regions in proteins.

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