Dataset Information

Calmodulation meta-analysis: predicting calmodulin binding via canonical motif clustering.

ABSTRACT: The calcium-binding protein calmodulin (CaM) directly binds to membrane transport proteins to modulate their function in response to changes in intracellular calcium concentrations. Because CaM recognizes and binds to a wide variety of target sequences, identifying CaM-binding sites is difficult, requiring intensive sequence gazing and extensive biochemical analysis. Here, we describe a straightforward computational script that rapidly identifies canonical CaM-binding motifs within an amino acid sequence. Analysis of the target sequences from high resolution CaM-peptide structures using this script revealed that CaM often binds to sequences that have multiple overlapping canonical CaM-binding motifs. The addition of a positive charge discriminator to this meta-analysis resulted in a tool that identifies potential CaM-binding domains within a given sequence. To allow users to search for CaM-binding motifs within a protein of interest, perform the meta-analysis, and then compare the results to target peptide-CaM structures deposited in the Protein Data Bank, we created a website and online database. The availability of these tools and analyses will facilitate the design of CaM-related studies of ion channels and membrane transport proteins.

SUBMITTER: Mruk K

PROVIDER: S-EPMC4076516 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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Calmodulation meta-analysis: predicting calmodulin binding via canonical motif clustering.

Mruk Karen K Farley Brian M BM Ritacco Alan W AW Kobertz William R WR

The Journal of general physiology 20140616 1

The calcium-binding protein calmodulin (CaM) directly binds to membrane transport proteins to modulate their function in response to changes in intracellular calcium concentrations. Because CaM recognizes and binds to a wide variety of target sequences, identifying CaM-binding sites is difficult, requiring intensive sequence gazing and extensive biochemical analysis. Here, we describe a straightforward computational script that rapidly identifies canonical CaM-binding motifs within an amino acid ...[more]

PMID: 24935744

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Project description:Transcription factor (TF) proteins recognize a small number of DNA sequences with high specificity and control the expression of neighbouring genes. The evolution of TF binding preference has been the subject of a number of recent studies, in which generalized binding profiles have been introduced and used to improve the prediction of new target sites. Generalized profiles are generated by aligning and merging the individual profiles of related TFs. However, the distance metrics and alignment algorithms used to compare the binding profiles have not yet been fully explored or optimized. As a result, binding profiles depend on TF structural information and sometimes may ignore important distinctions between subfamilies. Prediction of the identity or the structural class of a protein that binds to a given DNA pattern will enhance the analysis of microarray and ChIP-chip data where frequently multiple putative targets of usually unknown TFs are predicted. Various comparison metrics and alignment algorithms are evaluated (a total of 105 combinations). We find that local alignments are generally better than global alignments at detecting eukaryotic DNA motif similarities, especially when combined with the sum of squared distances or Pearson's correlation coefficient comparison metrics. In addition, multiple-alignment strategies for binding profiles and tree-building methods are tested for their efficiency in constructing generalized binding models. A new method for automatic determination of the optimal number of clusters is developed and applied in the construction of a new set of familial binding profiles which improves upon TF classification accuracy. A software tool, STAMP, is developed to host all tested methods and make them publicly available. This work provides a high quality reference set of familial binding profiles and the first comprehensive platform for analysis of DNA profiles. Detecting similarities between DNA motifs is a key step in the comparative study of transcriptional regulation, and the work presented here will form the basis for tool and method development for future transcriptional modeling studies.

Dataset Information

Calmodulation meta-analysis: predicting calmodulin binding via canonical motif clustering.

Publications

Calmodulation meta-analysis: predicting calmodulin binding via canonical motif clustering.

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