Dataset Information

Functional and genomic analyses of alpha-solenoid proteins.

ABSTRACT: Alpha-solenoids are flexible protein structural domains formed by ensembles of alpha-helical repeats (Armadillo and HEAT repeats among others). While homology can be used to detect many of these repeats, some alpha-solenoids have very little sequence homology to proteins of known structure and we expect that many remain undetected. We previously developed a method for detection of alpha-helical repeats based on a neural network trained on a dataset of protein structures. Here we improved the detection algorithm and updated the training dataset using recently solved structures of alpha-solenoids. Unexpectedly, we identified occurrences of alpha-solenoids in solved protein structures that escaped attention, for example within the core of the catalytic subunit of PI3KC. Our results expand the current set of known alpha-solenoids. Application of our tool to the protein universe allowed us to detect their significant enrichment in proteins interacting with many proteins, confirming that alpha-solenoids are generally involved in protein-protein interactions. We then studied the taxonomic distribution of alpha-solenoids to discuss an evolutionary scenario for the emergence of this type of domain, speculating that alpha-solenoids have emerged in multiple taxa in independent events by convergent evolution. We observe a higher rate of alpha-solenoids in eukaryotic genomes and in some prokaryotic families, such as Cyanobacteria and Planctomycetes, which could be associated to increased cellular complexity. The method is available at http://cbdm.mdc-berlin.de/~ard2/.

SUBMITTER: Fournier D

PROVIDER: S-EPMC3837014 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Functional and genomic analyses of alpha-solenoid proteins.

Fournier David D Palidwor Gareth A GA Shcherbinin Sergey S Szengel Angelika A Schaefer Martin H MH Perez-Iratxeta Carol C Andrade-Navarro Miguel A MA

PloS one 20131121 11

Alpha-solenoids are flexible protein structural domains formed by ensembles of alpha-helical repeats (Armadillo and HEAT repeats among others). While homology can be used to detect many of these repeats, some alpha-solenoids have very little sequence homology to proteins of known structure and we expect that many remain undetected. We previously developed a method for detection of alpha-helical repeats based on a neural network trained on a dataset of protein structures. Here we improved the det ...[more]

PMID: 24278209

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Project description:MOTIVATION: Solenoid proteins are emerging as a protein class with properties intermediate between structured and intrinsically unstructured proteins. Containing repeating structural units, solenoid proteins are expected to share sequence similarities. However, in many cases, the sequence similarities are weak and non-detectable. Moreover, solenoids can be degenerated and widely vary in the number of units. So that it is difficult to detect them. Recently, several solenoid repeats detection methods have been proposed, such as self-alignment of the sequence, spectral analysis and discrete Fourier transform of sequence. Although these methods have shown good performance on certain data sets, they often fail to detect repeats with weak similarities. In this article, we propose a new approach to recognize solenoid repeats and non-solenoid proteins using stationary wavelet packet transform (SWPT). Our method associates with three advantages: (i) naturally representing five main factors of protein structure and properties by wavelet analysis technique; (ii) extracting novel wavelet features that can capture hidden components from solenoid sequence similarities and distinguish them from global proteins; (iii) obtaining statistics features that capture repeating motifs of solenoid proteins. RESULTS: Our method analyzes the characteristics of amino acid sequence in both spectral and temporal domains using SWPT. Both global and local information of proteins are captured by SWPT coefficients. We obtain and integrate wavelet-based features and statistics-based features of amino acid sequence to improve the classification task. Our proposed method is evaluated by comparing to state-of-the-art methods such as HHrepID and REPETITA. The experimental results show that our algorithm consistently outperforms them in areas under ROC curve. At the same false positive rate, the sensitivity of our WAVELET method is higher than other methods. AVAILABILITY: http://www.naaan.org/anvo/Software/Software.htm.

Dataset Information

Functional and genomic analyses of alpha-solenoid proteins.

Publications

Functional and genomic analyses of alpha-solenoid proteins.

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