Dataset Information

Structural homology guided alignment of cysteine rich proteins.

ABSTRACT: BACKGROUND:Cysteine rich protein families are notoriously difficult to align due to low sequence identity and frequent insertions and deletions. RESULTS:Here we present an alignment method that ensures homologous cysteines align by assigning a unique 10 amino acid barcode to those identified as structurally homologous by the DALI webserver. The free inter-cysteine regions of the barcoded sequences can then be aligned using any standard algorithm. Finally the barcodes are replaced with the original columns to yield an alignment which requires the minimum of manual refinement. CONCLUSIONS:Using structural homology information to constrain sequence alignments allows the alignment of highly divergent, repetitive sequences that are poorly dealt with by existing algorithms. Tools are provided to perform this method online using the CysBar web-tool (http://CysBar.science.latrobe.edu.au) and offline (python script available from http://github.com/ts404/CysBar).

SUBMITTER: Shafee TM

PROVIDER: S-EPMC4709342 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Structural homology guided alignment of cysteine rich proteins.

Shafee Thomas M A TM Robinson Andrew J AJ van der Weerden Nicole N Anderson Marilyn A MA

SpringerPlus 20160112

<h4>Background</h4>Cysteine rich protein families are notoriously difficult to align due to low sequence identity and frequent insertions and deletions.<h4>Results</h4>Here we present an alignment method that ensures homologous cysteines align by assigning a unique 10 amino acid barcode to those identified as structurally homologous by the DALI webserver. The free inter-cysteine regions of the barcoded sequences can then be aligned using any standard algorithm. Finally the barcodes are replaced ...[more]

PMID: 26788439

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Project description:Peptide toxins isolated from animal venom secretions have proven to be useful pharmacological tools for probing the structure and function of a number of molecular receptors. Their molecular structures are stabilized by posttranslational formation of multiple disulfide bonds formed between sidechain thiols of cysteine residues, resulting in high thermal and chemical stability. Many of these peptides have been found to be potent modulators of ion channels, making them particularly influential in this field. Recently, several peptide toxins have been described that have an unusual tandem repeat organization, while also eliciting a unique pharmacological response toward ion channels. Most of these are two-domain peptide toxins from spider venoms, such as the double-knot toxin (DkTx), isolated from the Earth Tiger tarantula (Haplopelma schmidti). The unusual pharmacology of DkTx is its high avidity for its receptor (TRPV1), a property that has been attributed to a bivalent mode-of-action. DkTx has subsequently proven a powerful tool for elucidating the structural basis for the function of the TRPV1 channel. Interestingly, all tandem repeat peptides functionally characterized to date share this high avidity to their respective binding targets, suggesting they comprise an unrecognized structural class of peptides with unique structural features that result in a characteristic set of pharmacological properties. In this article, we explore the prevalence of this emerging class of peptides, which we have named Secreted, Cysteine-rich REpeat Peptides, or "SCREPs." To achieve this, we have employed data mining techniques to extract SCREP-like sequences from the UniProtKB database, yielding approximately sixty thousand candidates. These results indicate that SCREPs exist within a diverse range of species with greatly varying sizes and predicted fold types, and likely include peptides with novel structures and unique modes of action. We present our approach to mining this database for discovery of novel ion-channel modulators and discuss a number of "hits" as promising leads for further investigation. Our database of SCREPs thus constitutes a novel resource for biodiscovery and highlights the value of a data-driven approach to the identification of new bioactive pharmacological tools and therapeutic lead molecules.

Project description:In this study, we investigate the extent to which techniques for homology modeling that were developed for water-soluble proteins are appropriate for membrane proteins as well. To this end we present an assessment of current strategies for homology modeling of membrane proteins and introduce a benchmark data set of homologous membrane protein structures, called HOMEP. First, we use HOMEP to reveal the relationship between sequence identity and structural similarity in membrane proteins. This analysis indicates that homology modeling is at least as applicable to membrane proteins as it is to water-soluble proteins and that acceptable models (with C alpha-RMSD values to the native of 2 A or less in the transmembrane regions) may be obtained for template sequence identities of 30% or higher if an accurate alignment of the sequences is used. Second, we show that secondary-structure prediction algorithms that were developed for water-soluble proteins perform approximately as well for membrane proteins. Third, we provide a comparison of a set of commonly used sequence alignment algorithms as applied to membrane proteins. We find that high-accuracy alignments of membrane protein sequences can be obtained using state-of-the-art profile-to-profile methods that were developed for water-soluble proteins. Improvements are observed when weights derived from the secondary structure of the query and the template are used in the scoring of the alignment, a result which relies on the accuracy of the secondary-structure prediction of the query sequence. The most accurate alignments were obtained using template profiles constructed with the aid of structural alignments. In contrast, a simple sequence-to-sequence alignment algorithm, using a membrane protein-specific substitution matrix, shows no improvement in alignment accuracy. We suggest that profile-to-profile alignment methods should be adopted to maximize the accuracy of homology models of membrane proteins.

Dataset Information

Structural homology guided alignment of cysteine rich proteins.

Publications

Structural homology guided alignment of cysteine rich proteins.

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