The peptaibol database: a sequence and structure resource
Citations Over TimeTop 17% of 2003 papers
Abstract
The peptaibols are a large family of membrane-active peptides with considerable sequence homology, but with different biological properties and three-dimensional structures. They constitute a rich resource of naturally occurring 'mutants' which are potentially valuable for structure/function studies of ion channels. A searchable on-line database of sequences and structures of the peptaibols has been created at http://www.cryst.bbk.ac.uk/peptaibol, as a resource for the biological and structural community. In this paper, the contents and organization of the website are discussed as well as procedures for submission of new entries to the database. At present, more than 300 peptaibol sequences are stored in the database. Each sequence entry contains its full literature reference and information about its biological source. Tools are provided for searching for specific peptaibol sequences or groupings of sequences, and for locating peptaibols containing specified sequence motifs. In addition the website acts as a database for structural information. The coordinates of all currently available peptaibol x-ray and NMR structures are included and complemented, where appropriate. with molecular graphics illustrations. These include figures of model channel structures and comparisons between different peptaibol structures. The peptaibol database thus provides a tool for ready access to information and a means of investigating the sequences and structures of this class of polypeptides.
Related Papers
- → The PIR-International Protein Sequence Database(1999)246 cited
- → NrichD database: sequence databases enriched with computationally designed protein-like sequences aid in remote homology detection(2014)11 cited
- → Using homology relations within a database markedly boosts protein sequence similarity search(2015)8 cited
- → Sequential Sequence Mining Technique in Large Database of Gene Sequence(2010)2 cited
- Algorithm for Efficiently Mining Frequent Closed Sequence(2006)