TollML: a database of toll-like receptor structural motifs

• Published in: Journal of molecular modeling Vol. 16; no. 7; pp. 1283 - 1289
• Main Authors: Gong, Jing, Wei, Tiandi, Zhang, Ning, Jamitzky, Ferdinand, Heckl, Wolfgang M., Rössle, Shaila C., Stark, Robert W.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01.07.2010; Springer Verlag (Germany)
• Subjects: Amino Acid Motifs; Amino Acid Sequence; Binding Sites; Characterization and Evaluation of Materials; Chemistry; Chemistry and Materials Science; Computer Appl. in Life Sciences; Computer Applications in Chemistry; Databases, Protein; Humans; Internet; Molecular Medicine; Molecular Sequence Data; Multigene Family; Original Paper; Protein Isoforms - chemistry; Protein Isoforms - genetics; Protein Isoforms - metabolism; Protein Structure, Tertiary; Repetitive Sequences, Amino Acid; Sequence Homology, Amino Acid; Theoretical and Computational Chemistry; Toll-Like Receptors - chemistry; Toll-Like Receptors - genetics; Toll-Like Receptors - metabolism; Leucine-rich repeats; XML database; Toll-like receptor; Homology modeling; TollML
• ISSN: 1610-2940; 0948-5023
• DOI: 10.1007/s00894-009-0640-9

Toll-like receptors (TLRs) play a key role in the innate immune system. TLRs recognize pathogen-associated molecular patterns and initiate an intracellular kinase cascade to induce an immediate defensive response. During recent years TLRs have become the focus of tremendous research interest. A central repository for the growing amount of relevant TLR sequence information has been created. Nevertheless, structural motifs of most sequenced TLR proteins, such as leucine-rich repeats (LRRs), are poorly annotated in the established databases. A database that organizes the structural motifs of TLRs could be useful for developing pattern recognition programs, structural modeling and understanding functional mechanisms of TLRs. We describe TollML, a database that integrates all of the TLR sequencing data from the NCBI protein database. Entries were first divided into TLR families (TLR1-23) and then semi-automatically subdivided into three levels of structural motif categories: (1) signal peptide (SP), ectodomain (ECD), transmembrane domain (TD) and Toll/IL-1 receptor (TIR) domain of each TLR; (2) LRRs of each ECD; (3) highly conserved segment (HCS), variable segment (VS) and insertions of each LRR. These categories can be searched quickly using an easy-to-use web interface and dynamically displayed by graphics. Additionally, all entries have hyperlinks to various sources including NCBI, Swiss-Prot, PDB, LRRML and PubMed in order to provide broad external information for users. The TollML database is available at http://tollml.lrz.de .