Databases and computational interaction models of Toll-like receptors

Toll-like receptors (TLRs) recognize pathogen-associated molecular
patterns (PAMPs) on invading organisms and are the first line of
defense in innate immunity. To date, much has been learned about
TLRs and their roles in autoimmune diseases are being unraveled.
The autoimmune disease systemic lupus erythematosus (SLE)
progresses as a consequence of the inappropriate recognition of
self nucleic acids by TLRs. For the development of therapeutic
approaches of SLE it is necessary to understand possible negative
regulation mechanisms of TLR. Single immunoglobulin interleukin-1
receptor-related molecule (SIGIRR) is the best characterized TLR
signaling inhibitor. It can interfere with the receptor complexes
and attenuate the recruitment of downstream adaptors to the
receptors. So far, the mechanisms of structural interactions
between SIGIRR, TLRs and adaptor molecules are unknown. To develop
a working hypothesis for these interactions, we constructed three-
dimensional models for these single molecules based on
computational predictions. Then, models of essential complexes
involved in the TLR signaling and the SIGIRR inhibiting processes
were yielded through protein-protein docking analysis. With the
high-throughput genome sequencing projects, a central repository
for the growing amount of TLR sequence information has been
created. However, subsequent annotations for these TLR sequences
are incomplete. For example, the indicated numbers and positions of
leucine-rich repeat (LRR) motifs contained in individual TLR
ectodomains are greatly distinct or missing in established
databases. In this vein, we have developed a database of TLR
structural motifs called TollML (http://tollml.lrz.de). It
integrates all TLR protein sequences that have been identified to
date. These sequences were semi-automatically partitioned into
three levels of structural motif categories. The manual motif
identification procedure provided TollML with the most complete and
accurate database of LRR motifs compared with other databases that
contain TLR data. LRR motifs are present not only in TLRs, but also
in many other proteins. To date, more than 6,000 LRR protein
sequences and more than 130 crystal structures of them have been
determined. This knowledge has increased our ability to use
individual LRR structures extracted from the crystal structures as
building blocks to model LRR proteins with unknown structures.
Because the individual LRR structures are not directly available
from any protein structure database, we have developed a
conformational LRR database called LRRML (http://lrrml.lrz.de). It
collects three- dimensional LRR structures manually identified from
all determined crystal structures of LRR-containing proteins and
thus provides a source for the structural modeling and analysis of
LRR proteins. With the help of TollML and LRRML, we constructed
models of the human/mouse TLR5-13 ectodomains and suggested some
potential receptor-ligand interaction residues based on these
models.