bioRxivBioinformaticsPreprint
Extended insight into the preponderance of LRRs - a docking & simulation study with members of the TLR1 subfamily
Debayan DeyiD, Dipanjana DhariD, Sucharita DasiD, Aditi MaulikiD, Soumalee BasuiD
Abstract
The widespread structural motif of Leucine-rich repeats (LRR) constitute the extracellular part of the Toll-like receptor (TLR) family preceded by an intracellular Toll/interleukin-1 receptor (TIR) domain at the C-terminus. The benefit of using LRRs in these pattern recognition receptors (PRR) that are responsible for early detection of pathogens to elicit inflammatory/innate immune response still remains elusive. Phylogenetic analyses (Maximum Likelihood and Bayesian Inference) of nine TLR (TLR 1-9) genes from 36 mammals reconfirmed the existence of two distinct clades, one (TLR1/2/6) for recognizing bacterial cell wall derivatives and another (TLR7/8/9) for various nucleic acids. TLR3, TLR4 and TLR5 showed independent line of evolution. The distinction of the TLR1 subfamily to form heterodimers within its members and the existence of the paralogs TLR1 and TLR6 therein, was appealing enough to carry out further studies with the extracellular recognition domain. Dimerizing and ligand binding residues from the crystal structures of TLR1 and TLR6 were interchanged to generate chimeric proteins. The dimer forming ability of these variants with their common partner, TLR2, were checked before running MD simulations. The chimeras were compared with wild type dimers to find no significant alterations in the overall structure. Finally, interchanged ligands were docked to the variants to ratify reversal of the binding function. Intriguingly, sequence change in substantial numbers, 16 in TLR1 and 18 in TLR6, preserves the native scaffold offered by LRRs. This exercise thus depicts how the LRR motif has been advantageous to be selected as an evolutionarily conserved motif for essential cellular processes.
- organism: multiple species
- assay: protein structure
- research focus: protein structure prediction
- approach: statistical model
- approach: simulation
- Citations
- 0
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