Similar Structures but Different Roles – An Updated Perspective on TLR Structures

Toll-like receptors (TLRs) are pattern recognition receptors that recognize conserved structures in pathogens, trigger innate immune responses, and prime antigen-specific adaptive immunity. Elucidation of crystal structures of TLRs interacting with their ligands such as TLR1-2 with triacylated lipopeptide, TLR2-6 with diacylated lipopeptide, TLR4–MD-2 with LPS, and TLR3 with double-stranded RNA (dsRNA) have enabled an understanding of the initiation of TLR signaling. Agonistic ligands such as LPS, dsRNA, and lipopeptides induce “m” shaped TLR dimers in which C-termini converge at the center. Such central convergence is necessary to bring the two intracellular receptor TIR domains closer together and promote their dimerization, which serves as an essential step in downstream signaling. In this review, we summarize TLR ECD structures that have been reported to date with special emphasis on ligand recognition and activation mechanism

Comparative Analysis of Species-Specific Ligand Recognition in Toll-Like Receptor 8 Signaling: A Hypothesis

Toll-like receptors (TLRs) play a central role in the innate immune response by recognizing conserved structural patterns in a variety of microbes. TLRs are classified into six families, of which TLR7 family members include TLR7, 8, and 9, which are localized to endolysosomal compartments recognizing viral infection in the form of foreign nucleic acids. In our current study, we focused on TLR8, which has been shown to recognize different types of ligands such as viral or bacterial ssRNA as well as small synthetic molecules. The primary sequences of rodent and non-rodent TLR8s are similar, but the antiviral compound (R848) that activates the TLR8 pathway is species-specific. Moreover, the factors underlying the receptor's species-specificity remain unknown. To this end, comparative homology modeling, molecular dynamics simulations refinement, automated docking and computational mutagenesis studies were employed to probe the intermolecular interactions between this anti-viral compound and TLR8. Furthermore, comparative analyses of modeled TLR8 (rodent and non-rodent) structures have shown that the variation mainly occurs at LRR14-15 (undefined region); hence, we hypothesized that this variation may be the primary reason for the exhibited species-specificity. Our hypothesis was further bolstered by our docking studies, which clearly showed that this undefined region was in close proximity to the ligand-binding site and thus may play a key role in ligand recognition. In addition, the interface between the ligand and TLR8s varied depending upon the amino acid charges, free energy of binding, and interaction surface. Therefore, our current work provides a hypothesis for previous in vivo studies in the context of TLR signaling

Structure-Function Relationship of Cytoplasmic and Nuclear IκB Proteins: An In Silico Analysis

Cytoplasmic IκB proteins are primary regulators that interact with NF-κB subunits in the cytoplasm of unstimulated cells. Upon stimulation, these IκB proteins are rapidly degraded, thus allowing NF-κB to translocate into the nucleus and activate the transcription of genes encoding various immune mediators. Subsequent to translocation, nuclear IκB proteins play an important role in the regulation of NF-κB transcriptional activity by acting either as activators or inhibitors. To date, molecular basis for the binding of IκBα, IκBβ and IκBζ along with their partners is known; however, the activation and inhibition mechanism of the remaining IκB (IκBNS, IκBε and Bcl-3) proteins remains elusive. Moreover, even though IκB proteins are structurally similar, it is difficult to determine the exact specificities of IκB proteins towards their respective binding partners. The three-dimensional structures of IκBNS, IκBζ and IκBε were modeled. Subsequently, we used an explicit solvent method to perform detailed molecular dynamic simulations of these proteins along with their known crystal structures (IκBα, IκBβ and Bcl-3) in order to investigate the flexibility of the ankyrin repeat domains (ARDs). Furthermore, the refined models of IκBNS, IκBε and Bcl-3 were used for multiple protein-protein docking studies for the identification of IκBNS-p50/p50, IκBε-p50/p65 and Bcl-3-p50/p50 complexes in order to study the structural basis of their activation and inhibition. The docking experiments revealed that IκBε masked the nuclear localization signal (NLS) of the p50/p65 subunits, thereby preventing its translocation into the nucleus. For the Bcl-3- and IκBNS-p50/p50 complexes, the results show that Bcl-3 mediated transcription through its transactivation domain (TAD) while IκBNS inhibited transcription due to its lack of a TAD, which is consistent with biochemical studies. Additionally, the numbers of identified flexible residues were equal in number among all IκB proteins, although they were not conserved. This could be the primary reason for their binding partner specificities

IN SILICO APPROACHES TO STRUCTURAL AND FUNCTIONAL ELUCIDATION OF INTRACELLULAR TLR SIGNALING

학위논문(박사)아주대학교 일반대학원 :분자과학기술학과,2013. 8Maste

A Molecular Dynamics Approach to Explore the Intramolecular Signal Transduction of PPAR-α

Dynamics and functions of the peroxisome proliferator-activated receptor (PPAR)-α are modulated by the types of ligands that bind to the orthosteric sites. While several X-ray crystal structures of PPAR-α have been determined in their agonist-bound forms, detailed structural information in their apo and antagonist-bound states are still lacking. To address these limitations, we apply unbiased molecular dynamics simulations to three different PPAR-α systems to determine their modulatory mechanisms. Herein, we performed hydrogen bond and essential dynamics analyses to identify the important residues involved in polar interactions and conformational structural variations, respectively. Furthermore, betweenness centrality network analysis was carried out to identify key residues for intramolecular signaling. The differences observed in the intramolecular signal flow between apo, agonist- and antagonist-bound forms of PPAR-α will be useful for calculating maps of information flow and identifying key residues crucial for signal transductions. The predictions derived from our analysis will be of great help to medicinal chemists in the design of effective PPAR-α modulators and additionally in understanding their regulation and signal transductions

STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction

Abstract Protein post-translational modification (PTM) is an important regulatory mechanism that plays a key role in both normal and disease states. Acetylation on lysine residues is one of the most potent PTMs owing to its critical role in cellular metabolism and regulatory processes. Identifying protein lysine acetylation (Kace) sites is a challenging task in bioinformatics. To date, several machine learning-based methods for the in silico identification of Kace sites have been developed. Of those, a few are prokaryotic species-specific. Despite their attractive advantages and performances, these methods have certain limitations. Therefore, this study proposes a novel predictor STALLION (STacking-based Predictor for ProkAryotic Lysine AcetyLatION), containing six prokaryotic species-specific models to identify Kace sites accurately. To extract crucial patterns around Kace sites, we employed 11 different encodings representing three different characteristics. Subsequently, a systematic and rigorous feature selection approach was employed to identify the optimal feature set independently for five tree-based ensemble algorithms and built their respective baseline model for each species. Finally, the predicted values from baseline models were utilized and trained with an appropriate classifier using the stacking strategy to develop STALLION. Comparative benchmarking experiments showed that STALLION significantly outperformed existing predictor on independent tests. To expedite direct accessibility to the STALLION models, a user-friendly online predictor was implemented, which is available at: http://thegleelab.org/STALLION.</jats:p