In Silico Study Of Heterodimerization Of Tlr2 And Tlr6

In silico study of Heterodimerization of TLR2 and TLR6

Er. RAGHVENDRA SACHAN

1

CONTENTS

  

List of abbreviation List of figures List of tables

Chapter No.

Particulars

Page No.

1.

Introduction

9

2.

Review of Literature

15

3.

Materials and Methods

26

4.

Results and Discussion

34

5.

Conclusion

51

6.

References

52

2

ABBREVIATIONS

3D

3-dimensional

APC

Antigen Presenting Cell

AP1

Activating protein-1

BLAST

Basic Local Alignment Search Tool

DC

Dendritic Cell

ExPASy

Expert Protein Analysis System

GN

Gram-negative

GPI

Glycosylphosphatidyllinositol

IFN

Interferon

Ig

Immunoglobulin

IKK

I B kinase

IL

Interleukin

IRAK

Interleukin 1 receptor-associated kinase

IRF

Interferon regulatory factor

LP

Lipoprotein

LPS

Lipopolysaccharide

MAL

MyD88 adapter like

MAPK

Mitogen-activated protein kinase

NF- B

Nuclear factor- B

3

NK

Natural killer

NMR

Nuclear Magnetic Resonance

PAMP

Pathogen associated molecular pattern

PDB

Protein Data Bank

PDG

Peptidoglycan

PG

peptidoglycan

PRR

Pattern recognition receptor

SAVS

Structural Analysis and Validation Server

TAB

TAK1-binding protein

TAK1

Transforming growth factor beta-activated kinase 1

TH

T-helper

TICAM

TIR domain-containing adapter molecule

TIR

Toll-IL-1 receptor

TIRAP

Toll-IL-1 receptor domain-containing adapter protein

TLR

Toll-like receptor

TNF

Tumor necrosis factor

TRAF6

Tumor necrosis factor receptor associated factor 6

TRAM

TRIF-related adapter molecule

TRIF

Toll-IL-1 receptor domain-containing adapter-inducing interferon-β

4

LIST OF FIGURES

Figure No.

Particulars

Page No.

Figure 2.1

Toll-like receptors: Structure and Heterodimerisation.

6

Figure 2.2

TLRs and their Ligands.

8

Figure 2.3

TLRs Distribution on Dendritic Cells.

11

Figure 2.4

TLR 2, 6 Signaling Pathway.

15

Figure 2.5

TH Cell Proliferation activated by TLR.

17

Figure 4.1

Structure of TLR2 predicted by using SWISS MODEL server.

26

Figure 4.2

Structure of TIR domain of TLR2 predicted by using SWISS

27

MODEL server.

Figure 4.3

Structure of TLR6 predicted by using SWISS MODEL server.

28

Figure 4.5

Structure of TIR domain of TLR6 predicted by using SWISS

29

MODEL server.

Figure 4.6

Ramachandran Plot of TLR2.

30

Figure 4.7

Ramachandran Plot of TIR domain of TLR2.

30

Figure 4.8

Ramachandran Plot of TLR6.

31

5

Figure 4.9

Ramachandran Plot of TIR domain of TLR6.

31

Figure 4.10

Interaction between TLR2 and TLR6 after Protein-Protein

35

Docking.

Figure 4.11

Regions of interactions between TLR2 and TLR6.

36

Figure 4.12

Amino acid residues involved in interactions between TLR2 and

37

TLR6.

Figure 4.13

Interaction between TIR domains of TLR2 and TLR6 after

39

Protein-Protein Docking.

Figure 4.14

Region of interaction between TIR domains of TLR2 and TLR6.

6

40

LIST OF TABLES

Table No.

Particulars

Page No.

Table 2.1

Toll-like receptors and their characteristics.

9

Table 4.1

Verify-3D results.

34

Table 4.2

Docking correlation summary of TLR2/TLR6.

36

Table 4.3

Amino Acid residues involved in interaction between TLR2 and

38

TLR6.

Table 4.4

Docking correlation summary of TIR domains of TLR2 and

40

TLR6.

Table 4.5

Amino Acid residues involved in interaction between TIR domains of TLR2 & TLR6.

7

40

ABSTRACT Toll-like Receptors (TLRs) not only recognize pathogens but also, upon ligand binding, initiate a cascade of cellular signaling that direct the subsequent immune responses. TLR6 has been shown to be required for the recognition of diacylated lipoproteins and lipopeptides derived from Mycoplasma and to activate the NF- B signaling cascade in conjunction with TLR2. In this study, we have produced a heterodimer of TLR2/TLR6 and a TIR-TIR platform formed by heterodimerization of TLR2/TLR6 by using computer assisted homology modelling and protein-protein docking. We have done a detailed analysis of selected TLR2 and TLR6 docked complex and TIR-TIR docked complex to find out the interacting amino acid residues between both the molecules. We have found that heterodimerization of TLR2 with TLR6 is an evolutionary process which enhance the ligand recognition capacity to enable the innate immune system. TLR2 and TLR6 are interacting with each other at three points and TIR domains of both TLR2 and TLR6 are interacting at one point. These interactions are very crucial for activation of pro- and anti-inflammatory cytokine cascade and T cell proliferation to stimulate immune responses.

8

Chapter 1

INTRODUCTION 1.1 Signal Transduction and Toll-Like Receptor Signal transduction refers to a process by which a cell converts one kind of signal or stimulus into another. Most processes of signal transduction involve ordered sequences of biochemical reactions inside the cell, which are carried out by enzymes, activated by second messengers, resulting in a signal transduction pathway. Such processes are usually rapid, lasting in the order of milliseconds in the case of ion flux, minutes for the activation of protein- and lipid-mediated kinase cascades, or hours and even days for gene expression. The number of proteins and other molecules participating in the events involving signal transduction increases as the process emanates from the initial stimulus, resulting in a "signal cascade," beginning with a relatively small stimulus that elicits a large response. This is referred to as amplification of the signal. Vertebrate immunity can be broadly categorized into adaptive and innate immunity (Janeway et al., 2002). Adaptive immune responses are mediated by clonally distributed B and T lymphocytes and are characterized by specificity and memory. Recognition relies on the generation of a random and highly diverse repertoire of antigen receptors, the T- and B-cell receptors, followed by clonal selection and expansion of receptors with relevant specificities (Janssens and Beyaert, 2003). This mechanism accounts for the generation of immunological memory, an important advantage, but has the main limitation that specific clones need to expand and differentiate into effector cells before they can participate in host defense. Therefore, adaptive immune responses are typically delayed for 4 to 7 days (Janeway et al., 2002). To control the infection during the first days, our body relies on the evolutionarily ancient and more universal innate immune system. Its main functions include opsonization, activation of complement and coagulation cascades, phagocytosis, activation of proinflammatory signaling cascades, and apoptosis (Medzhitov, 2001). The innate immune system also has an important function in activation and shaping of the adaptive immune response through the induction of co-stimulatory molecules and cytokines (Medzhitov and Janeway, 1997). In contrast to the 9

clonotypic receptors, expressed by B and T lymphocytes, the innate immune system uses nonclonal sets of recognition molecules, called Pattern Recognition Receptors (PRRs). Pattern recognition receptors bind conserved molecular structures found in large groups of pathogens, termed Pathogen-Associated Molecular Patterns (PAMP) (Medzhitov and Janeway, 1997). There are various groups of pattern recognition receptors, which can be secreted, expressed on the cell surface, or resident in intracellular compartments (Medzhitov, 2001). The Toll-like receptors (TLRs) are one of the most important pattern recognition receptor families. Toll-like receptors are a recently discovered family of cell surface receptors that have received considerable attention because they are helping to unravel the details of the immune response to infection. They are key components of the innate immune response, the arm of the immune system that provides a rapid frontline attack against organisms to contain infection while the adaptive arm generates an antigen-specific response. It is now appreciated that through Tolllike receptors, the innate immune system demonstrates substantial specificity and controls activation of the adaptive immune system with precision (Kang et al., 2006). The TIR-TIR platform formed by the dimerization of TLR2 and TLR6 promotes homotypic protein-protein interactions with additional cytoplasmic adapter molecules to form an active signaling complex resulting in the expression of pro- and anti-inflammatory cytokine genes.

1.2 Toll-Like Receptor and Fungal Infection The incidences of fungal infections are increasing at an alarming rate, presenting an enormous challenge to healthcare professional. The increase is directly related to the growing population of immunocompromised individuals, resulting from changes in medical practice such as the use of intensive chemotherapy and immunosuppressive drugs. There are two types of fungal infection i.e. Superficial and subcutaneous fungal infection and Systematic fungal infection Historical clinical observations suggested that cellular immunity is central outcome of deep fungal infections, and experimental observations later proved this. Adaptive immunity is influenced by the generation of TH (helper T cell) cell and continuous production of proinflamatory cytokine Naїve TH cell when stimulated with antigen captured by antigen presenting cell (APC) differentiated into two T cell subset i.e. TH1, TH2. TH1 cell secrete IFN-γ

10

and mainly promote cellular immunity whereas TH2 cell produce mainly IL-4, IL-5, IL-10 and also IL-12 drives TH1 differentiation and IL-4 drives TH2 differentiation (Barton et al., 2002; Trinchieri, 2003, Iwasaki and Medzhitov, 2004). The balance between TH1 TH2 determines the onset and outcome of allergic as well as autoimmune disease. A tilt towards type1 T-helper cell pathway seems essential in antifungal host defenses. It has been reported that TLR-2/6 recognizes some components of Zymosan of yeast which result in production of cytokines and chemokines. Recent studies have demonstrated a crucial involvement of TLRs in the recognition of fungal pathogens such as Candida albicans, Aspergillus fumigatus, and Cryptococcus neoformans (Netea et al., 2004). Through the study of fungal infection in knock-out mice deficient in either TLRs or TLR-associated adaptor molecules, it became apparent that specific TLRs such as TLR2 and TLR6 play differential roles in the activation of the various arms of the innate immune response. Recent data also suggest that TLRs offer escape mechanisms to certain pathogenic microorganisms, especially through TLR2-driven induction of anti-inflammatory cytokines. These new data have substantially increased our knowledge of the recognition of fungal pathogens, and the study of TLRs remains one of the most active areas of research in the field of fungal infections.

1.3 Bioinformatics Evolution of Biology as a spectrum science, with the improvement in research and development, has resulted in unprecedented rate of data output from millions of research facilities the world over. Computing has not only greatly eased but has also innovatively achieved a greater comprehension of this data surge with Bioinformatics as the master-tool. Bioinformatics is a form of computing, with the use of pre-programmed software, to achieve comprehendible goals from data repositories in the exponentially increasing data intensive fields of molecular biology, genomics and drug discovery. Thus, Bioinformatics has emerged as the science devoted to the management and analysis of data for molecular biology, using advanced computing techniques.

11

1.4 Homology Modelling Homology modeling, also termed comparative modelling or knowledge-based modelling, develops a three-dimensional model from a protein sequence based on the structures of homologous proteins. Almost all homology modeling techniques rely on the identification of one or more known protein structures likely to resemble the structure of the query sequence and on the production of an alignment that maps residues in the query sequence to residues in the template sequence. The sequence alignment and template structure are then used to produce a structural model of the target. This approach to structure prediction is possible because a small change in the protein sequence usually results in a small change in its 3D structure. Because protein structures are more conserved than protein sequences, detectable levels of sequence similarity usually imply significant structural similarity (Blundell et al., 1987).

1.5 Docking Molecular docking is a study of how two or more molecular structures, for example drug and enzyme or receptor of protein, fit or interact together in 3D space. If the three-dimensional structure of the target protein is known, docking algorithm can be applied to virtually search for potential leads. The two main questions arising are what the complex between a protein and a potential leads looks like and how strong the binding affinity of the lead is with respect to other candidates (Rarey, 2002). Protein-Protein Docking is also known as the molecular docking technique. Protein receptor-ligand docking is used to check the structure, position and orientation of a protein when it interacts with small molecules like ligands.

1.6 Objective 1. To predict the 3-D structures of protein of TLR2, TLR6 and TIR domains of both TLR2 and TLR6 by Homology modelling. 2. To find out the amino acid residues involved in Heterodimerisation of TLR2/TLR6 and in

the TIR-TIR platform formed by Heterodimerisation of TLR2/TLR6 by protein-protein docking.

12

Chapter 2

REVIEW OF LITERATURE Recognition of pathogen-associated molecular signatures is critically important in proper activation of the immune system. The Toll-like receptor (TLR) signaling network is responsible for innate immune response. In human, 10 TLRs recognize a variety of ligands from pathogens to trigger immunological responses. (Medzhitov et al., 1997; Chaudhary et al., 1998; Rock et al., 1998; Takeuchi et al., 1999; Du et al., 2000; Chuang et al., 2001) TLRs activate NKB and other signaling pathways, which results in the secretion of various inflammatory cytokines. 2.1 From Toll Receptors to Toll-Like Receptors The first member of the TLR family identified was a Drosophila protein implicated in dorsoventral patterning during embryonal development (Hashimoto et al., 1988). Gay and Keith were the first to realize that the intracellular domain of Drosophila Toll showed striking similarities to the intracellular domain of the mammalian interleukin-1 (IL-1) receptor, and Lemaitre et al. demonstrated that Drosophila Toll was also involved in the immune response of the adult fly. Different human homologues of Drosophila Toll were identified and shown to induce activation of the transcription factor nuclear factor- B (NF- B) upon overexpression, revealing that TLRs and IL-1 receptors trigger similar signal transduction cascades (Medzhitov et al., 1997; Rock et al., 1998). In 1997, Janeway et al. discovered the first human homologue of the drosophila Toll receptor, now known as Toll-like receptor 4. It contained the Toll-like receptor/IL-1 receptor intracytoplastic domain (Slack et al., 2000), but instead of an immunoglobulin (Ig) extracellular domain like the IL-1 receptor, it showed a structure similar to that of the fly receptor, composed of leucine-rich repeats. This similarity represented the ancient strategy of pattern recognition receptors conserved throughout evolution and utilized by both human beings and insects. In 1998, Poltorak et al. discovered by positional cloning that the lps gene in the lipopolysaccharide (LPS)-nonresponsive mouse strain CH3/HeJ encoded a murine member of the TLR family, providing the first clue of a function as pattern recognition receptors for 13

mammalian TLRs. Once Toll-like receptors were discovered to recognize pathogen-associated molecular patterns, they became the most important group of pattern recognition receptors in the innate immune system.

2.2 Structural importance of Toll-IL-1 receptor (TIR) homology domain The structure of the Toll-like receptor has now been well characterized and has provided useful information about the downstream cellular signaling that occurs after ligand binding (Kang et al., 1996) (Figure 2.1). Toll-like receptors are transmembrane proteins with a series of leucinerich repeats in the N-terminal extracellular domain and a cytoplasmic portion greatly similar in structure to that of IL-1 receptor (Bowie and O’Neill, 2000). This intracellular region is hence referred to as the Toll-IL-1 receptor homology domain (Slack et al., 2000). The Toll-IL-1 receptor motif is also found in a number of important adaptor proteins that recruit downstream kinases and transcription factors, such as myeloid differentiation factor 88, Toll-IL-1 receptor domain containing adaptor protein, and Toll-IL-1 receptor domain containing adaptor inducing interferon-β (IFN-β) (Kang et al., 1996).

Fungi Extracellular Leucine rich repeats

TLR2

TLR6

Transmembrane Domain TIR domain (Highly conserved cytoplasmic domain) Intracellular SIGNALING PATHWAY

TLR Family Structure

Functional Interaction between Receptors

Figure 2.1 Toll-Like Receptors: Structure and Heterodimerisation

14

It is the physical interaction between the Toll-IL-1 receptor domains of the Toll-like receptor and the adaptor proteins that form a structural platform on which other downstream signaling molecules dock to initiate the signaling cascade. This area is so crucial that even single point mutations in the Toll-IL-1 receptor domain have been shown to abolish the host immune response to pathogen-associated molecular pattern stimulation of some Toll-like receptors (Kang et al., 1996). Although the exact nature of the physical interaction between the Toll-like receptor Toll-IL-1 receptor domain and adaptor proteins has not been fully clarified, structure and function studies have provided important information regarding the molecular basis of Toll-IL-1 receptor signaling. A large, conserved area containing a special configuration of amino acids called the „„BB loop‟‟ was shown to protrude away from the rest of the Toll-IL-1 receptor domain that may mediate interactions with downstream adaptor molecules (Xu et al., 2000). Also, evidence suggests that multiple signaling pathways are dependent on common Toll-IL-1 receptor residues, and the differential outcomes of Toll-like receptor activation probably reflect diverging signaling pathways downstream of the Toll-IL-1 receptor domain (Ronni et al., 2003). It is uncertain whether these structurally critical areas participate in the oligomerization of the Toll-like receptors, in the interactions with adaptor proteins, or with the subsequently recruited kinases. Attempts to isolate the critical amino acid residues within the Toll-IL-1 receptor domain involved in Toll-like receptor signaling continue.

2.3 Characteristics of Toll-Like Receptor The toll-like receptor (TLR) signaling pathway is the front-line subsystem against invasive microorganisms for both innate and adaptive immunity (Iwasaki and Medzhitov, 2004). To sense innumerable and various pathogenic threats, TLRs have evolved to recognize pathogenassociated molecular patterns (PAMPs), which represent molecular features on the surface of pathogens. The TLR gene family and their pathways have been evolutionarily well conserved in both invertebrates and vertebrates (Hoffmann and Reichhart, 2002; Roach et al., 2005). Each TLR binds to a variety of PAMPs that work as molecular markers of potential pathogens that the host shall be defended against. For example, TLR4 was found to be a receptor for

15

lipolysaccharide (LPS) and essential to generate responses to Gram-negative bacteria in which LPS is a part of the outer membrane (Poltorak et al., 1998), TLR9 responds to DNAcontaining unmethylated CpG motifs (Hemmi et al., 2000), TLR3 is activated by doublestranded RNA (Alexopoulou et al., 2001), and bacteria flagellin activates TLR5 (Hayashi et al., 2001). TLRs and interleukin 1 receptors (IL-1Rs) have a conserved region of amino acids, which is known as the toll/IL-1R (TIR) domain (Slack et al., 2000). Signaling of the TLR/IL-1R superfamily is mediated through myeloid differentiation primary response gene 88 (MyD88), IL-1R-associated kinases (IRAKs), transforming growth factor beta-activated kinase 1 (TAK1), TAK1-binding protein 1 (TAB1), TAB2, tumor necrosis factor (TNF) receptor-associated factor 6 (TRAF6), etc. (Akira and Takeda, 2004). It should be mentioned that TLR1, TLR2, TLR6, TLR4, and TLR5 are located on the plasma membrane, whereas TLR3, TLR7, and TLR9 are not located on the cell surface (Akira and Takeda, 2004). While ligands for each TLR and interactions downstream of receptors are now being identified at a dramatic pace, doubt is now being cast on the global logic behind all TLR pathways. It was argued that the TLR pathway forms an hourglass structure (Beutler, 2004), but the precise shape of the global TLR signaling network and its functional implications has not been elucidated. Since TLRs activate innate immunity and influence the nature of adaptive immunity (Hoebe et al., 2004), understanding the logic behind TLR signaling is the most important topic in immunology.

Triacylated Lipoprotein

Lipoprotein Zymosan GPI Anchors

Double stranded RNA

Imidazoquinolines ss RNA Flagellin CpG DNA

LPS

MD2 CD 14

TLR1

TLR2 TLR2

TLR6 TLR3

TLR4

TLR5

Figure 2.2 TLRs and their Ligands

16

TLR7

TLR9

Table 2.1 Toll-like receptors and their characteristics (Kang et al., 2006)

Toll-like receptor

Endogenous ligands

Exogenous ligands

Cytokines and effector molecules induced

Proposed effector functions

TLR1

None identified

Tri-acylated LP Mycobacterial 19-kd LP (TLR2/TLR1)

TNF-α IL-12

TLR2

HSPgp96 HSP60 HSP70

TNF-α, IL-1b IL-6 IL-8 IL-10 IL-12 NO12 IL-4, IL-5, IL-6, IL-13 (mast cells)

TLR3

None identified

PG of gram-positive bacteria Acylated Lipoprotein Zymosan of yeast Lipoteichoic acid GPI anchors (Trypanosoma cruzi) Outer membrane protein A (Klebsiella pneumoniae) LAM (Mycobacteria) Mycobacterial 19-kd LP Double-stranded RNA

Defense against Mycobacteria and other organisms expressing triacylated LP Defense against various gram-positive bacteria, Mycobacteria, Mycoplasma, protozoa, and fungi Activation of respiratory burst Induction of apoptosis. Mast cell activation and degranulation

IFN-β

Antiviral defense

TLR4

HSPgp96 HSP60 HSP70 β-defensin 2 Fibrinogen None identified

TNF-α, IFN-β IL-1, IL-6,IL-10, IL-13, Macrophage Inflammatory protein-1a/b TNF-α,IL-1b,IL-6 IL-10, IFN-γ

Defense against various gram-negative bacteria, fungi, and viruses Induction of apoptosis

TLR5

LPS F protein of RSV1 Escherichia coli P fimbriae Mouse mammary tumor virus envelope proteins Flagellin

TLR6

None identified

TNF-α

TLR7

Single-stranded RNA (influenza Virus Single-stranded RNA (HIV-1)

Di-acylated LP (Mycoplasma) Zymosan of yeast GPI anchors (T cruzi) Midazoquinolines (Imiquimod, Resiquimod) Loxoribine, Bropirimine

TLR8

Single-stranded RNA (HIV-1)

TLR9

Chromatin-IgG complexes

TLR10

None identified

Imidazoquinolines (Imiquimod, Resiquimod) Unmethylated Cytidineguanine DNA Live or inactivated Herpes simplex virus None identified

17

Defense against flagellated bacteria DC maturation Defense against bacteria, fungi, mycoplasma, and protozoa

IFN-α (plasmacytoid DCs) IFN-γ (T cells) IFN-β, TNF-α, IL-1,IL-6, IL-8 IL-12, IL-18 Similar to TLR7

Antiviral and antitumor defense DC maturation Activation and migration of Langerhans cells from skin to lymph nodes TH1 development Similar to TLR7

IFN-α (plasmacytoid DCs) IFN-β ,IFN-γ (NK cells), IL-6,IL-12 Unknown

Antibacterial and antiviral defense TH1 development B cell proliferation DC maturation Unknown

2.4 Receptors of the Adaptive versus those of the Innate Immune System To appreciate the significance of Toll-like receptors and their pivotal role in immunology, it is helpful to review the differences between adaptive and innate immunity (Kang et al., 1996). The innate immune system is charged with curbing the proliferation of an invading pathogen during the initial stages of an infection, before the lymphocyte expansion that characterizes the adaptive immune response. The hallmark of the innate immune system is the rapidity with which it responds to microbes and orchestrates the appropriate cellular response to defend the host. For this response to occur, a foreign organism must be quickly recognized and identified as a threat. At the heart of innate immunity is professional antigen presenting cells (APCs), such as macrophages and dendritic cells (DCs) that provide continual surveillance of the environment and are prepared to rapidly alert other vital components of the immune system to perilous substances. Instead of differentiating the countless potential microorganisms, APCs recognize patterns that are common and indispensable among pathogen classes, termed „„pathogen-associated molecular patterns.‟‟ A classic example of a PAMP is the lipopolysaccharide (LPS) of gramnegative (GN) bacteria, which serves as a vital structural component of the cell wall and is found across all GN bacterial species. The receptors that recognize PAMPs are known as pattern-recognition receptors and are found both on cellular membranes and as circulating plasma proteins. These receptors are germ-line encoded, that is, they rely on inherited genetic material to provide the different receptor specificities. It is estimated that the receptors of the innate immune system number only in the hundreds and each type of receptor is identical for each individual (Medzhitov and Janeway, 2000). Thus, the recognition of molecular patterns that are vital and common to large groups of pathogenic organisms confers an evolutionary advantage to offspring and is an efficient use of a finite genome. During maturation, random combinations of genetic material create an incredibly large repertoire of receptors, endowing each lymphocyte with a unique receptor. The differences in the way the receptors of the adaptive and innate immune response are genetically engineered are complementary: the variability of the adaptive immune response is much more significant, on the order of 1018 potential lymphocyte receptors per individual, yet subsequent generations

18

must reinvent their own defense (Kang et al., 1996). The innate immune system has a limited variety of highly evolved receptors that have been retained through generations. Also contributing to the complementary functions are the differences in onset of action. In the adaptive immune response, time is required for the selected cell to clonally proliferate and mature into a fully functional effector cell, while cells of the innate immune system are able immediately to mount an effective immune response. Toll-like receptors represent a class of membrane bound pattern recognition receptors that not only recognize common pathogens but also, upon ligand binding, initiate a cascade of cellular signaling that directs the subsequent immune responses.

2.5 TLRs Distribution on Dendritic Cells DC maturation, which is mediated by TLR family signaling, is a critical link between innate and adaptive immunity (Iwasaki et al., 2004).

Figure 2.3 TLRs Distribution on Dendritic Cells

(Iwasaki et al., 2004)

2.6 Toll-Like Receptor Signal Transduction The TLR signaling through different intracellular molecules, such as MAP kinases and IκB kinases which are conserved signaling elements for many receptors, leads to a distinct set of proinflammatory gene expressions (Jayalakshmi et al., 2007).

2.6.1 TLR mediated MyD88-dependent and independent cellular signaling The signaling pathways activated by TLRs are broadly classified into MyD88-dependent and independent pathways (Takeda and Akira, 2005) as MyD88 is the universal adapter protein recruited by all TLRs except TLR3. The major pathways activated by TLR engagement are 19

passed through I B kinase (IKK), MAPK and phosphatidylinositol 3-kinase (PI3K)/Akt pathways. These pathways regulate the balance between cell viability and inflammation. The signaling pathways activated by a specific TLR are largely dictated by the adapter proteins recruited to the intracellular domain of the TLR upon ligand binding (Akira and Takeda, 2004). There are currently four cytosolic adaptor proteins that are thought to play a crucial role in specificity of individual TLR-mediated signaling pathways. Amongst them, TLR4 signaling involves all four adapter proteins, MyD88 (myeloid differentiation primary response gene 88), MyD88 adapter like [MAL; also known as TIRAP (TIR domain-containing adapter protein)], TIR domain-containing adapter protein inducing IFN- β domain-containing adapter molecule 1)], and TRIF-related adapter molecule [TRAM; also known as TICAM2 (TIR domain-containing adapter molecule 2)] (McGettrick and O'Neill, 2004). The differential recruitment of these adapter proteins by different TLRs form the basis for the specificity in the signaling process activated by them. 2.6.1.1 MyD88 is the primary adapter for microbial signaling Every TLR member differentially utilizes adapters, but MyD88 (296 amino acid protein) seems to be the widely used adapter molecule. MyD88 harbors a TIR domain as well as a death domain. The carboxy terminal of TIR domain interacts with the cognate domains in the cytoplasmic tails of the TLRs, and the amino terminal death domain mediates the interaction with the corresponding domain of interleukin 1 receptor-associated kinase 4 (IRAK4) (Wesche et al., 1997; Li et al., 2002). MyD88 was originally isolated as a myeloid differentiation primary response gene that is rapidly induced upon IL-6 stimulated differentiation of M1 myloleukemic cells into macrophages (Lord et al., 1990). 2.6.1.2 Adapters mediating MyD88-independent signaling Most of the TLRs seem to be absolutely dependent on the expression of MyD88 for all of their functions. MyD88-independent signaling events are controlled by TRIF/TRAM (for TLR4 and TLR 2,6) and induce IRF3-dependent type I interferon production (Fitzgerald et al., 2003; Hoebe et al., 2003; Oshiumi et al., 2003; Yamamoto et al., 2003).

20

2.6.2 Kinases involved in signaling from adapters to transcription factors 2.6.2.1 Downstream of TLR signaling by adapters are mediated by IRAK family The next component of downstream TLR signaling is the IRAK family members. IRAKs are important mediators in the signal transduction of the TLR family as they may act to potentiate the downstream signaling. So far, four IRAKs have been identified, such as IRAK1, IRAK2, IRAK4 and IRAKM. IRAK1 and IRAK4 possess intrinsic serine/threonine protein kinase activities, whereas IRAK2 and IRAKM lack this activity, that may negatively regulate TLR mediated signaling. IRAK1 has three TRAF6 (tumor necrosis factor receptor associated factor 6) binding motifs to mediate the interaction with TRAF6 (Ye et al., 2002) and undergoes autophosphorylation. IRAK4 and IRAK1 are sequentially phosphorylated and dissociated from MyD88, which results in activation of TRAF6. 2.6.2.2 TRAF6 is the central activator of MAPK during microbial infection TRAF6 belongs to an E3 ubiquitin ligase family, which facilitates the synthesis of lysine 63 linked polyubiquitin chains (Chen, 2005). TRAF6 is the

activator of canonical NF- B

pathway (Hayden and Ghosh, 2004). TRAF6 is ubiquitinated at K63 chains and this K63 polyubiquitinated TRAF6 mediates activation of the next component in the pathway, which is most likely to be TGF-β

activated kinase-1 (TAK1) (Sun et al., 2004). In fact, the TAK1

associated proteins, TAB2 and TAB3, contain a domain that interacts specifically with K63ubiquitin chains. This model for TLR signaling predicts that the TAK1-TAB complex associates with K63-ubiquitinated TRAF6 to activate TAK1 kinase, which then activates the IKK complex as well as the JNK kinases. Sato et al., 2003 reported that TRAF6 is involved in TRIF mediated IRF3 activation and NF- B activation during TLR signaling. However, a recent paper delineated the involvement of TRAF6 in TLR signaling, where TRAF6 is involved in MyD88 mediated NF- B activation but not TRIF mediated NF- B activation (Gohda et al., 2004). 2.6.3 Transcription factors activated by TLR engagement PAMPs stimulation through TLR-dependent and independent pathways converges at the activation of transcription factors NF- B, IRF3/7/5, and/or AP-1. These transcription factors collaborate with each other to produce a large number of cytokines, which are barely detectable 21

in resting cells. The multi-transcription factor binding sites in the promoter of a given gene lead to this highly specific activation (Jayalakshmi et al., 2007). The multistage gene regulation by this interaction and the specific transcription factors activated is discussed below. 2.6.3.1 NF- B as double edged sword The continued research on TLRs has led to the delineation of specificity in the regulation and interaction of transcription factors upon stimulation leading to a highly specific gene expression. NF- B is the major transcription factor, which functions on TLR signaling to control/elicit inflammation. NF- B was first described as a B cell specific transcription factor that binds the

B site in the Ig

light chain enhancer (Sen and Baltimore, 1986). Viral

promoters contain NF- B binding sites making it advantageous for its replication. So it is not exaggerating to say that cells which have NF- B as a sword against the viral infection turn back against to them. NF- B has often been called a „central mediator of the immune response‟. MAL-MyD88 and TRAM-TRIF pathways stimulate NF- B activation albeit with different kinetics (Selvarajoo, 2006). NF- B activity was found to be inducible in all cell types and it is now known that members of the NF- B/Rel family regulate many genes involved in immune and inflammatory responses (Pahl, 1999; Hayden and Ghosh, 2004). 2.6.3.2 Activating protein-1 (AP1) The JNK and p38 cascades are activated first and foremost in response to inflammatory cytokines, bacterial products, and various stress factors. Activation of TAK1 during TLR signaling results in the activation of MAPKs, including JNK/p38, leading to the activation of AP-1 (Ninomiya et al., 1999; Akira and Takeda, 2004; Sato et al., 2005), which together with NF- B governs the production of inflammatory cytokines and chemokines (Kawai and Akira, 2006). Activation of these JNK/p38 cascades is associated with selective activation of different AP-1 subunits and transcription factors interacting with AP-1 (Johnson and Lapadat, 2002). This activation via p38 is necessary for the full induction of TNF-α

-12 as

inhibition of p38 abrogates this biological response. All these studies together indicate that it is the differential activation and binding of AP-1 subunits, which contribute to the inflammation.

22

LIPOPROTEIN, ZYMOSAN

LIPOPOLYSACCHARIDES LPS

TLR4 MD2

TLR2 TLR6

CD14

PLASMA MEMBRANE

TIRAP TIR TOLLIP T R A F 6

TRAM

TRIF

MyD88

IRAK4 IRAK1

IRF5

Myd88 Dependent Pathway

TAK1 TAB2 TAB1

RIP1

P

Myd88 Independent Pathway

P

IKKγ

MKK3

IKKα

P MEK1 MEK2

IкB

P

JNK

P38

TPL2

P

(Ubiquitin Mediated Proteolysis)

P MKK6

IKKβ

P105 P

DEGRADATION

MKK7

MKK4

NF-кB

MAPK Signaling Pathway

AP1

ERK

IкB

NUCLEUS AP1 NF-кB CELL MEDIATED IMMUNITY

IRF5

IRF5

INFLAMMATORY CYTOKINES (IL-12, IL-1, IL-6, TNF-α)

BACTERIAL DEATH

T CELL

INFLUENCE ADAPTIVE IMMUNE RESPONSE

APOPTOSIS OF HOST CELL

DIRECT ANTIMICROBIAL RESPONSE

Figure 2.4 TLR 2, 6 Signaling Pathway 23

2.7 TLRs bridge the gap between innate and adaptive immunity Identification of the different ligands of the Toll like receptors has allowed the study of the cellular signaling that occurs after ligand engagement. The downstream signaling has revealed the previously unrecognized role of the innate immune system as a regulator of the adaptive immune response at several steps along the path from Toll-like receptor engagement to the resultant inflammatory response. The first example of the impact of Toll-like receptors in controlling the adaptive immune system is illustrated by the events that take place during the physical interaction between APCs and T cells in the lymphoid organs (Kang et al., 1996). The „„2-signal hypothesis‟‟ states that when a circulating T cell encounters a captured antigen on the surface of an APC in the context of a major histocompatibility complex, a second costimulatory signal must be seen by the T cell for it to become activated and to clonally expand. These essential co-stimulatory molecules include B7-1 (CD80) and B7-2 (CD86) on the surfaces of APCs that engage their cognate receptors on T cells (CD28 and CD152) at the time of antigen presentation. Engagement of Toll-like receptors by microbial products initiates the expression of these second signals. If this critical communication between the T cell and the APC does not occur, the T cell will invariably meet a fate of apoptosis or permanent anergy to the antigen stimulus. This phenomenon constitutes a valuable safety mechanism to prevent an inadvertent expansion of a T-cell clone; it requires that a pathogen must be recognized by the Toll-like receptors of the innate immune system before a fully developed adaptive immunologic reaction can occur (Kang et al., 1996). Secondly, the innate immune system directs the type of adaptive immune response that is waged against a stimulus. Naїve T cells have the potential to differentiate toward one of the two mutually antagonistic poles of helper T (TH) cell types termed TH1 or TH2 subsets. The principal function of the TH1 subset is to stimulate phagocyte-mediated defense against intracellular organisms, whereas the TH2 cells promote IgE, eosinophil, and mast-cell-mediated immune responses against extracellular pathogens (Barton et al., 2002; Trinchieri, 2003, Iwasaki and Medzhitov, 2004). The APCs produce cytokines that instruct the expanding clone of T cells to differentiate toward either a TH1 or a TH2 profile. It is becoming increasingly clear that the nature of the antigen and the Toll-like receptor to which it binds can determine the

24

specific cytokine milieu that the APC will produce to influence the polarity of the TH response (Kang et al., 1996). Lastly, APCs regulate a specialized subset of T cells known as regulatory T cells. Ordinarily the peripheral effector T cells remain in a quiescent state owing to their suppression by regulatory T cells. This mechanism of peripheral tolerance is important in protecting the host against the development of potentially auto reactive cells, but the presence of these regulatory cells also means that the concomitant, bystander suppression of the T cell bearing a useful receptor specific for the pathogen-associated molecular patterns of an invading organism could result in detrimental consequences to the host in the setting of an infection. There is now evidence that IL- 6 secreted from Toll-like receptor activated DCs can relieve this suppression, allowing the activation of the antigen-specific T cell during antigen presentation (Pasare and Medzhitov, 2003). Therefore, the Toll-like receptor expressing APCs not only provide the necessary co-stimulatory signals, while presenting antigens to the naïve T cells and cytokines that instruct TH1 or TH2 differentiation but also suppress the inhibitory regulators of the T cells in the appropriate setting, thereby permitting the mounting of an effective adaptive immune response.

Figure 2.5 TH Cell Proliferation activated by TLR (Akira et al., 2001)

25

During infection, CD4+ TH cell responses polarize to become primarily TH1 or TH2. TH1 cells, which make IFN- , are crucial for immunity to many bacterial and protozoal infections, whereas TH2 cells, which make IL-4, IL-5, and IL-13, are important for resistance to Helminth infections. Polarized TH1 responses are induced by dendritic cells (DCs), which respond to pathogen-derived TLR ligands to produce IL-12 and related cytokines that are instrumental in TH1 cell outgrowth and coordinately process and present Ag in the context of MHC class II to activate naїve TH cells. It has become clear recently that TLR-activated DCs generally favor the development of TH1 responses due in large part to the fact that TLR ligation usually induces the production of IL-12, a cytokine that plays a pivotal role in TH1 cell differentiation (Barton et al., 2002; Trinchieri, 2003, Iwasaki and Medzhitov, 2004). TLR ligands can activate dendritic cells to provide a MyD88-dependent negative signal for TH2 cell development (Jie, et al., 2005). Toll-like receptor-1 (TLR1) and TLR6 are receptors of the TLR family that form heterodimers with TLR2. Netea et al., 2008 investigated the role of TLR1 and TLR6 for the recognition of the fungal pathogen Candida albicans. TLR1 is not involved in the recognition of C. albicans, and TLR1 knock-out (TLR1−/−) mice showed a normal susceptibility to disseminated candidiasis. In contrast, recognition of C. albicans by TLR6 modulated the balance between TH1 and TH2 cytokines, and TLR6 knock-out mice displayed a defective production of IL-10 and an increased IFN-γ release. Production of the monocyte-derived cytokines tumor necrosis factor, IL-1, and IL-6 was normal in TLR6−/− mice, and this was accompanied by a normal susceptibility to disseminated candidiasis. In conclusion, TLR6 is involved in the recognition of C. albicans and modulates the TH1/TH2 cytokine balance, but this results in a mild phenotype with a normal susceptibility of TLR6−/− mice to Candida infection (Netea et al., 2008). Increased trophoblast apoptosis has been observed in pregnancy complications such as preeclampsia. The factors promoting this cell death are unknown. Trophoblasts express Tolllike receptors (TLR), enabling them to recognize microorganisms. In response to the TLR2 ligand peptidoglycan (PDG), trophoblasts undergo apoptosis suggesting that gram-positive bacteria may promote trophoblast apoptosis. Abrahams et al., 2006 reported the role of TLR6 in the regulation of TLR2 mediated apoptosis following ligation by PDG.

26

Chapter 3

MATERIALS AND METHODS 3.1

MATERIALS

3.1.1 Amino Acid Sequence of Toll-Like Receptor 2 in FASTA Format >gi|19718734|ref|NP_003255.2| toll-like receptor 2 [Homo sapiens] MPHTLWMVWVLGVIISLSKEESSNQASLSCDRNGICKGSSGSLNSIPSGLTEAVKSLDLSNNRITYISNS DLQRCVNLQALVLTSNGINTIEEDSFSSLGSLEHLDLSYNYLSNLSSSWFKPLSSLTFLNLLGNPYKTLG ETSLFSHLTKLQILRVGNMDTFTKIQRKDFAGLTFLEELEIDASDLQSYEPKSLKSIQNVSHLILHMKQH ILLLEIFVDVTSSVECLELRDTDLDTFHFSELSTGETNSLIKKFTFRNVKITDESLFQVMKLLNQISGLL ELEFDDCTLNGVGNFRASDNDRVIDPGKVETLTIRRLHIPRFYLFYDLSTLYSLTERVKRITVENSKVFL VPCLLSQHLKSLEYLDLSENLMVEEYLKNSACEDAWPSLQTLILRQNHLASLEKTGETLLTLKNLTNIDI SKNSFHSMPETCQWPEKMKYLNLSSTRIHSVTGCIPKTLEILDVSNNNLNLFSLNLPQLKELYISRNKLM TLPDASLLPMLLVLKISRNAITTFSKEQLDSFHTLKTLEAGGNNFICSCEFLSFTQEQQALAKVLIDWPA NYLCDSPSHVRGQQVQDVRLSVSECHRTALVSGMCCALFLLILLTGVLCHRFHGLWYMKMMWAWLQAKRK PRKAPSRNICYDAFVSYSERDAYWVENLMVQELENFNPPFKLCLHKRDFIPGKWIIDNIIDSIEKSHKTV FVLSENFVKSEWCKYELDFSHFRLFDENNDAAILILLEPIEKKAIPQRFCKLRKIMNTKTYLEWPMDEAQ REGFWVNLRAAIKS

3.1.2 Amino Acid Sequence of Toll-Like Receptor 6 in FASTA Format >gi|20143971|ref|NP_006059.2| toll-like receptor 6 [Homo sapiens] MTKDKEPIVKSFHFVCLMIIIVGTRIQFSDGNEFAVDKSKRGLIHVPKDLPLKTKVLDMSQNYIAELQVS DMSFLSELTVLRLSHNRIQLLDLSVFKFNQDLEYLDLSHNQLQKISCHPIVSFRHLDLSFNDFKALPICK EFGNLSQLNFLGLSAMKLQKLDLLPIAHLHLSYILLDLRNYYIKENETESLQILNAKTLHLVFHPTSLFA IQVNISVNTLGCLQLTNIKLNDDNCQVFIKFLSELTRGSTLLNFTLNHIETTWKCLVRVFQFLWPKPVEY LNIYNLTIIESIREEDFTYSKTTLKALTIEHITNQVFLFSQTALYTVFSEMNIMMLTISDTPFIHMLCPH APSTFKFLNFTQNVFTDSIFEKCSTLVKLETLILQKNGLKDLFKVGLMTKDMPSLEILDVSWNSLESGRH KENCTWVESIVVLNLSSNMLTDSVFRCLPPRIKVLDLHSNKIKSVPKQVVKLEALQELNVAFNSLTDLPG CGSFSSLSVLIIDHNSVSHPSADFFQSCQKMRSIKAGDNPFQCTCELREFVKNIDQVSSEVLEGWPDSYK CDYPESYRGSPLKDFHMSELSCNITLLIVTIGATMLVLAVTVTSLCIYLDLPWYLRMVCQWTQTRRRARN IPLEELQRNLQFHAFISYSEHDSAWVKSELVPYLEKEDIQICLHERNFVPGKSIVENIINCIEKSYKSIF VLSPNFVQSEWCHYELYFAHHNLFHEGSNNLILILLEPIPQNSIPNKYHKLKALMTQRTYLQWPKEKSKR GLFWANIRAAFNMKLTLVTENNDVKS

27

3.2

TOOLS USED

3.2.1 FASTA It was the first widely used program designed for database similarity searching (Lipman and Pearson, 1988; Pearson, 1990). FASTA enables the user to compare a query sequence against large databases, and various versions of the program are available. FASTA determines all overlapping words of a certain length in both the query sequence and in each of the sequences in the target database, creating two lists in the process. 3.2.2 BLAST In bioinformatics, Basic Local Alignment Search Tool, or BLAST, (Altschul et al., 1900) is an algorithm for comparing biological sequences, such as the amino acid sequences of different proteins or the DNA sequences. A BLAST search enables a researcher to compare a query sequence with a library or database of sequences, and identify library sequences that resemble the query sequence above a certain threshold. BLAST searches for high scoring sequence alignments between the query sequence and sequences in the database, using a heuristic approach that approximates the Smith-Waterman algorithm. Protein-protein BLAST (blastp) program, given a protein query, returns the most similar protein sequences from the protein database that the user specifies. 3.2.3 PROTEIN DATA BANK (PDB) The Protein Data Bank (PDB) is a website (www.rcsb.org/pdb), a depositary files containing experimentally determined atomic coordinates of biological macromolecules. It is a public domain repository for 3-D structural data of proteins and nucleic acids. This data typically obtained by X-ray crystallography or NMR spectroscopy. The database is the central repository for biological structural data. It includes placing queries to the PDB and interpreting files.

3.2.4 Swiss Model Swiss

model

is

a

fully automated

protein

structure

homology-modelling

server

(http://swissmodel.expasy.org//SWISS-MODEL.html), accessible via the ExPASY web server. The purpose of this server is to make Protein Modelling accessible to all biochemists and molecules biologists worldwide. SWISS-MODEL was initiated in 1993 by Manuel Peitsch, and further developed at Glaxo Welcome Experimental Research in Geneva and the 28

SIB Swiss Institute of Bioinformatics by Manuel Peitsch, Nicolas Guex and Torsten Schwede. Since 2001, SWISS-MODEL is being developed by Torsten Schwede’s Structural Bioinformatics Group at the SIB & Biozentrum (University of Basel). Computational resources for the SWISS-MODEL server are provided in collaboration by the Biozentrum (University Basel), the Swiss Institute of Bioinformatics and the Advanced Biomedical Computing Center (NCI Frederick, USA) (Guex and Peitsch, 1997; Schwede et al., 2003; Arnold et al., 2006). 3.2.5 SWISS PDB VIEWER V 3.7 SP5 Deep view (formerly called Swiss-PDB Viewer) is a friendly but powerful molecular graphics program. It is designed for use with computing tools available from the Expert Protein Analysis System, or ExPASy Molecular Biology Server in Geneva. Deep View is under continuing development by Nicolas Guex and Manuel C. Peitsch of Geneva Glaxo Welcome Experimental Research. Deep view allows you to build models from scratch, simply by giving an amino acid sequence. It allows viewing several proteins simultaneously and superimposing them to compare their structures and sequences. It supports surface rendering, homology modeling, structure quality (threading) evaluation, energy minimization, site-directed mutagenesis, loop rebuilding, electrostatic field calculation, structure superposition, Ramachandran plot generation, and sequence-structure viewing (Guex and Peitsch, 1997). 3.2.6 STRUCTURAL ANALYSIS AND VALIDATION SERVER SAVS is a server (http://nihserver.mbi.ucla.edu/SAVES_3) for analyzing protein structures for validity and assessing how correct they are. It utilizes six programs for doing this: 

PROCHEK- Checks the stereochemical quality of a protein structure by analyzing residueby-residue geometry and overall structure geometry (Morris et al., 1992; Laskowski et al., 1993).



WHAT_CHECK- Derived from a subset of protein verification tools from the WHATIF program (Vriend 1990), this does extensive checking of many sterochemical parameters of the residues in the model.



ERRAT- Analyzes the statistics of non-bonded interactions between different atom types and plots the value of the error function versus position of 9-residue sliding window, calculated by comparison with statistics from refined structures (Colovos and Yeates, 1993 ).

29



VERIFY_3D- Determines the compatibility of an atomic model (3D) with its own amino acid sequence (1D) by assigned a structural class based on its location and environment and comparing the results to good structures (Bowie et al., 1991; Luethy et al., 1992).



PROVE- Calculates the volumes of atoms in macromolecules using an algorithm which treats the atoms like hard spheres and calculates a statistical Z-score deviation for the model from highly resolved (2.0 Å or better) and refined (R-factor of 0.2 or better) PDB-deposited structures.

3.2.7 RAMACHANDRAN PLOT A Ramachandran plot (also known as Ramachandran Map or a Ramachandran Diagram), developed by Gopalasamudram Narayana Ramachandran, and is a way to visualize dihedral angles φ against ψ of amino acid residues in protein structure. It shows the possible conformations of φ and ψ angles for the polypeptide. One would expect their larger side chains would result in more restrictions and consequently a smaller allowable region in the Ramachandran plot (Ramachandran et al., 1963). 3.2.8 HEX 4.5 Hex was developed by Dave Ritchie, 1996 department of computing science, university of Aberdeen, Scotland. Hex is an interactive molecular graphics program for calculating and displaying feasible docking modes of pairs of protein and DNA molecules. Hex can also calculate small-ligand/protein docking (provided the ligand is rigid), and it can superpose pairs of molecules using only knowledge of their 3D shapes. The main thing which distinguishes Hex from other macromolecular docking programs and molecular graphics packages is its use of spherical polar Fourier correlations to accelerate the docking and superposition calculations. In Hex's docking calculations, each molecule is modeled using 3D parametric functions which are used to encode both surface shape and electrostatic charge and potential distributions. By writing an expression for the overlap of pairs of parametric functions, one can derive an expression for a docking score as a function of the six degrees of freedom in a rigid body docking search (Ritchie and Kemp, 2000).

30

3.3 METHODOLOGY 3.3.1 Target Identification As the object was to find the region of interaction between TLR2/TLR6 and TIR domains of TLR2 and TLR6, these were the target proteins. 3.3.2 Target sequence retrieval The first and foremost step was searching for the protein through NCBI that provide us with the basic properties and the ID for the protein along with the FASTA format. The primary sequences was retrieved from NCBI using the following link: http://ncbi.nlm.nih.gov/ 3.3.3 Database searching for the target protein The FASTA format of the protein was noted and was given as input for NCBI protein- protein BLAST (blastp). The results showed a number of homologous proteins, which were similar in their protein sequence with the target protein. The extent of similarity was judged on the basis of certain parameters like E-value, Identities and Positives. 3.3.4 Retrieval of PDB file Homologous sequences of TLR2 were retrieved from Protein Data Bank. PDB files (PDB ID2Z7X and 1FYW) were homologous to TLR2 protein sequences. 3.3.5 Prediction of protein structure by homology modelling The 3 D structure of TLR 6 and TIR domain of TLR6 were predicted by using the SWISS MODEL server. We had performed homology modelling for TLR2 also by using the SWISS MODEL server. The steps performed for homology modelling are as follows: i.

Identify homologous proteins and determine the extent of their sequence similarity with one another and the unknown protein sequence.

ii.

Align the sequences.

iii.

Identify structurally conserved and structurally variable regions.

iv.

Generate coordinates for core (structurally conserved) residues of the unknown structure from those of the known structure(s).

v.

Generate conformations for the loops (structurally variable) in the unknown structure. 31

vi.

Build the side-chain conformations.

vii.

Refine and evaluate the unknown structure.

3.3.6 Analysis and Verification of the models All models were then uploaded to SAVS Server to analyze predicted protein structures for validity and assessing. All models were submitted to PROCHECK to check the stereo-chemical quality of a protein structure by analyzing residue-by-residue geometry and overall structure geometry. PROCHECK summary of all predicted models show the disallowed regions according to the RAMACHANDRAN Plot and the bad contacts of the model. We had submitted all models to Verify-3D to determine the compatibility of an atomic model (3D) with its own amino acid sequence (1D) by assigned a structural class based on its location and environment (alpha, beta, loop, polar, nonpolar etc) and comparing the results to good structures. 3.3.7 Structure refinement and energy minimization Deep View (Swiss PDB viewer) was then used to visualize and to rectify the bad contacts by Energy Minimization after removing the disallowed regions. 3.3.8 Directed for the Docking by using HEX The program HEX was used for rigid body protein-protein docking between TLR2 and TLR6. The docking was also carried out between TIR domains of both TLR2 and TLR6. For the docking of TLR2/TLR6 and TIR domains of TLR2 and TLR6, we employed grid dimension of 0.6, twist range of 15, distance range of 15, scan step of 1, sub steps 2, steric scan 16, and final search 25 and 500 solutions. The correlation type used was the „shape and electrostatic‟. The program generated 500 lowest energy matches. Post processing involved bumps removal and NEWTON like energy minimization. Automated selection of docked structure was based upon threshold of RMS within 2Å. Selection from subsequent output was done on the basis that only those docked solutions, which had their TIR domains in the same plane and their respective N-terminals facing the cell membrane. Out of these, the docked complex having the least energy was selected.

32

3.3.8 Analysis of region of interaction Selected docked complex of TLR2 and TLR6 and TIR domains of both TLR2 and TLR6 were analyzed to find out the regions of interaction among them. Swiss PDB viewer was used to find out the interacting amino acid residues between two molecules.

33

Chapter 4

RESULTS AND DISCUSSION 4.1 Homology Modelling Results 4.1.1 TLR2 The following 3-D structure of TLR2 was obtained by using SWISS MODEL server. The generated model was based on template PDB ID 2Z7X (chain A) and had residue range from 27 to 553 and sequence identity of 92.15%.

34

Figure 4.1 Structure of TLR2 predicted by using SWISS MODEL server

35

4.1.2 TIR-TLR2 The model of TIR domain of TLR2 generated by submitting its FASTA format to SWISS MODEL server. The generated model of TIR domain of TLR2 was based on template PDB ID 1O77 (chain A) and had residue range from 639 to 784 and sequence identity of 95.89%.

36

37

Figure 4.2 Structure of TIR domain of TLR2 predicted by using SWISS MODEL server

38

4.1.3 TLR6 The following 3-D structure of TLR6 was obtained by using SWISS MODEL server. The generated model was based on template PDB ID 2Z7X (chain B) and had residue range from 33 to 556 and sequence identity of 49.62%.

39

Figure 4.3 Structure of TLR6 predicted by using SWISS MODEL server

40

4.1.4 TIR-TLR6 The model of TIR domain of TLR6 generated by submitting its FASTA format to SWISS MODEL server. The model generated of TIR domain of TLR6 was based on template PDB ID 1FYV (chain A) and had residue range from 630 to 786 and sequence identity of 87.90%.

Figure 4.5 Structure of TIR domain of TLR6 predicted by using SWISS MODEL server

41

4.2 Ramachandran Plot 4.2.1 TLR2 Ramachandran plot of TLR2 shows 68.18% amino acid residues in the core region, 31.5% in the allowed region and 0.4% in the generously allowed region.

Figure 4.6 Ramachandran Plot of TLR2 4.2.2 TIR-TLR2 Ramachandran plot of TIR domain of TLR2 shows 83.1% amino acid residues in the core region, 14.7% in the allowed region and 2.2% in the generously allowed region.

Figure 4.7 Ramachandran Plot of TIR domain of TLR2 42

4.2.3 TLR6 Ramachandran plot of TLR6 shows 78.8% amino acid residues in the core region, 19.8% in the allowed region and 1.4% in the generously allowed region. Comparatively more residues form β–pleated sheet.

Figure 4.8 Ramachandran Plot of TLR6 4.2.4 TIR-TLR6 Ramachandran plot of TIR domain of TLR6 shows 81.9% amino acid residues in the core region, 17.4% in the allowed region and 0.7% in the generously allowed region.

Figure 4.9 Ramachandran Plot of TIR domain of TLR6 43

4.3 PROCHECK Summary All predicted models were submitted to PROCHECK to check the stereochemical quality of modelled protein structures by analyzing residue-by-residue geometry and overall structure geometry. The PROCHECK results of all models are as follows: 4.3.1 TLR2 +----------<<< P R O C H E C K S U M M A R Y >>>----------+ | | | /var/www/html/Services/SAVES_3/jobs/1287804/TLR2.pdb 2.0 527 residues | | | *| Ramachandran plot: 68.1% core 31.5% allow 0.4% gener 0.0% disall | | | *| All Ramachandrans: 44 labelled residues (out of 525) | +| Chi1-chi2 plots: 9 labelled residues (out of 354) | | | +| Main-chain params: 5 better 0 inside 1 worse | | Side-chain params: 5 better 0 inside 0 worse | | | *| Residue properties: Max.deviation: 4.1 Bad contacts: 0 | *| Bond len/angle: 5.5 Morris et al class: 2 2 2 | +| 1 cis-peptides | | G-factors Dihedrals: -0.36 Covalent: 0.26 Overall: -0.12 | | | | M/c bond lengths:100.0% within limits 0.0% highlighted | | M/c bond angles: 97.6% within limits 2.4% highlighted | *| Planar groups: 80.6% within limits 19.4% highlighted 7 off graph | | | +----------------------------------------------------------------------------+ + May be worth investigating further. * Worth investigating further.

4.3.2 TIR-TLR2 +----------<<< P R O C H E C K S U M M A R Y >>>----------+ | | | /var/www/html/Services/SAVES_3/jobs/1731225/TLR2_TIR.p 2.0 146 residues | | | +| Ramachandran plot: 83.1% core 14.7% allow 2.2% gener 0.0% disall | | | *| All Ramachandrans: 10 labelled residues (out of 144) | +| Chi1-chi2 plots: 2 labelled residues (out of 108) | | | | Main-chain params: 6 better 0 inside 0 worse | | Side-chain params: 5 better 0 inside 0 worse | | | *| Residue properties: Max.deviation: 5.6 Bad contacts: 0 | *| Bond len/angle: 6.7 Morris et al class: 1 1 2 | +| 2 cis-peptides | | G-factors Dihedrals: -0.20 Covalent: 0.27 Overall: -0.01 | | | | M/c bond lengths:100.0% within limits 0.0% highlighted | | M/c bond angles: 97.2% within limits 2.8% highlighted | *| Planar groups: 69.7% within limits 30.3% highlighted 6 off graph | | | +----------------------------------------------------------------------------+ + May be worth investigating further. * Worth investigating further.

44

4.3.3 TLR6 +----------<<< P R O C H E C K S U M M A R Y >>>----------+ | | | /var/www/html/Services/SAVES_3/jobs/3875898/TLR6.pdb 2.0 472 residues | | | *| Ramachandran plot: 78.8% core 19.8% allow 1.4% gener 0.0% disall | | | *| All Ramachandrans: 22 labelled residues (out of 465) | +| Chi1-chi2 plots: 3 labelled residues (out of 324) | | | | Main-chain params: 6 better 0 inside 0 worse | | Side-chain params: 5 better 0 inside 0 worse | | | +| Residue properties: Max.deviation: 11.1 Bad contacts: 0 | +| Bond len/angle: 4.8 Morris et al class: 1 1 2 | | | | G-factors Dihedrals: -0.23 Covalent: 0.21 Overall: -0.05 | | | | M/c bond lengths: 100.0% within limits 0.0% highlighted | | M/c bond angles: 96.7% within limits 3.3% highlighted | *| Planar groups: 77.1% within limits 22.9% highlighted 10 off graph | | | +----------------------------------------------------------------------------+ + May be worth investigating further. * Worth investigating further.

4.3.4 TIR-TLR6 +----------<<< P R O C H E C K S U M M A R Y >>>----------+ | | | /var/www/html/Services/SAVES_3/jobs/2535652/TLR6_2.pdb 2.0 157 residues | | | +| Ramachandran plot: 81.9% core 17.4% allow 0.7% gener 0.0% disall | | | *| All Ramachandrans: 7 labelled residues (out of 155) | +| Chi1-chi2 plots: 2 labelled residues (out of 117) | | | | Main-chain params: 6 better 0 inside 0 worse | | Side-chain params: 5 better 0 inside 0 worse | | | +| Residue properties: Max.deviation: 4.9 Bad contacts: 0 | +| Bond len/angle: 3.4 Morris et al class: 1 2 2 | | | | G-factors Dihedrals: -0.22 Covalent: 0.33 Overall: 0.00 | | | | M/c bond lengths:100.0% within limits 0.0% highlighted | | M/c bond angles: 98.1% within limits 1.9% highlighted | *| Planar groups: 75.4% within limits 24.6% highlighted 2 off graph | | | +----------------------------------------------------------------------------+ + May be worth investigating further. * Worth investigating further.

45

4.4 Verify- 3D Results Table 4.1 Verify-3D results S. No.

Name of Protein

Percentage of residues has averaged 3D-1D score >0.2

Status

1

TLR2

93.97%

Pass

2

TIR-TLR2

93.88%

Pass

3

TLR6

94.04%

Pass

4

TIR-TLR6

95.97%

Pass

46

4.5 Docking Results 4.5.1 Docking Result of TLR2 and TLR6 TLR2 and TLR6 were submitted for docking to Hex that generated 500 models. The following model was selected on the basis of certain criteria and had least energy.

TLR2 TLR6

Figure 4.10.1 Ribbon structure of docked complex

TLR2

TLR6

TLR2

TLR6

Figure 4.10.2 Backbone and molecular surface structure of docked complex Figure 4.10 Interaction between TLR2 and TLR6 after Protein-Protein Docking

47

Table 4.2 Docking correlation summary of TLR2/TLR6 Etotal

Eshape

Eforce

Vshape

Vclash

(KJ/mol)

(KJ/mol)

(KJ/mol)

(KJ/mol)

(KJ/mol)

-343.56

-321.78

-21.78

302.79

0.00

Bmp

H-H bond

RMS

0.00

-1

-1

4.5.2 Regions of Interactions between TLR2 and TLR6 Three regions of interaction were identified in docked complex of TLR2 and TLR6.

TLR6

TLR2 1

2

3

Figure 4.11 Regions of interaction between TLR2 and TLR6

48

i.

First region of interaction between TLR2 and TLR6

TLR2 TLR2 ASN248

ii.

TLR6

TLR6

ASN248

ASP233

ASP233

Second region of interaction between TLR2 and TLR6

TLR2

TLR6

TLR2

LEU250

TLR6 LEU250

ASN231 ASN231

iii.

Third region of interaction between TLR2 and TLR6

TLR2

TLR6

TLR6

TLR2 LEU318

LEU318

LYS339

LYS338

Figure 4.12 Amino acid residues involved in interactions between TLR2 and TLR6 49

Table 4.3 Amino Acid residues involved in interaction between TLR2 and TLR6

S. No.

1

2

3

Distances (Å)

Amino Acid residue of TLR2

Amino Acid residue of TLR6

1.43

ASN248 (OD1)

ASP233 (OD1)

1.49

ASN248 (OD1)

ASP233 (OD2)

1.28

ASN248 (CZ)

ASP233 (OD2)

1.62

ASN248 (ND2)

ASP233 (OD2)

1.66

LEU250 (CD2)

ASN231 (OD1)

1.83

LEU250 (CD2)

ASN231 (ND2)

1.80

LYS338 (CE)

LEU318 (CB)

2.57

LYS338 (NZ)

LEU318 (CB)

2.67

LYS338 (CE)

LEU318 (CG)

Abbreviations: ASN- Asparagine, ASP- Aspartic Acid, B- Beta, C- Carbon, D- Delta, E- Epsilon, GGamma, LEU- Leucine, LYS- Lysine, N- Nitrogen, O- Oxygen, Z- Zeta.

50

4.5.3 Docking Result of TIR Domains of TLR2 and TLR6 The following docked complex of TIR domains of both TLR2 and TLR6 was selected as both protein were in the same plane and had their N-terminal towards the cell membrane.

TIR-TLR2

TIR-TLR6

TIR-TLR6

TIR-TLR2

Figure 4.13.1 Ribbon structure of docked complex

TIR-TLR2

TIR-TLR6 TIR-TLR2

TIR-TLR6

Region of interaction

Figure 4.13.2 Backbone and molecular surfaces structure of docked complex

Figure 4.13 Interaction between TIR domains of TLR2 and TLR6 after Protein-Protein Docking

51

Table 4.4 Docking correlation summary of TIR domains of TLR2 and TLR6 Etotal

Eshape

Eforce

Vshape

Vclash

(KJ/mol)

(KJ/mol)

(KJ/mol)

(KJ/mol)

(KJ/mol)

-135.7

-134.4

-1.4

-117.2

0.0

Bmp

H-H bond

RMS

0.0

-1

-1

4.5.4 Region of interaction between TIR domains of TLR2 and TLR6

TIR-TLR6

TIR-TLR2

TIR-TLR6

TIR-TLR2

ILE745 ILE745 PHE678 PHE678

PRO680 GLN747

PRO680

GLN747

Figure 4.14 Region of interaction between TIR domains of TLR2 & TLR6

Table 4.5 Amino Acid residues involved in interaction between TIR domains of TLR2 & TLR6 S. No.

DISTANCE (Å) TIR DOMAIN OF TLR2 TIR DOMAIN OF TLR6

1

2.37

ILE745 (CA)

PRO680 (C)

2

2.55

GLN747 (CA)

PRO680 (CD)

3

2.84

GLN747 (CD)

PHE678 (C)

Abbreviations: A- Alpha, C- Carbon, D- Delta, GLN- Glutamine, ILE- Isoleucine, PHE- Phenylalanine, PRO- Proline.

52

4.6

DISCUSSION

As of now, it is understood that TLR2 signaling complex functions via forming a heterodimer either with TLR1 to recognize Tri-acylated LP, or with TLR6 to recognize Di-acylated LP, Zymosan of yeast and GPI anchors of T. cruzi. Previous in vitro studies as well as computerized docking studies suggest that MyD88 and TIR domain of TLR2, which is activated by formation of heterodimer with either TLR1 or TLR6 (Gautam et al., 2006). For the fungal infection to occur it is essential that a heterodimer forms between TLR2 and TLR6 and TIR domains of both TLR2 and TLR6 to activate the signaling. In the consequence of an ability to form such a heterodimer, a decreased activity is observed in innate immune response. Separate molecular modeling studies were carried out for the interaction of TLR2 and TLR6 molecules and TIR domains of both TLR2 and TLR6. Previously resolved crystal structures of TLR2, TLR1 and their TIR domains exist, but none of TLR6. Structures of TLR6 and TIR domain of TLR6 were modeled by using SWISS MODEL. In our project work studies were corroborated using computer assisted docking methodology of the two TIR domains interaction of TLR2 and TLR6. We used remodeled structure of TLR2, its TIR domain, modeled structure of TLR6, and its TIR domain as initiating molecules. As described in methodology all protein molecules were individually structurally refined and energy minimized. Hex program was run for rigid protein- protein docking between TLR2 and TLR6 and between TIR domains of both TLR2 and TLR6. The correlation type „shape and electrostatic‟ was used. We employed grid dimension of 0.6Å, twist range of 15Å, distance range of 15Å, scan step of 1, sub steps 2, steric scan 16, and final search 25 and 500 solutions. The program had generated 500 lowest energy matches. The Hex program selected the docked structures, which had threshold RMS within 2Å. From these combinations only those docked structure were selected which had their N-terminal facing towards cell membrane and were in same plane. This complex represented the functionally meaningful orientations between TLR2 and TLR6 (Figure 4.10) and TIR domains of both TLR2 and TLR6 (Figure 4.13). The docked complex, which had least energy score was selected for further analysis. From the table 4.2 and table 4.4 we can see the energy scores and correlation summary of docked complex of TLR2/TLR6 and TIR domains of both TLR2 and TLR6, respectively.

53

We have done detail analysis of selected TLR2 and TLR6 docked complex to find out the interacting amino acid residues between both molecules. From the figure 4.11 we can see that TLR2 and TLR6 are interacting with each other at three points. The figure 4.12 shows that in first region amino acid residues ASN248 of TLR2 interacts with ASP233 of TLR6. In second region LEU250 of TLR2 interacts with ASN231 of TLR6 and in third region LYS338 of TLR2 interacts with LEU318 of TLR6 (Table 4.3). Study of selected docked complex (Figure 4.13) of TIR domains of TLR2 and TLR6 was also carried out. From the figure 4.14 we can see that amino acid residues ILE745 and GLN747 of TIR domain of TLR2 are interacting with PRO680 and PHE678 of TIR domain of TLR6. The three bonds have been emerged as the prominent factor for the stabilization of this region in the docked complex of TIR domains of TLR2 and TLR6. These bonds are formed between the residues ILE745(CA)---PRO680(C), GLN747(CA)---PRO680(CD), and GLN747(CD)--PHE678(C) which have distances of 2.37Å, 2.55Å and 2.84Å, respectively (Table 4.5). These interactions are crucial for TLR2/TLR6 mediated responses. This Heterodimerization of TLR2 and TLR6 and TIR domains of both TLR2 and TLR6 activates the NF- B signaling. This active signaling complex further recruits other intracellular adapter molecules such as MyD88 and TIRAP. This active signaling results in the expression of pro- and anti-inflammatory cytokines IL-1, IL-6, IL-12 and TNF-α. IL-12 plays a pivotal role in TH1 cell differentiation to stimulate immune responses.

54

Chapter 5

CONCLUSION TLRs not only recognize pathogens but also, upon ligand binding, initiate a cascade of cellular signaling that direct the subsequent immune responses. We conclude that heterodimerization of TLR2 with TLR6 is an evolutionary process which enhance the ligand recognition capacity to enable the innate immune system. We found that the amino acid residues ASN248, LEU250 and LYS338 of TLR2 are interacting with residues ASP233, ASN231 and LEU318 of TLR6 respectively in three regions while ILE745 and GLN747 residues of TIR domain of TLR2 are interacting with PRO680 and PHE678 residues of TIR domain of TLR6. These amino acid residues play an important role in the formation of heterodimer. After the formation of heterodimer, TLR2/TLR6 complex can recognize the numerous structures of LP present in various pathogens, thus, providing a sort of basic level specificity to the innate immune system in humans. In our project work we have studied about the heterodimerization of TLR2/TLR6 which is essential for the activation of pro- and anti-inflammatory cytokine cascade and T cell proliferation. In future this heterodimer can act as a drug target to stimulate immune responses.

55

REFERENCE 1.

Abrahams, V.M.; Aldo, P.B.; Romero, R. and Mor, G. (2006) TLR6 protects first trimester Trophoblast cells from TLR2/TLR1 mediated apoptosis. American Journal of Reproductive Immunology, Volume 55, Number 6, 390-391.

2.

Akira, S. and Takeda, K. (2004). Toll-like receptor signalling. Nat Rev Immunol; 4:499-511.

3.

Akira, S.; Takeda, K. and Kaisho, T. (2001). Toll-like receptors: critical proteins linking innate and acquired immunity. Nat Immunol; 2:675-80.

4.

Altschul, S.F.; Gish, W.; Miller, W.; Myers, E.W.; Lipman D.J. (1990). Basic Local Alignment Search Tool. J. Mol. Biol. 215:403-410.

5.

Arnold, K.; Bordoli, L.; Kopp, J.; and Schwede, T. (2006). The SWISS-MODEL Workspace: A webbased environment for protein structure homology modelling. Bioinformatics, 22,195-201.

6.

Barton, G.M. and Medzhitov, R. (2002). Control of adaptive immune responses by Toll-like receptors. Curr. Opin. Immunol; 14:380.

7.

Baxevanis, A.D. and Ouellette, B.F.F. (2005). Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, third edition. Wiley.

8.

Blundell, T.L.; Sibanda, B.L.; Sternberg, M.J.E.; and Thornton, J.M. (1987) Knowledge-Based Prediction of Protein Structures and the Design of Novel Molecules. Nature 326: 347-352.

9.

Bowei, A. and O’Neill, L.A. (2000). The interleukin-1 receptor/toll-like receptor superfamily: signal generators for proinflammatory interleukins and microbial products. J Leukoc Biol; 67:508

10.

Bowie, J.U.; Lüthy, R. and Eisenberg, D. (1991). A method to identify protein sequences that fold into a known three-dimensional structure. Science; 253(5016):164-70.

11.

Chaudhary, P. M.; Ferguson, C.; Nguyen, V.; Nguyen, O.; Massa, H. F.; Eby, M.; Jasmin, A.; Trask, B. J.; Hood, L. and Nelson, P. S. (1998). Cloning and characterization of two Toll/Interleukin-1 receptorlike genes TIL3 and TIL4: evidence for a multi-gene receptor family in humans. Blood 91:4020-4027.

12.

Chen, Z.J. (2005). Ubiquitin signalling in the NF-kappaB pathway. Nat Cell Biol; 7:758-65

13.

Chuang, T. and Ulevitch, R. J. (2001). Identification of hTLR10: a novel human Toll-like receptor preferentially expressed in immune cells. Biochim. Biophys. Acta 1518:157-161.

14.

Colonna, M.; Trinchieri, G. and Liu, YJ. (2004). Plasmacytoid dendritic cells in immunity. Nat Immunol; 5:1219-26.

15.

Colovos, C. and Yeates, T.O. (1993). Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci. 2(9):1511-9.

16.

Du, X.; Poltorak, A.; Wei, Y. and Beutler, B. (2000). Three novel mammalian Toll-like receptors: gene structure, expression, and evolution. Eur. Cytokine Netw. 11:362-371.

17.

Fitzgerald, K.A.; McWhirter, S.M.; Faia, K.L.; Rowe, D.C., Latz, E.; Golenbock, D.T.; Coyle, A.J.; Liao, S.M. and Maniatis, T. (2003). IKKepsilon and TBK1 are essential components of the IRF3 signaling pathway. Nat Immunol; 4:491-6.

18.

Fitzgerald, K.A.; Palsson-McDermott, E.M.; Bowie AG, Jefferies CA, Mansell AS, Brady G, Brint E, Dunne A, Gray P, Harte MT, McMurray D, Smith DE, Sims JE, Bird TA and O'Neill LA. (2001). Mal (MyD88-adapter-like) is required for Toll-like receptor-4 signal transduction. Nature; 413:78-83.

19.

Gay, N. J. and Keith, F. J. (1991). Drosophila Toll and IL-1 receptor. Nature 351:355-356.

20.

Gilcrease, M.Z. (2006). "Integrin signaling in epithelial cells". Cancer Lett. 247 (1): 1-25.

56

21.

Gohda, J.; Matsumura, T.; Inoue, J. (2004). Cutting edge: TNFRassociated factor (TRAF) 6 is essential for MyD88-dependent pathway but not toll/IL-1 receptor domaincontaining adaptor-inducing IFN-beta (TRIF)-dependent pathway in TLR signaling. J Immunol; 173:2913-7

22.

Guex, N. and Peitsch, M. C. (1997) SWISS-MODEL and the Swiss-PdbViewer: An environment for comparative protein modelling. Electrophoresis 18: 2714-2723.

23.

Hashimoto, C.; Hudson, K. L. and Anderson, K. V. (1988). The Toll gene of Drosophila, required for dorsal-ventral embryonic polarity, appears to encode a transmembrane protein. Cell 52:269-279.

24.

Hayden, M.S.; Ghosh, S. (2004) Signaling to NF-kappaB. Genes Dev; 18:2195-224.

25.

Hoebe, K.; Du, X.; Georgel, P.; Janssen, E.; Tabeta, K.; Kim, S.O.; Goode, J.; Lin, P.; Mann, N.; Mudd, S.; Crozat, K.; Sovath, S.; Han, J. and Beutler, B. (2003). Identification of Lps2 as a key transducer of MyD88-independent TIR signalling. Nature; 424:743-8.

26.

Iwasaki, A. and Medzhitov, R. (2004). Toll-like receptor control of the adaptive immune responses. Nat. Immunol. 5:987.

27.

Janeway, C. A. Jr., and Medzhitov, R. (2002). Innate immune recognition. Annu. Rev. Immunol. 20:197216.

28.

Janssens, S. and Beyaert, R. (2003). Role of Toll-Like Receptors in Pathogen Recognition. Clinical Microbiology Reviews, October, p. 637-646, Vol. 16, No. 4.

29.

Jayalakshmi Krishnan, Kumar Selvarajoo, Masa Tsuchiya, Gwang Lee1, and Sangdun Cho, (August 2007), “Toll-like receptor signal transduction”, Experimental and Molecular Medicine, , Vol. 39, No. 4, 421438.

30.

Jie Sun, Matthew Walsh, Alejandro V. Villarino, Laura Cervi, Christopher A. Hunter, Yongwon Choi and Edward J. Pearce. (2005). The Journal of Immunology; 174: 742-751.

31.

Jitendra K. Gautam, Ashish, Laurey D. Comeau, Joanna K. Krueger, and Michael F. Smith, Jr. (2006). Structural and functional evidence for the role of the TLR2 DD loop in TLR1/TLR2 Heterodimerisation and signaling. J Biol Chem.; 281(40): 30132–30142.

32.

Johnson, G.L. and Lapadat, R. (2002). Mitogen-activated protein kinase pathways mediated by ERK, JNK, and p38 protein kinases. Science; 298:1911-2.

33.

Kanae Oda and Hiroaki Kitano (2006). A comprehensive map of the toll-like receptor signaling network. Molecular Systems Biology 2:2006.0015.

34.

Kang, Sammy S. W.; Kauls, Lynda S. and Gaspari, Anthony A. (2006). Toll-like receptors: Applications to dermatologic disease, J Am Acad Dermatol; 54:951-83.

35.

Kawai, T. and Akira, S. (2006). Innate immune recognition of viral infection. Nat Immunol; 7:131-7.

36.

Kopp J. and Schwede T. (2004) The SWISS-MODEL Repository of annotated three-dimensional protein structure homology models Nucleic Acids Research 32, D230-D234.

37.

Kuntz, I.D.; Meng, E.C.; and Shoichet, B.K., (1994). Receptor-Based Molecular Design, Acc. Chem. Res. 27, 117-123.

38.

Laskowski, R. A.; MacArthur, M. W.; Moss, D. S. & Thornton, J. M. (1993). PROCHECK: a program to check the stereochemical quality of protein structures. J. Appl. Cryst., 26, 283-291.

39.

Lemaitre, B.; Nicolas, E.; Michaut, L.; Reichhart, J. M. and Hoffmann, J. A. (1996). The dorsoventral regulatory gene cassette spatzle/Toll/cactus controls the potent antifungal response in Drosophila adults. Cell 86:973-983

40.

Li, S.; Strelow, A.; Fontana, E.J. and Wesche, H. (2002). IRAK-4: a novel member of the IRAK family with the properties of an IRAK-kinase. Proc Natl Acad Sci USA; 99:5567-72.

57

41.

Lord, K.A.; Hoffman-Liebermann, B. and Liebermann, D.A. (1990). Complexity of the immediate early response of myeloid cells to terminal differentiation and growth arrest includes ICAM-1, Jun-B and histone variants. Oncogene; 5:387-96.

42.

Luthy, R.; Bowie, J.U. and Eisenberg, D. (1992). Assessment of protein models with three-dimensional profiles. Nature; 356(6364):83-5.

43.

McGettrick, A.F. and O'Neill, L.A. (2004). The expanding family of MyD88-like adaptors in Toll-like receptor signal transduction. Mol Immunol; 41:577-82.

44.

Medzhitov, R. (2001). Toll-like receptors and innate immunity. Nat. Rev. Immunol. 1:135-145.

45.

Medzhitov, R., and C. A. Janeway, Jr. (1997). Innate immunity: the virtues of a nonclonal system of recognition. Cell 91:295-298.

46.

Medzhitov, R., and Janeway C. A. (2000). Advances in immunology: Innate immunity. N Engl J Med; 343:338-44.

47.

Medzhitov, R.; Preston-Hurlburt, P.; and Janeway, C. A., Jr. (1997). A human homologue of the Drosophila Toll protein signals activation of adaptive immunity. Nature 388:394-397.

48.

Morris, A.L.; MacArthur, M.W.; Hutchinson, E.G. & Thornton, J.M. (1992). Stereochemical quality of protein structure coordinates. Proteins, 12, 345-364.

49.

Netea, M. G.; Van de Veerdonk, Frank; Van der Meer, J. W. M. and Kullberg, B. J. (2008). Role of TLR1 and TLR6 in the host defense against disseminated candidiasis FEMS Immunology & Medical Microbiology 52 (1):118–123.

50.

Netea, M. G.; Van der Graaf, C.; Van der Meer, J. W. M. and Kullberg, B. J. (2004). Recognition of fungal pathogens by Toll-like receptors European Journal of Clinical Microbiology & Infectious Diseases Volume 23, Number 9:672-676.

51.

Ninomiya-Tsuji, J.; Kishimoto, K.; Hiyama, A.; Inoue. J.; Cao, Z. and Matsumoto, K. (1999). The kinase TAK1 can activate the NIK-I kappaB as well as the MAP kinase cascade in the IL-1 signalling pathway. Nature; 398:252-6.

52.

Oshiumi, H.; Sasai, M.; Shida, K.; Fujita, T.; Matsumoto, M. and Seya, T. (2003). TIR-containing adapter molecule (TICAM)-2, a bridging adapter recruiting to toll-like receptor 4 TICAM-1 that induces interferon-beta. J Biol Chem; 278:49751-62.

53.

Pahl, H.L. (1999). Activators and target genes of Rel/NF-kappaB transcription factors. Oncogene; 18:685366.

54.

Pasare, C. and Medzhitov, R. (2003). Toll Pathway-dependent blockade of CD4+CD5+T cell-mediated suppression by dendritic cells. Science; 299:1033-6

55.

Pearson, W.R. (1990). Rapid and Sensitive Sequence Comparison with FASTP and FASTA.

56.

Pearson, W.R. and Lipman, D.J. (1988). Improved Tools for Biological Sequence Comparison. PNAS 85:2444- 2448.

57.

Poltorak, A., X. He, I. Smirnova, M. Y. Liu, C. Van Huffel, X. Du, D. Birdwell, E. Alejos, M. Silva, C. Galanos, M. Freudenberg, P. Ricciardi-Castagnoli, B. Layton, and B. Beutler. (1998). Defective LPS signaling in C3H/HeJ and C57BL/10ScCr mice: mutations in Tlr4 gene. Science 282:2085-2088

58.

Ramachandran, G.N.; Ramakrishnan, C. and Sasisekharan, V. (1963): Stereochemistry of polypeptide chain configurations. In: J. Mol. Biol. vol. 7, p. 95-99.

59.

Rarey, and Matthias, (2002). Protein-ligand docking in drug design. In: Thomas Lengauer (ed.) Bioinformatics- from genomes to drugs, Vol I (Wiley-VCH) 7: 315-318.

60.

Ritchie, D.W. and Kemp, G.J.L. (2000), Protein Docking Using Spherical Polar Fourier Correlations, PROTEINS: Struct. Funct. Genet. 39, 178-194.

58

61.

Rock, F. L.; Hardiman, G.; Timans, J. C.; Kastelein, R. A. and Bazan, J. F. (1998). A family of human receptors structurally related to Drosophila Toll. Proc. Natl. Acad. Sci. USA 95:588-593

62.

Ronni, T.; Agrawal, V.; Haykinson, M.; Haberland, M.E.; Cheng, G.; Smale, S.T. (2003), Common interaction surfaces of the toll-like receptor 4 cytoplasmic domain stimulate multiple nuclear targets. Mol Cell Biol; 23:2543-55

63.

Sato, S.; Sugiyama, M.; Yamamoto, M.; Watanabe, Y.; Kawai, T.; Takeda, K. and Akira, S. (2003). Toll/IL-1 receptor domain-containing adaptor inducing IFN-beta (TRIF) associates with TNF receptorassociated factor 6 and TANK-binding kinase 1, and activates two distinct transcription factors, NF-kappa B and IFN-regulatory factor-3, in the Toll-like receptor signaling. J Immunol; 171:4304-10.

64.

Schwede T, Kopp J, Guex N, and Peitsch MC (2003) SWISS-MODEL: an automated protein homologymodeling server. Nucleic Acids Research 31: 3381-3385.

65.

Selvarajoo, K. (2006). Discovering differential activation machinery of the Toll-like receptor 4 signaling pathways in MyD88 knockouts. FEBS Lett; 580:1457-64.

66.

Sen, R. and Baltimore, D. (1986). Multiple nuclear factors interact with the immunoglobulin enhancer sequences. Cell; 46:705-16

67.

Sun, L.; Deng, L.; Ea, C.K.; Xia, Z.P. and Chen ZJ. (2004). The TRAF6 ubiquitin ligase and TAK1 kinase mediate IKK activation by BCL10 and MALT1 in T lymphocytes. Mol Cell; 14:289-301.

68.

Takeda, K. and Akira, S. (2005). Toll-like receptors in innate immunity. Int Immunol;17:1-14

69.

Takeuchi, O., T. Kawai, H. Sanjo, N. G. Copeland, D. J. Gilbert, N. A. Jenkins, K. Takeda, and S. Akira. (1999). TLR6: a novel member of an expanding Toll-like receptor family. Gene 231:59-65

70.

Trinchieri, G. (2003). Interleukin-12 and the regulation of innate resistance and adaptive immunity. Nat. Rev. Immunol. 3:133.

71.

Vriend, G. (1990). WHAT IF: A molecular modeling and drug design program. J. Mol. Graph. 8:52-56.

72.

Wesche, H.; Henzel, W.J.; Shillinglaw, W.; Li, S. and Cao, Z. (1997). MyD88: an adapter that recruits IRAK to the IL-1 receptor complex. Immunity; 7:837-47.

73.

Wilke and Christoph, (2004). Molecular conformation and ligand- receptor docking, Seminar: Algorithms in drug design, WSI Computer Science Department.

74.

Xu, Y.; Tao, X.; Shen, B.; Horng, T.; Medzhtov, R.; Manley J.L. et al. (2000), Structural basis for signal transduction by the toll/interleukin-1 receptor domains. Nature; 408:111-5

75.

Yamamoto, M.; Sato, S.; Hemmi, H.; Hoshino, K.; Kaisho, T.; Sanjo, H.; Takeuchi, O.; Sugiyama, M.; Okabe, M.; Takeda, K. and Akira, S. (2003). "Role of adaptor TRIF in the MyD88-independent toll-like receptor signaling pathway", Science 301 (5633): 640-3.

76.

Yamamoto, M.; Sato, S.; Hemmi, H.; Hoshino, K.; Kaisho, T.; Uematsu, S.; Takeuchi, O.; Sugiyama, M.; Takeda, K. and Akira, S. (2003). "TRAM is specifically involved in the Toll-like receptor 4-mediated MyD88-independent signaling pathway". Nat Immunol 4 (11): 1144-50.

77.

Ye, H.; Arron, J.R.; Lamothe, B.; Cirilli, M.; Kobayashi, T.; Shevde, N.K.; Segal, D.; Dzivenu, O.K.; Vologodskaia, M.; Yim, M.; Du, K.; Singh, S.; Pike J.W.; Darnay, B.G.; Choi, Y. and Wu, H. (2002). Distinct molecular mechanism for initiating TRAF6 signalling. Nature; 418:443-7.

59

Web links used http://biocarta.com/ http://blast.ncbi.nlm.nih.gov/Blast.cgi http://en.wikipedia.org/wiki/ http://ncbi.nlm.nih.gov/ http://nihserver.mbi.ucla.edu/SAVES_3 http://swissmodel.expasy.org//SWISS-MODEL.html http://www.biochem.vt.edu/modeling/homology.html http://www.cryst.bbk.ac.uk/PPS2/course/section3/rama.html http://www.csd.abdn.ac.uk/hex/ http://www.genome.jp/kegg/

60