TLR4-Targeting Therapeutics: Structural Basis and Computer-Aided Drug Discovery Approaches

Abstract

The integration of computational techniques into drug development has led to a substantial increase in the knowledge of structural, chemical, and biological data. These techniques are useful for handling the big data generated by empirical and clinical studies. Over the last few years, computer-aided drug discovery methods such as virtual screening, pharmacophore modeling, quantitative structure-activity relationship analysis, and molecular docking have been employed by pharmaceutical companies and academic researchers for the development of pharmacologically active drugs. Toll-like receptors (TLRs) play a vital role in various inflammatory, autoimmune, and neurodegenerative disorders such as sepsis, rheumatoid arthritis, inflammatory bowel disease, Alzheimer's disease, multiple sclerosis, cancer, and systemic lupus erythematosus. TLRs, particularly TLR4, have been identified as potential drug targets for the treatment of these diseases, and several relevant compounds are under preclinical and clinical evaluation. This review covers the reported computational studies and techniques that have provided insights into TLR4-targeting therapeutics. Furthermore, this article provides an overview of the computational methods that can benefit a broad audience in this field and help with the development of novel drugs for TLR-related disorders.

Keywords: TLR4; agonist; antagonist; computer-aided drug discovery; molecular dynamics; virtual screening.

Figure 1

The drug discovery and development pipeline. The initial step in drug discovery is target identification; the aim is to find a potential target via different approaches like genomics and proteomics. The next step is target validation, which classifies the molecular target and evaluates whether it is suitable for drug development by various in vivo and in vitro methods. Various techniques such as high-throughput screening (HTS), ligand-based drug design (LBDD), pharmacophore modeling, molecular docking, and structure-based drug design (SBDD) are employed to identify hit compounds. These hit molecules are then evaluated and modified to improve the activity of the lead compound (e.g., by quantitative SAR [QSAR] analysis). Prior to human clinical trials, preclinical studies are carried out that provide detailed knowledge on the toxicity and appropriate dose of drugs. Based on these findings, it is decided whether the drug is ready to be tested in humans. Clinical trials are the final stage of the drug development process. Once clinical trials are over, the successful drug is approved by the FDA and becomes available on the market for clinical use.

Figure 2

TLR4-MD2-LPS complex (PDB ID: 3FXI). (a) The ECD is divided into three subdomains: N-terminal, central, and C-terminal domains. The evolutionary conserved patches A and B resides in the N-terminal domain and central domain, respectively. (b) Key residues involved in the binding of LPS with TLR4-MD2 complex. TLR4 residue R241 is shown in green color and MD2 residues K58, Y102, S118, S120, K122, and G123 shown in magenta color. LPS is represented by red color.

Figure 3

The binding mechanism of LPS for TLR4 activation. A series of events takes place prior to LPS recognition by the TLR4-MD2 complex. (1) LBP extracts LPS from the bacterial membrane and (2) transfers it to membrane anchored CD14, where it binds to the hydrophobic pocket located at the N terminus and forms a monomeric complex. (3) CD14 facilitates LPS transfer to the TLR4-MD2 complex where it initiated the intracellular pathway.

Figure 4

Illustration of TLR4 signaling pathway. Recognition of LPS activates MyD88-dependent and TRIF-dependent pathways. In MyD88-dependent pathway, recruitment of MyD88 by TIRAP initiates the interaction of IRAKs and TRAF6, that leads to the activation of transcription factors. Endocytosis of TLR4 initiates TRIF-dependent signaling, which involves recruitment of TRIF and TRAM, that leads to subsequent induction of TBK1 and IKKε, as well as activation of transcription factor IRF3. AP1, activated protein 1; ERK, extracellular-regulated kinase, IRAK, interleukin receptor-associated kinase; IKK, inhibitor of ĸ B kinase; IRF3, interferon response factor 3; JNK, c-Jun N-terminal kinase; MAPK, mitogen-activated protein kinase; MyD88, myeloid differentiation primary response protein 88; NF-ĸB, nuclear factor ĸB; p38, protein 38; TAK1, transforming growth factor β-activated kinase 1; TBK1, TANK-binding kinase 1; TIRAM, TRIF-related adaptor molecule; TIRAP, TIR domain-containing adaptor protein; TRAF, tumor necrosis factor receptor-associated factor.

Figure 5

Two-dimensional structures of important TLR4 ligands.