Structural insights into pharmacophore-assisted in silico identification of protein–protein interaction inhibitors for inhibition of human toll-like receptor 4 – myeloid differentiation factor-2 (hTLR4−MD-2) complex

Abstract

Toll-like receptor 4 (TLR4) is a member of Toll-Like Receptors (TLRs) family that serves as a receptor for bacterial lipopolysaccharide (LPS). TLR4 alone cannot recognize LPS without aid of co-receptor myeloid differentiation factor-2 (MD-2). Binding of LPS with TLR4 forms a LPS−TLR4−MD-2 complex and directs downstream signaling for activation of immune response, inflammation and NF-κB activation. Activation of TLR4 signaling is associated with various pathophysiological consequences. Therefore, targeting protein–protein interaction (PPI) in TLR4−MD-2 complex formation could be an attractive therapeutic approach for targeting inflammatory disorders. The aim of present study was directed to identify small molecule PPI inhibitors (SMPPIIs) using pharmacophore mapping-based approach of computational drug discovery. Here, we had retrieved the information about the hot spot residues and their pharmacophoric features at both primary (TLR4−MD-2) and dimerization (MD-2−TLR4*) protein–protein interaction interfaces in TLR4−MD-2 homo-dimer complex using in silico methods. Promising candidates were identified after virtual screening, which may restrict TLR4−MD-2 protein–protein interaction. In silico off-target profiling over the virtually screened compounds revealed other possible molecular targets. Two of the virtually screened compounds (C11 and C15) were predicted to have an inhibitory concentration in μM range after HYDE assessment. Molecular dynamics simulation study performed for these two compounds in complex with target protein confirms the stability of the complex. After virtual high throughput screening we found selective hTLR4−MD-2 inhibitors, which may have therapeutic potential to target chronic inflammatory diseases.

Keywords:

• Toll-Like Receptor 4 (TLR4)
• lipopolysaccharide (LPS)
• hTLR4−MD-2 complex inhibitors
• protein–protein interactions (PPIs)
• small molecule protein–protein interaction inhibitors (SMPPIIs)

Abbreviations:

• ADR: Adverse drug reaction
• ADMET: Absorption, distribution, metabolism, excretion and toxicity
• API: Active pharmaceutical ingredient
• CHARMM: Chemistry at HARvard macromolecular mechanics
• DAMPs: Danger/damage associated molecular patterns
• DMEs: Drug-metabolizing enzymes
• DTs: Drug transporters
• ECD: Extra cellular domain
• fs: femto seconds
• LE: Ligand efficiency
• LMWIs: Low molecular weight inhibitors
• LPS: LipoPolySaccharide
• LRRs: Leucine rich repeats
• MD: Molecular dynamics
• MDS: Molecular dynamics simulation
• MD-2: Myeloid differentiation factor-2
• NAMD: NAnoscale molecular dynamics
• NF-κB: Nuclear factor Kappa B
• ns: nano seconds
• PAMPs: Pathogen associated molecular patterns
• PRRs: Pattern recognition receptors
• PPIs: Protein–protein interactions
• ps: pico seconds
• RMSD: Root mean square deviation
• RMSF: Root mean square fluctuation
• Ro4: Rule of four
• SMPPIIs: Small molecule protein-protein interaction inhibitors
• TIR: Toll/IL-1 receptor like
• TLRs: Toll-like receptors
• TLR4: Toll-like receptor 4
• TMD: Transmembrane domain
• VS: Virtual screening
• VMD: Visual molecular dynamics

Acknowledgement

Molecular visualization, analysis and graphics were performed by UCSF Chimera and VMD. Authors are thankful to Chemaxon for providing free academic licenses for the use of their software. Free research access to the BioSolveIT software toolkit through the scientific challenge scheme is gratefully acknowledged. Authors are also thankful to the authors of all the web servers and software packages that are freely available to academic users and were used in this study. Vinita Mishra is thankful for the research fellowship provided by the Puri Foundation for Education in India for carrying out the study.