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.
J Biomol Struct Dyn
; 37(8): 1968-1991, 2019 May.
Article
en En
| MEDLINE
| ID: mdl-29842849
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.
Palabras clave
ADMET: Absorption, distribution, metabolism, excretion and toxicity; ADR: Adverse drug reaction; 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; LE: Ligand efficiency; LMWIs: Low molecular weight inhibitors; LPS: LipoPolySaccharide; LRRs: Leucine rich repeats; MD-2: Myeloid differentiation factor-2; MD: Molecular dynamics; MDS: Molecular dynamics simulation; NAMD: NAnoscale molecular dynamics; NF-κB: Nuclear factor Kappa B; PAMPs: Pathogen associated molecular patterns; PPIs: Proteinprotein interactions; PRRs: Pattern recognition receptors; 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; TLR4: Toll-like receptor 4; TLRs: Toll-like receptors; TMD: Transmembrane domain; Toll-Like Receptor 4 (TLR4); VMD: Visual molecular dynamics; VS: Virtual screening; fs: femto seconds; hTLR4−MD-2 complex inhibitors; lipopolysaccharide LPS); ns: nano seconds; proteinprotein interactions (PPIs); ps: pico seconds; small molecule proteinprotein interaction inhibitors (SMPPIIs)
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Simulación por Computador
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Antígeno 96 de los Linfocitos
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Receptor Toll-Like 4
Tipo de estudio:
Diagnostic_studies
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Prognostic_studies
Idioma:
En
Revista:
J Biomol Struct Dyn
Año:
2019
Tipo del documento:
Article
País de afiliación:
India