Selectivity mechanism of BCL-XL/2 inhibition through <i>in silico</i> investigation

Luan, Jiasi; Hu, Baichun; Wang, Shizun; Liu, Haihan; Lu, Shuaizhong; Li, Weixia; Sun, Xizhe; Shi, Jiyue; Wang, Jian

doi:10.1039/d2cp01755e

Cited by 9 publications

(7 citation statements)

References 36 publications

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“…XP docking results need to refer to XP Gscore, which is generally believed to be less than − 6, indicating that the ligand and protein have stable binding properties. The value of MM-GBSA energy is lower than − 30 kcal/mol, indicating that the ligand binds to the protein stably 28 . The scores of EA docking with TP53 and TLR4 are − 9.115 and − 6.285 respectively, which are less than − 6.…”

Section: Discussionmentioning

confidence: 97%

“…The amino acids that play an important role in the binding of EA to TNF-α protein are mainly ALA18, ARG32, ALA33, and VAL150, and their interactions are mainly hydrobridging, hydrogen bonding, and hydrophobic. The ΔAffinity score is greater than 0 indicating that the affinity between the mutant protein and the ligand is lower than that between the original protein and the ligand indicating that the affinity is weakened and that the mutant amino acid site is the key to protein–ligand binding 28 . The highest Δaffinity results for TNF-α were 150 (VAL → TRP), 18 (ALA → GLU), and 144 (PHE → GLY), corresponding to 21.868 kcal/mol, 5.812 kcal/mol, and 4.338 kcal/mol respectively, and the Δaffinity scores were all greater than 0.…”

Section: Discussionmentioning

confidence: 99%

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Network pharmacology, computational biology integrated surface plasmon resonance technology reveals the mechanism of ellagic acid against rotavirus

Zheng,

Haseeb,

Wang

et al. 2024

Sci Rep

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The target and mechanism of ellagic acid (EA) against rotavirus (RV) were investigated by network pharmacology, computational biology, and surface plasmon resonance verification. The target of EA was obtained from 11 databases such as HIT and TCMSP, and RV-related targets were obtained from the Gene Cards database. The relevant targets were imported into the Venny platform to draw a Venn diagram, and their intersections were visualized. The protein–protein interaction networks (PPI) were constructed using STRING, DAVID database, and Cytoscape software, and key targets were screened. The target was enriched by Gene Ontology (GO) and KEGG pathway, and the ‘EA anti-RV target-pathway network’ was constructed. Schrodinger Maestro 13.5 software was used for molecular docking to determine the binding free energy and binding mode of ellagic acid and target protein. The Desmond program was used for molecular dynamics simulation. Saturation mutagenesis analysis was performed using Schrodinger's Maestro 13.5 software. Finally, the affinity between ellagic acid and TLR4 protein was investigated by surface plasmon resonance (SPR) experiments. The results of network pharmacological analysis showed that there were 35 intersection proteins, among which Interleukin-1β (IL-1β), Albumin (ALB), Nuclear factor kappa-B1 (NF-κB1), Toll-Like Receptor 4 (TLR4), Tumor necrosis factor alpha (TNF-α), Tumor protein p53 (TP53), Recombinant SMAD family member 3 (SAMD3), Epidermal growth factor (EGF) and Interleukin-4 (IL-4) were potential core targets of EA anti-RV. The GO analysis consists of biological processes (BP), cellular components (CC), and molecular functions (MF). The KEGG pathways with the highest gene count were mainly related to enteritis, cancer, IL-17 signaling pathway, and MAPK signaling pathway. Based on the crystal structure of key targets, the complex structure models of TP53-EA, TLR4-EA, TNF-EA, IL-1β-EA, ALB-EA, NF-κB1-EA, SAMD3-EA, EGF-EA, and IL-4-EA were constructed by molecular docking (XP mode of flexible docking). The MMGBS analysis and molecular dynamics simulation were also studied. The Δaffinity of TP53 was highest in 220 (CYS → TRP), 220 (CYS → TYR), and 220 (CYS → PHE), respectively. The Δaffinity of TLR4 was highest in 136 (THR → TYR), 136 (THR → PHE), and 136 (THR → TRP). The Δaffinity of TNF-α was highest in 150 (VAL → TRP), 18 (ALA → GLU), and 144 (PHE → GLY). SPR results showed that ellagic acid could bind TLR4 protein specifically. TP53, TLR4, and TNF-α are potential targets for EA to exert anti-RV effects, which may ultimately provide theoretical basis and clues for EA to be used as anti-RV drugs by regulating TLR4/NF-κB related pathways.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Network pharmacology, computational biology integrated surface plasmon resonance technology reveals the mechanism of ellagic acid against rotavirus

Zheng,

Haseeb,

Wang

et al. 2024

Sci Rep

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“…The molecular mechanics/generalized born surface area (MM/GBSA) of the Prime module ( 55 ) was applied to calculate the binding free energy of ligand-protein complex trajectories via the following equations ( 56 )ΔGbind=ΔEMM+ΔGsolΔEMM=ΔEvdW+ΔECoulomb+ΔGCovalentΔGsol=ΔGGB+ΔGLipo+ΔGCorrectionΔECorrection=ΔEHbond+ΔEPacking+ΔESelfCont…”

Section: Methodsmentioning

confidence: 99%

S -nitrosylation of a receptor-like cytoplasmic kinase regulates plant immunity

Cui,

Pan,

Cui

et al. 2024

Sci. Adv.

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Perception of pathogen/microbial-associated molecular patterns (P/MAMPs) by plant cell surface receptors leads to a sustained burst of reactive oxygen species (ROS), a key feature of P/MAMP-triggered immunity (PTI). Here we report that P/MAMP recognition leads to a rapid nitrosative burst, initiating the accumulation of nitric oxide (NO), subsequently leading to S -nitrosylation of the receptor-like cytoplasmic kinase (RLCK), botrytis-induced kinase 1 (BIK1), at Cys 80 . This redox-based, posttranslational modification, promotes the phosphorylation of BIK1, subsequently resulting in BIK1 activation and stabilization. Further, BIK1 S -nitrosylation increases its physical interaction with RBOHD, the source of the apoplastic oxidative burst, promoting ROS formation. Our data identify mechanistic links between rapid NO accumulation and the expression of PTI, providing insights into plant immunity.

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“…Each complex was subjected to three repetitions of the simulation using the equilibrated part of the MD trajectories. Totally, 100 snapshots were extracted from the equilibrium trajectory for MM-GBSA free energy calculation according to the following equations: 36 Δ G bind = G complex − ( G protein + G ligand )Δ G bind = Δ E MM + Δ G sol − T Δ S Δ E MM = Δ E ele + Δ E vdW + Δ E int Δ G sol = Δ G GB + Δ G SA where Δ G blind represents the total binding free energy of protein–ligand complexes, while Δ E MM represents the gas-phase interaction energy, which is decomposed into electrostatic interaction energy (Δ E ele ), van der Waals interaction energy (Δ E vdW ) and internal energy of the system (Δ E int ). − T Δ S represents the change in ligand-binding conformational entropy where T and S denote the absolute temperature and entropy, respectively.…”

Section: Methodsmentioning

confidence: 99%