Allosteric Type and Pathways Are Governed by the Forces of Protein–Ligand Binding

Huang, Qiaojing; Song, Pengbo; Chen, Yixin; Liu, Zhongfan; Lai, Luhua

doi:10.1021/acs.jpclett.1c01253

Cited by 22 publications

(25 citation statements)

References 64 publications

(83 reference statements)

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“…Another latest success in computationally characterizing the modulatory impacts of allosteric ligands is the tool AlloType, 247 which deploys a thermodynamic model to predict the modulatory mechanisms of allosteric modulators. Energetic changes during substrate binding are quantified as readouts of the influence conferred by allosteric binding.…”

Section: Characterization Of Allosteric Modulatorsmentioning

confidence: 99%

Along the allostery stream: Recent advances in computational methods for allosteric drug discovery

Chai

Wang

et al. 2021

WIREs Comput Mol Sci

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Allostery is a universal, biological phenomenon in which orthosteric sites are finetuned by topologically distal allosteric sites triggered by perturbations, such as ligand binding, residue mutations, or post-translational modifications. Allosteric regulation is implicated in a variety of physiological and pathological conditions and is thus emerging as a novel avenue for drug discovery. Allosteric drugs have traditionally been discovered by serendipity through large-scale experimental screening. Recently, we have witnessed significant progress in biophysics, particularly in structural bioinformatics, which has facilitated the in-depth characterization of allosteric effects and the accurate detection of allosteric residues and exosites. These advances improve our understanding of allosterism and promote allosteric drug discovery, thereby revolutionizing the shift from the traditional serendipitous route used to discover allosteric drugs to the updated path centered on rational structure-based design. In this review, recent advances in computational methods applied to allosteric drug discovery are summarized. We comprehensively review these achievements along various levels of allosteric events, from the construction of allosteric databases to the identification and analysis of allosteric residues, signals, sites, and modulators. We expect to increase the awareness of the discovery of allosteric drugs using structure-based computational methods.

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Section: Characterization Of Allosteric Modulatorsmentioning

confidence: 99%

Along the allostery stream: Recent advances in computational methods for allosteric drug discovery

Chai

Wang

et al. 2021

WIREs Comput Mol Sci

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“…Many studies have shown that the level of GPX4 increases following the administration of puerarin. Moreover, Huang et al [ 63 ] reported that compound 1d4 can be an allosteric activator to activate GPX4 for connecting with the key allosteric site (D21 and D23) and key catalytic residues (C46, Q81, K90, W136 and so on). Meanwhile, the result of molecular docking predicted that puerarin may bind to GPX4 with residues ASP21, ASP23, LYS90 and ASP101.…”

Section: Discussionmentioning

confidence: 99%

Puerarin Induces Molecular Details of Ferroptosis-Associated Anti-Inflammatory on RAW264.7 Macrophages

et al. 2022

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Puerarin is a natural flavonoid with significant anti-inflammatory effects. Recent studies have suggested that ferroptosis may involve puerarin countering inflammation. However, the mechanism of ferroptosis mediated by the anti-inflammatory process of puerarin has not been widely explored. Herein, puerarin at a concentration of 40 μM showed an anti-inflammatory effect on lipopolysaccharide (LPS)-induced macrophages RAW264.7. The analysis of network pharmacology indicated that 51 common targets were enriched in 136 pathways, and most of the pathways were associated with ferroptosis. Subsequently, the analysis of metabolomics obtained 61 differential metabolites that were enriched in 30 metabolic pathways. Furthermore, integrated network pharmacology and metabolomics revealed that puerarin exerted an excellent effect on anti-inflammatory in RAW264.7 via regulating ferroptosis-related arachidonic acid metabolism, tryptophan metabolism, and glutathione metabolism pathways, and metabolites such as 20-hydroxyeicosatetraenoic acid (20-HETE), serotonin, kynurenine, oxidized glutathione (GSSG), gamma-glutamylcysteine and cysteinylglycine were involved. In addition, the possible active binding sites of the potential targeted proteins such as acyl-CoA synthetase long-chain family member 4 (ACSL4), prostaglandin-endoperoxide synthase 2 (PTGS2), arachidonate 15-lipoxygenase (ALOX15) and glutathione peroxidase 4 (GPX4) with puerarin were further revealed by molecular docking. Thus, we suggested that ferroptosis mediated the anti-inflammatory effects of puerarin in macrophages RAW264.7 induced by LPS.

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“…23,28,29 It can also be combined with the linear response theory to describe the conformational changes upon ligand binding and the resulting allostery. 26,30 The original ANM 29 regards the Cα atom of each residue in a protein as a node, and the node pairs within a cutoff distance are connected by harmonic springs with the same force constant, where the influence of side chains is not considered. Recently, Kaynak et al proposed a multiscale elastic network model, RESPEC, which includes side-chain information and protein−ligand interactions into the original ANM.…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

“…Here, we consider only the allosteric effect caused by the binding affinity and not the catalytic constant change, which requires quantum chemistry treatment. 30 A positive allostery (activation) occurs when ΔΔG < 0, while a negative allostery (inhibition) occurs when ΔΔG > 0. For the dynamic allostery, the structures do not change after ligand binding and the enthalpic contribution to ΔΔG becomes zero.…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

Quantitative Analysis of Dynamic Allostery

Huang

Lai

Liu

2022

J. Chem. Inf. Model.

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Dynamic allostery refers to one important class of allosteric regulation that does not involve noticeable conformational changes upon effector binding. In recent years, many “quasi”-dynamic allosteric proteins have been found to only experience subtle conformational changes during allosteric regulation. However, as enthalpic and entropic contributions are coupled to each other and even tiny conformational changes could bring in noticeable free energy changes, a quantitative description is essential to understand the contribution of pure dynamic allostery. Here, by developing a unified anisotropic elastic network model (uANM) considering both side-chain information and ligand heavy atoms, we quantitatively estimated the contribution of pure dynamic allostery in a dataset of known allosteric proteins by excluding the conformational changes upon ligand binding. We found that the contribution of pure dynamic allostery is generally small (much weaker than previously expected) and robustly exhibits an allosteric activation effect, which exponentially decays with the distance between the substrate and the allosteric ligand. We further constructed toy models to study the determinant factors of dynamic allostery in monomeric and oligomeric proteins using the uANM. Analysis of the toy models revealed that a short distance, a small angle between the two ligands, strong protein–ligand interactions, and weak protein internal interactions lead to strong dynamic allostery. Our study provides a quantitative estimation of pure dynamic allostery and facilitates the understanding of dynamic-allostery-controlled biological processes and the design of allosteric drugs and proteins.

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Allosteric Type and Pathways Are Governed by the Forces of Protein–Ligand Binding

Cited by 22 publications

References 64 publications

Along the allostery stream: Recent advances in computational methods for allosteric drug discovery

Along the allostery stream: Recent advances in computational methods for allosteric drug discovery

Puerarin Induces Molecular Details of Ferroptosis-Associated Anti-Inflammatory on RAW264.7 Macrophages

Quantitative Analysis of Dynamic Allostery

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