Revealing the binding modes and the unbinding of 14-3-3σ proteins and inhibitors by computational methods

Hu, Guodong; Cao, Zanxia; Xu, Shicai; Wang, Wei; Wang, Jihua

doi:10.1038/srep16481

Cited by 21 publications

(10 citation statements)

References 65 publications

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“…For each trajectory, we extracted 500 snapshots from the last 50 ns of the MD trajectories with an interval of 100 ps. Briefly, the MM-PBSA method was used to calculate the binding free energies using the following equations [64,65]:…”

Section: Binding Free Energy Calculationsmentioning

confidence: 99%

Ligand Binding Mechanism and Its Relationship with Conformational Changes in Adenine Riboswitch

et al. 2020

IJMS

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Riboswitches are naturally occurring RNA aptamers that control the expression of essential bacterial genes by binding to specific small molecules. The binding with both high affinity and specificity induces conformational changes. Thus, riboswitches were proposed as a possible molecular target for developing antibiotics and chemical tools. The adenine riboswitch can bind not only to purine analogues but also to pyrimidine analogues. Here, long molecular dynamics (MD) simulations and molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) computational methodologies were carried out to show the differences in the binding model and the conformational changes upon five ligands binding. The binding free energies of the guanine riboswitch aptamer with C74U mutation complexes were compared to the binding free energies of the adenine riboswitch (AR) aptamer complexes. The calculated results are in agreement with the experimental data. The differences for the same ligand binding to two different aptamers are related to the electrostatic contribution. Binding dynamical analysis suggests a flexible binding pocket for the pyrimidine ligand in comparison with the purine ligand. The 18 μs of MD simulations in total indicate that both ligand-unbound and ligand-bound aptamers transfer their conformation between open and closed states. The ligand binding obviously affects the conformational change. The conformational states of the aptamer are associated with the distance between the mass center of two key nucleotides (U51 and A52) and the mass center of the other two key nucleotides (C74 and C75). The results suggest that the dynamical character of the binding pocket would affect its biofunction. To design new ligands of the adenine riboswitch, it is recommended to consider the binding affinities of the ligand and the conformational change of the ligand binding pocket.

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Section: Binding Free Energy Calculationsmentioning

confidence: 99%

Ligand Binding Mechanism and Its Relationship with Conformational Changes in Adenine Riboswitch

et al. 2020

IJMS

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“…In the MM-PSA, a series of representative snapshots from MD trajectories were collected to incorporate the effects of thermal averaging with force field/continuum solvent models. [ 15 ]. Compared with rigorous methods such as free energy perturbation (FEP), MM-PBSA is more computationally efficient [ 16 ].The MM-PBSA method was developed by Kollman et al [ 17 ] and has been widely used ever since, with more than 100 publications each year from 2010 to now [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Computational Studies of a Mechanism for Binding and Drug Resistance in the Wild Type and Four Mutations of HIV-1 Protease with a GRL-0519 Inhibitor

Dou

et al. 2016

IJMS

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Drug resistance of mutations in HIV-1 protease (PR) is the most severe challenge to the long-term efficacy of HIV-1 PR inhibitor in highly active antiretroviral therapy. To elucidate the molecular mechanism of drug resistance associated with mutations (D30N, I50V, I54M, and V82A) and inhibitor (GRL-0519) complexes, we have performed five molecular dynamics (MD) simulations and calculated the binding free energies using the molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) method. The ranking of calculated binding free energies is in accordance with the experimental data. The free energy spectra of each residue and inhibitor interaction for all complexes show a similar binding model. Analysis based on the MD trajectories and contribution of each residues show that groups R2 and R3 mainly contribute van der Waals energies, while groups R1 and R4 contribute electrostatic interaction by hydrogen bonds. The drug resistance of D30N can be attributed to the decline in binding affinity of residues 28 and 29. The size of Val50 is smaller than Ile50 causes the residue to move, especially in chain A. The stable hydrophobic core, including the side chain of Ile54 in the wild type (WT) complex, became unstable in I54M because the side chain of Met54 is flexible with two alternative conformations. The binding affinity of Ala82 in V82A decreases relative to Val82 in WT. The present study could provide important guidance for the design of a potent new drug resisting the mutation inhibitors.

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“…Their virtual docking into a high-resolution crystal structure of 14-3-3σ (PDB ID: 3P1N) and follow-up analysis led to the synthesis and cocrystallization of 11 14-3-3 inhibitors (e.g., compound B2 (19, Figure 12)). 138 A detailed computational analysis of the binding mode of eight of these molecules was performed by the group of Wang et al 139 They showed that the hydrophilic residues (Arg56, Arg129, and Tyr130 of 14-3-3σ) at the bottom of the binding pocket form seven stable hydrogen bonds with the phosphate group. In addition, two residues (Leu174 and Val178) in contact with a moiety accommodating the phosphate group contribute large van der Waals energies, and residue Leu126 provides large electrostatic energies.…”

Section: 137mentioning

confidence: 99%

Modulators of Protein–Protein Interactions

et al. 2014

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Direct interactions between proteins are essential for the regulation of their functions in biological pathways. Targeting the complex network of protein−protein interactions (PPIs) has now been widely recognized as an attractive means to therapeutically intervene in disease states. Even though this is a challenging endeavor and PPIs have long been regarded as "undruggable" targets, the last two decades have seen an increasing number of successful examples of PPI modulators, resulting in growing interest in this field. PPI modulation requires novel approaches and the integrated efforts of multiple disciplines to be a fruitful strategy. This perspective focuses on the hub-protein 14-3-3, which has several hundred identified protein interaction partners, and is therefore involved in a wide range of cellular processes and diseases. Here, we aim to provide an integrated overview of the approaches explored for the modulation of 14-3-3 PPIs and review the examples resulting from these efforts in both inhibiting and stabilizing specific 14-3-3 protein complexes by small molecules, peptide mimetics, and natural products.

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Revealing the binding modes and the unbinding of 14-3-3σ proteins and inhibitors by computational methods

Cited by 21 publications

References 65 publications

Ligand Binding Mechanism and Its Relationship with Conformational Changes in Adenine Riboswitch

Ligand Binding Mechanism and Its Relationship with Conformational Changes in Adenine Riboswitch

Computational Studies of a Mechanism for Binding and Drug Resistance in the Wild Type and Four Mutations of HIV-1 Protease with a GRL-0519 Inhibitor

Modulators of Protein–Protein Interactions

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