Molecular Mechanism of Binding Selectivity of Inhibitors toward BACE1 and BACE2 Revealed by Multiple Short Molecular Dynamics Simulations and Free-Energy Predictions

Chen, Jianzhong; Wang, Jinan; Yin, Baohua; Pang, Laixue; Wang, Wei; Zhu, Weiliang

doi:10.1021/acschemneuro.9b00348

Cited by 75 publications

(45 citation statements)

References 101 publications

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“…Quantum mechanics/molecular mechanics (QM/MM) techniques were utilized to predict the binding energies of inhibitors for multiple therapeutic targets including BACE1 [62]. Multiple short MD simulations were coupled with MM-GBSA calculations to understand selectivity of BACE1 vs. BACE2 inhibitors [63]. Free energy perturbation (FEP) was successful in optimizing a novel series of amidine containing spirocyclic BACE1 inhibitors [64].…”

Section: Cadd For the Development Of Bace1 Inhibitorsmentioning

confidence: 99%

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Computer-Aided Drug Design of β-Secretase, γ-Secretase and Anti-Tau Inhibitors for the Discovery of Novel Alzheimer’s Therapeutics

Mouchlis

Melagraki

Zacharia

et al. 2020

IJMS

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Aging-associated neurodegenerative diseases, which are characterized by progressive neuronal death and synapses loss in human brain, are rapidly growing affecting millions of people globally. Alzheimer’s is the most common neurodegenerative disease and it can be caused by genetic and environmental risk factors. This review describes the amyloid-β and Tau hypotheses leading to amyloid plaques and neurofibrillary tangles, respectively which are the predominant pathways for the development of anti-Alzheimer’s small molecule inhibitors. The function and structure of the druggable targets of these two pathways including β-secretase, γ-secretase, and Tau are discussed in this review article. Computer-Aided Drug Design including computational structure-based design and ligand-based design have been employed successfully to develop inhibitors for biomolecular targets involved in Alzheimer’s. The application of computational molecular modeling for the discovery of small molecule inhibitors and modulators for β-secretase and γ-secretase is summarized. Examples of computational approaches employed for the development of anti-amyloid aggregation and anti-Tau phosphorylation, proteolysis and aggregation inhibitors are also reported.

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Section: Cadd For the Development Of Bace1 Inhibitorsmentioning

confidence: 99%

“…were coupled with MM-GBSA calculations to understand selectivity of BACE1 vs. BACE2 inhibitors [63]. Free energy perturbation (FEP) was successful in optimizing a novel series of amidine containing spirocyclic BACE1 inhibitors [64].…”

Section: Cadd For the Development Of Bace1 Inhibitorsmentioning

confidence: 99%

Computer-Aided Drug Design of β-Secretase, γ-Secretase and Anti-Tau Inhibitors for the Discovery of Novel Alzheimer’s Therapeutics

Mouchlis

Melagraki

Zacharia

et al. 2020

IJMS

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“…Nevertheless, we may predict some approximate behavior from simulations that suffer from sampling inefficiencies, in certain conditions e.g., upon introducing mutations or relaxation after removing ligands ( Orellana, 2019 ). Here, we employed different starting configurations and multiple short simulations to enhances the configuration space sampling to better probe the conformational changes ( Knapp et al, 2018 ; Chen et al, 2019b , c ; Chen et al, 2020 ). Fortunately, the conformational transitions of the nivolumab/PD-1 complex we concerned have been observed in a simulation time of 100 ns.…”

Section: Discussionmentioning

confidence: 99%

Investigating the Role of the N-Terminal Loop of PD-1 in Binding Process Between PD-1 and Nivolumab via Molecular Dynamics Simulation

Liu

Jin

Chen

et al. 2020

Front. Mol. Biosci.

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The blockade of immune checkpoints, such as programmed death receptor 1 (PD-1) and programmed death ligand 1 protein (PD-L1), is a promising therapeutic approach in cancer immunotherapy. Nivolumab, a humanized IgG4 antibody targeting PD-1, was approved by the US Food and Drug Administration for several cancers in 2014. Crystal structures of the nivolumab/PD-1 complex show that the epitope of PD-1 locates at the IgV domain (including the FG and BC loops) and the N-terminal loop. Although the N-terminal loop of PD-1 has been shown to play a dominant role in the complex interface of the static structure, its role in the dynamic binding process has not been illustrated clearly. Here, eight molecular systems were established for nivolumab/PD-1 complex, and long-time molecular dynamics simulations were performed for each. Results showed that the N-terminal loop of PD-1 prefers to bind with nivolumab to stabilize the interface between IgV and nivolumab. Furthermore, the binding of the N-terminal loop with nivolumab induces the rebinding between the IgV domain and nivolumab. Thus, we proposed a two-step binding model for the nivolumab/PD-1 binding, where the interface switches to a high-affinity state with the help of the N-terminal loop. This finding suggests that the N-terminal loop of PD-1 might be a potential target for anti-PD-1 antibody design, which could serve as an important gatekeeper for the anti-PD-1 antibody binding.

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“…e dynamic simulation was carried out using GROMACS 4.0 [27]. In this experiment, it is the GROMOS 96 force field that was added to the docking complexes.…”

Section: E Molecular Dynamics Simulation Of Docking Complexesmentioning

confidence: 99%

Molecular Interactions of Renin with Chikusetsusaponin IV and Momordin IIc

Zhang

Zhu

Chen

2019

Journal of Chemistry

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The paper dealt with the molecular mechanism for the binding sites and driving forces of renin with chikusetsusaponin IV and momordin IIc by means of molecular docking and free energy calculation based on the crystal structure. The result showed that renin and the saponins fit well. As shown by LigPlot + software analyzing the hydrogen bonding and hydrophobic effect between renin and the saponins, the amino acid residues such as Ser230, Tyr85, and Tyr201 form the hydrogen bonds, with S3sp, S3, and S2′ being the active pockets. In addition, there are relatively strong hydrophobic interactions of renin with saponins in S3sp, S3, S2, S1, S1′, and S2′, with Gly228, Val36, Ala229, Gln19, Met303, Gln135, Ser41, Ile137, Asp38, Arg82, and Tyr83 being the key amino acids. The dynamics reached equilibration after about 1000 ps simulation with average root-mean-square deviations of 0.222 nm and 0.217 nm. The molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) yielded −1.10812 kcal/mol and −39.0587 kcal/mol total binding energy for the two complexes, respectively, which were primarily contributed by electrostatic and van der Waals interaction energies, and the binding was strongly unfavored by polar solvation energy, a further confirmation that momordin IIc has stronger hydrogen bonding and hydrophobic effect in the inhibition of renin than the chikusetsusaponin IV.

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Molecular Mechanism of Binding Selectivity of Inhibitors toward BACE1 and BACE2 Revealed by Multiple Short Molecular Dynamics Simulations and Free-Energy Predictions

Cited by 75 publications

References 101 publications

Computer-Aided Drug Design of β-Secretase, γ-Secretase and Anti-Tau Inhibitors for the Discovery of Novel Alzheimer’s Therapeutics

Computer-Aided Drug Design of β-Secretase, γ-Secretase and Anti-Tau Inhibitors for the Discovery of Novel Alzheimer’s Therapeutics

Investigating the Role of the N-Terminal Loop of PD-1 in Binding Process Between PD-1 and Nivolumab via Molecular Dynamics Simulation

Molecular Interactions of Renin with Chikusetsusaponin IV and Momordin IIc

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