Study of early stages of amyloid Aβ13-23 formation using molecular dynamics simulation in implicit environments

Bajda, Marek; Filipek, Sławomir

doi:10.1016/j.compbiolchem.2015.02.014

Cited by 8 publications

(5 citation statements)

References 49 publications

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“…Then, these α-helical fragments convert into oligomers and fibrils with a β-structure; i.e., normally soluble peptides are converted to insoluble, β-rich amyloid deposits . Moreover, it has been reported that fibrillogenesis involves an oligomeric α-helical intermediate. , The α-helical monomeric structure and β-sheet pentamer were taken directly from the Protein Data Bank (both structures determined by NMR spectroscopy; PDB codes 1IYT and 2BEG, respectively), , while the oligomeric α-helical structures were obtained by protein–protein docking (Figure ), and upon further preparation, all forms were used for ligand docking. The helical monomer and oligomers contained the whole 42-amino acid amyloid sequence (Aβ 1–42 ), i.e., DAEFRHDSGY EVHHQKLVFF AEDVGSNKGA IIGLMVGGVV IA, and each chain presented an α-helix-kink-α-helix motif.…”

Section: Resultsmentioning

confidence: 99%

Dual Inhibitors of Amyloid-β and Tau Aggregation with Amyloid-β Disaggregating Properties: Extended In Cellulo, In Silico, and Kinetic Studies of Multifunctional Anti-Alzheimer’s Agents

Pasieka

Panek

Szałaj

et al. 2021

ACS Chem. Neurosci.

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In Alzheimer’s disease, neurons slowly degenerate due to the accumulation of misfolded amyloid β and tau proteins. In our research, we performed extended studies directed at amyloid β and tau aggregation inhibition using in cellulo ( Escherichia coli model of protein aggregation), in silico , and in vitro kinetic studies. We tested our library of 1-benzylamino-2-hydroxyalkyl multifunctional anti-Alzheimer’s agents and identified very potent dual aggregation inhibitors. Among the tested derivatives, we selected compound 18 , which exhibited a unique profile of biological activity. This compound was the most potent and balanced dual aggregation inhibitor (Aβ 42 inhibition (inh.) 80.0%, tau inh. 68.3% in 10 μM), with previously reported in vitro inhibitory activity against h BuChE, h BACE1, and Aβ ( h BuChE IC 50 = 5.74 μM; h BACE1 IC 50 = 41.6 μM; Aβ aggregation (aggr.) inh. IC 50 = 3.09 μM). In docking studies for both proteins, we tried to explain the different structural requirements for the inhibition of Aβ vs tau. Moreover, docking and kinetic studies showed that compound 18 could inhibit the amyloid aggregation process at several steps and also displayed disaggregating properties. These results may help to design the next generations of dual or selective aggregation inhibitors.

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Section: Resultsmentioning

confidence: 99%

Dual Inhibitors of Amyloid-β and Tau Aggregation with Amyloid-β Disaggregating Properties: Extended In Cellulo, In Silico, and Kinetic Studies of Multifunctional Anti-Alzheimer’s Agents

Pasieka

Panek

Szałaj

et al. 2021

ACS Chem. Neurosci.

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“…Transient formation of helical structures in Aβ 40 has been observed to expedite its aggregation, 44,45 and it has been suggested that the transient helices in Aβ 40 may accelerate the nucleation in solution by assembling the peptides and aligning them appropriately. 46,47 Recent experiments have also suggested a similar role of transient helices in Aβ aggregation mediated by membrane surfaces 48 or by an air−water interface. 49 Considering the importance of Aβ−fibril binding with respect to the secondary nucleation process, our finding that the Aβ 40 fibril surface can induce helical structures of bound Aβ 40 raises the possibility that the secondary nucleation of Aβ 40 might also involve a helix-mediated mechanism.…”

Section: ■ Conclusionmentioning

confidence: 95%

In Silico Study of Recognition between Aβ₄₀ and Aβ₄₀ Fibril Surfaces: An N-Terminal Helical Recognition Motif and Its Implications for Inhibitor Design

Jiang

Cao

Han

2017

ACS Chem. Neurosci.

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The recent finding that the surface of amyloid-β (Aβ) fibril can recruit Aβ peptides and convert them into toxic oligomers has rendered fibril surfaces attractive as inhibition targets. Through extensive simulations with hybrid-resolution and all-atom models, we have investigated how Aβ recognizes its own fibril surfaces. These calculations give a ∼2.6-5.6 μM half-saturation concentration of Aβ on the surface (cf. experimental value ∼6 μM). Aβ was found to preferentially bind to region 16-24 of Aβ fibrils through both electrostatic and van der Waals forces. Both terminal regions of Aβ contribute significantly to binding energetics. A helical binding pose of the N-terminal region of Aβ (Aβ) not seen before is highly preferred on the fibril surface. Aβ in a helical form can arrange side chains with similar properties on the same sides of the helix and maximize complementary interactions with side chain arrays characteristic of amyloid fibrils. Helix formation on a fibril surface implies a helix-mediated mechanism for Aβ oligomerization catalyzed by fibrils. We propose an Aβ analogue that can exhibit enhanced helical character and interactions with Aβ fibrils and may thus be used as a template with which to pursue potent inhibitors of Aβ-fibril interactions.

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“…Thus, there has been much effort put into the development and testing of implicit membrane solvent models 27-33 . Implicit membranes have appeared in several recent computational studies as they can assist in finding the proper native fold of a membrane protein for structural studies and calculations 34-36 . Implementation of an implicit membrane is currently available in packages such as APBS 32 , Delphi 33, 37 , and both the Amber 16 1 and AmberTools 16 2 suites.…”

Section: Introductionmentioning

confidence: 99%

“…In the case of membrane proteins, the membrane must also be included when modeling solvation effects. − In general, the molecules that make up a lipid membrane are much more complex than water or other small organic solvents, and this increases the computational expense of their inclusion. Thus, there has been much effort put into the development and testing of implicit membrane solvent models. − Implicit membranes have appeared in several recent computational studies, as they can assist in finding the proper native fold of a membrane protein for structural studies and calculations. − Implementation of an implicit membrane is currently available in packages such as APBS, Delphi, , and both the Amber 16 and AmberTools 16 suites. With the implementation of an implicit membrane model into the Amber/PBSA program, − the implicit membrane model can be more readily interfaced with the existing MMPBSA framework. − …”

Section: Introductionmentioning

confidence: 99%

Modeling Membrane Protein–Ligand Binding Interactions: The Human Purinergic Platelet Receptor

et al. 2016

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Membrane proteins, due to their roles as cell receptors and signaling mediators, make prime candidates for drug targets. The computational analysis of protein-ligand binding affinities has been widely employed as a tool in rational drug design efforts. Although efficient implicit solvent-based methods for modeling globular protein-ligand binding have been around for many years, the extension of such methods to membrane protein-ligand binding is still in its infancy. In this study, we extended the widely used Amber/MMPBSA method to model membrane protein-ligand systems, and we used it to analyze protein-ligand binding for the human purinergic platelet receptor (P2Y12R), a prominent drug target in the inhibition of platelet aggregation for the prevention of myocardial infarction and stroke. The binding affinities, computed by the Amber/MMPBSA method using standard parameters, correlate well with experiment. A detailed investigation of these parameters was conducted to assess their impact on the accuracy of the method. These analyses show the importance of properly treating the non-polar solvation interactions and the electrostatic polarization in the binding of nucleotide agonists and non-nucleotide antagonists to P2Y12R. Based on the crystal structures and the experimental conditions in the binding assay, we further hypothesized that the nucleotide agonists lose their bound magnesium ion upon binding to P2Y12R, and our computational study supports this hypothesis. Ultimately, this work illustrates the value of computational analysis in the interpretation of experimental binding reactions.

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Study of early stages of amyloid Aβ13-23 formation using molecular dynamics simulation in implicit environments

Cited by 8 publications

References 49 publications

Dual Inhibitors of Amyloid-β and Tau Aggregation with Amyloid-β Disaggregating Properties: Extended In Cellulo, In Silico, and Kinetic Studies of Multifunctional Anti-Alzheimer’s Agents

Dual Inhibitors of Amyloid-β and Tau Aggregation with Amyloid-β Disaggregating Properties: Extended In Cellulo, In Silico, and Kinetic Studies of Multifunctional Anti-Alzheimer’s Agents

In Silico Study of Recognition between Aβ₄₀ and Aβ₄₀ Fibril Surfaces: An N-Terminal Helical Recognition Motif and Its Implications for Inhibitor Design

Modeling Membrane Protein–Ligand Binding Interactions: The Human Purinergic Platelet Receptor

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Study of early stages of amyloid Aβ13-23 formation using molecular dynamics simulation in implicit environments

Cited by 8 publications

References 49 publications

Dual Inhibitors of Amyloid-β and Tau Aggregation with Amyloid-β Disaggregating Properties: Extended In Cellulo, In Silico, and Kinetic Studies of Multifunctional Anti-Alzheimer’s Agents

Dual Inhibitors of Amyloid-β and Tau Aggregation with Amyloid-β Disaggregating Properties: Extended In Cellulo, In Silico, and Kinetic Studies of Multifunctional Anti-Alzheimer’s Agents

In Silico Study of Recognition between Aβ40 and Aβ40 Fibril Surfaces: An N-Terminal Helical Recognition Motif and Its Implications for Inhibitor Design

Modeling Membrane Protein–Ligand Binding Interactions: The Human Purinergic Platelet Receptor

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In Silico Study of Recognition between Aβ₄₀ and Aβ₄₀ Fibril Surfaces: An N-Terminal Helical Recognition Motif and Its Implications for Inhibitor Design