Research Article: Molecular dynamics simulation of aluminium binding to amyloid-β and its effect on peptide structure

Date Published: June 11, 2019

Publisher: Public Library of Science

Author(s): Matthew Turner, Shaun T. Mutter, Oliver D. Kennedy-Britten, James A. Platts, Eugene A. Permyakov.

http://doi.org/10.1371/journal.pone.0217992

Abstract

Multiple microsecond-length molecular dynamics simulations of complexes of Al(III) with amyloid-β (Aβ) peptides of varying length are reported, employing a non-bonded model of Al-coordination to the peptide, which is modelled using the AMBER ff14SB forcefield. Individual simulations reach equilibrium within 100 to 400 ns, as determined by root mean square deviations, leading to between 2.1 and 2.7 μs of equilibrated data. These reveal a compact set of configurations, with radius of gyration similar to that of the metal free peptide but larger than complexes with Cu, Fe and Zn. Strong coordination through acidic residues Glu3, Asp7 and Glu11 is maintained throughout all trajectories, leading to average coordination numbers of approximately 4 to 5. Helical conformations predominate, particularly in the longer Al-Aβ40 and Al-Aβ42 peptides, while β-strand forms are rare. Binding of the small, highly charged Al(III) ion to acidic residues in the N-terminus strongly disrupts their ability to engage in salt bridges, whereas residues outside the metal binding region engage in salt bridges to similar extent to the metal-free peptide, including the Asp23-Lys28 bridge known to be important for formation of fibrils. High helical content and disruption of salt bridges leads to characteristic tertiary structure, as shown by heat maps of contact between residues as well as representative clusters of trajectories.

Partial Text

Alzheimer’s disease (AD) is a devastating neurodegenerative condition that poses major healthcare challenges. Significant hallmarks of AD include the death of neurons and their connections in addition to the presence of insoluble plaques and neurofibrillary tangles. The amyloid hypothesis suggests that aggregation of the amyloid-β (Aβ) peptide into soluble oligomers and senile plaques is the main driver of AD.[1,2] In contrast, the metal ion hypothesis suggests that disruption of metal ion homeostasis promotes Aβ aggregation and onset of AD.[3–5] The primary focus of the metal ion hypothesis has been on naturally occurring metals, particularly Cu(II),[6–11] Zn(II),[12–15] and Fe(II),[16–20] though studies over several decades have also linked aluminium(III) with the development of AD.[21–25] Aluminium does not naturally occur in human biology, but Al(III) is a known neurotoxin that interacts with a range of metal-binding proteins, influencing the homeostasis of other ions. Specifically, Exley et al showed that aluminium induces conformational changes in Aβ,[26] that have been linked to inhibition of Aβ degradation as well as promotion of aggregation,[27] and formation of reactive oxygen species.[28]

Aβ peptides were constructed in extended conformations in MOE[33] with appropriate residue protonation states for physiological pH. Al(III) was coordinated via Glu3, Asp7 and Glu11, as identified by Mujika et al.[31] Structures were subjected to short LowMode[34] conformational searches to obtain reasonable starting structures. MD simulations were performed using the AMBER16 package.[35] The AMBER ff14SB forcefield parameter set[36] was used to model all standard amino acid residues; the non-bonded MCPB.py[37] approach was used for Al(III), enabling the metal ion to sample different coordination sites during simulation. RESP charges for the metal-coordinating regions were obtained from B3LYP/6-31G(d) calculations using Gaussian09.[38] This combination of functional and basis set matches that used by Li and Merz in developing and testing MCPB.py, and is recommended for compatibility with AMBER-style forcefields. The Al(III) ion was fixed as a 3+ charge, with radius of 1.37 Å.

Three separate 1 μs simulations of Al-Aβ16 (denoted A, B and C) were performed, starting from the same minimised conformation, but with different random velocities sampled from the Maxwell-Boltzmann distribution at 310 K. Similarly, five 1 μs simulations (A-E) were performed for both Al-Aβ40 and Al-Aβ42. Root mean square displacement (RMSD) relative to the starting structures was used as a measure of equilibration.[9,46] Fig 1 shows RMSD plots for all backbone atoms in Al-Aβ16, Al-Aβ40 and Al-Aβ42 systems, respectively, relative to their respective minimised structures. Equilibration points are shown in Table 1; Al-Aβ16 simulations reach stable values rapidly, while Al-Aβ40 and Al-Aβ42 simulations take longer to equilibrate. All analysis reported is taken from data extracted from frames after each equilibration point, averaged over the relevant simulations, such that trajectories of 2.7, 2.1 and 2.4 μs are available for further analysis. Averages and standard deviations of RMSD are shown in Table 2, further illustrating the equilibration of the trajectories: standard deviations of around 1 Å are found across the multiple microsecond trajectories. As might be expected, the larger peptides reach larger average values of RMSD than the N-terminal fragment, with a notable increase for the 42-residue peptide over the 40-residue one.

Microsecond timescale simulations of Al(III) bound to Aβ16, Aβ40 and Aβ42 in implicit aqueous solvent reveal a picture of a flexible, unstructured set of systems in which no one structure dominates. Equilibration, as judged by time evolution of RMSD, is fast for the smallest peptide, but takes several hundred nanoseconds for the larger ones. The non-bonded model of ion coordination employed allows the ion to sample numerous ligating sites within the peptide, but in fact we find that coordination to the N-terminal acidic residues Glu3, Asp7 and Glu11 is highly stable across over 2 μs of MD trajectories for each system. This leads to average coordination number in the inner coordination shell for Al of around 4, with a further 2 to 3 oxygens in the outer coordination shell.

Our data show that atomistic molecular dynamics simulations of Al(III) bound to amyloid-β peptides of different lengths, using a non-bonded model of ion coordination, equilibrate in timescales of several hundred nanoseconds. Collating post-equilibration trajectories from multiple microsecond MD runs indicates stable binding of Al(III) to acidic residues in the N-terminal region of Aβ and an average coordination number of around 4. The metal binding residues Glu3, Asp7 and Glu11 are typically relatively immobile, while N- and C-terminal residues are highly flexible. This flexibility still allows significant quantities of secondary structure to develop: the longest peptide, Aβ42, reaches almost 50% helical content, this being concentrated largely in residues 11–20 and 26–36. Salt bridges are strongly affected by the presence of the ion, most notably as coordination to acidic N-terminal residues severely retards their ability to form salt bridges. This, along with hydrogen bond patterns that reflect the rather high helical content, leads to characteristic patterns in tertiary structure in which stable contacts between salt bridged pairs, as well as residues bound to Al(III) are apparent. Overall, we find that coordination of Al(III) to the N-terminus of Aβ has a major impact in the structure and dynamics of the peptide, inducing significant helical content, reducing the impact of salt bridges, reducing the flexibility of binding residues and increasing that of terminal residues.

 

Source:

http://doi.org/10.1371/journal.pone.0217992

 

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