Letter pubs.acs.org/JPCL
Cite This: J. Phys. Chem. Lett. 2019, 10, 1594−1599
Multiscale Aggregation of the Amyloid Aβ16−22 Peptide: From Disordered Coagulation and Lateral Branching to Amorphous Prefibrils Mara Chiricotto,† Simone Melchionna,‡ Philippe Derreumaux,† and Fabio Sterpone*,† †
Laboratoire de Biochimie Théorique, IBPC, CNRS UPR9080, Univ. Paris Diderot, Sorbonne Paris Cité, 13 rue Pierre et Marie Curie, 75005 Paris, France ‡ ISC−CNR, Dipartimento di Fisica, Universita Sapienza, P.le A. Moro 5, 00185 Rome, Italy J. Phys. Chem. Lett. Downloaded from pubs.acs.org by IOWA STATE UNIV on 03/22/19. For personal use only.
S Supporting Information *
ABSTRACT: In this work we investigate the multiscale dynamics of the aggregation process of an amyloid peptide, Aβ16−22. By performing massive coarse-grained simulations at the quasi-atomistic resolution and including hydrodynamic effects, we followed the formation and growth of a large elongated aggregate and its slow structuring. The elongation proceeds via a two-step nucleation mechanism with disordered aggregates formed initially and subsequently fusing to elongate the amorphous prefibril. A variety of coagulation events coexist, including lateral growth. The latter mechanism, sustained by long-range hydrodynamics correlations, actually can create a large branched structure spanning a few tens of nanometers. Our findings confirm the experimental hypothesis of a critical contribution of lateral growth to the amyloid aggregation kinetics and the capability of our model to sample critical structures like prefibril hosting annular pores.
rotein aggregation into fibrils with high content of β-sheet secondary structures is a hallmark of many neurodegenerative diseases.1,2 In Alzheimer’s disease, the formation of fibrils by amyloid-β peptides Aβ1−40 and Aβ1−42 has been associated with the loss of neuronal efficiency. Despite intense experimental efforts3−7 to microscopically characterize this aggregation process, a complete picture is still missing. Namely, two main questions are discussed. The first one relates to the nucleation process. It is debated whether the formation of structured fibrils occurs in one or two nucleation steps.8−14 Experimentally, it was reported that prion and amyloid proteins prefer the two-step mechanism8,13 with the monomers aggregating first in stable disordered oligomers that subsequently merge into a structured fibril. A second aspect to consider is the self-catalytic role of the formed fibrils.15 Theoretical and experimental studies have shown that secondary lateral nucleation on the surface of an existing fibril impacts the aggregation kinetics.16,17 Computer simulations at a different level of modeling therefore play a crucial role in supporting experiments where microscopic information is lacking. 18−21 For instance, simplified implicit solvent models can follow the early steps of aggregation.9,11,12,22 In these low-resolution approaches, despite the richness of the obtained information, the models generally lack atomistic detail and neglect solvent-mediated correlations in the aggregation dynamics, a key ingredient of many-body diffusive processes.23−25 In this work we describe how an innovative computer simulation scheme allows studying the multiscale aggregation
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© XXXX American Chemical Society
process of an amyloid peptide system of unprecedented size at quasi-atomistic resolution. Namely, we study the aggregation of the fragment Aβ16−22 that represents the central hydrophobic core of the amyloid Aβ peptide, an essential stretch in the interaction with neuron receptor LilrB,26 and that forms fibrils in vitro.27 For this purpose, we have employed the lattice Boltzmann molecular dynamics (LBMD) technique that naturally includes hydrodynamic interactions (HI) in the implicit solvent simulations28,29 (see the Supporting Information). The molecular component of the system is modeled by the quasi-atomistic OPEP coarse-grained force-field30,31 that allows conformational changes and samples the formation of secondary structures in peptide aggregates. The inclusion of HI is essential to account for solvent-mediated correlations during aggregation because these affect the monomer/oligomer diffusivity as well as the size fluctuations of the formed aggregates.23,25 The Multiscales of Aβ16−22 Aggregation. Starting from a uniform distribution and random orientation, 103 peptides were placed in a simulation box of edge length L = 30 nm matching a concentration c = 60 mM. The aggregation process at the microsecond time scale, tsim = 0.7 μs, was monitored over time by computing the number and the size of the peptide clusters formed during the evolution. Received: February 13, 2019 Accepted: March 20, 2019 Published: March 20, 2019 1594
DOI: 10.1021/acs.jpclett.9b00423 J. Phys. Chem. Lett. 2019, 10, 1594−1599
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body of the elongated prefibril has a section that can vary between 1 and 5 nm. At the level of the thinner section, weak cohesive forces cause fragmentation events. Moreover, during elongation, branched structures are visible and can be related to later second nucleation. Finally, and interestingly, when the prefibril growth reaches the steady regime (t > 350 ns) a large pore of size 3−7 nm is formed at one edge. Oligomeric Formation. We focus here on the fast aggregation, and in order to explore concentration effects, we produced auxiliary simulations at c = 40 mM and c = 30 mM (see the Supporting Information). At each concentration, four independent simulations were performed. At a short time scale, the peptides start forming oligomeric species, inducing the decay of the number of free monomers. As shown in Figure 3a, the decay has two characteristic times: a fast one, τ1 ≈ 2−10 ns, that concerns the majority of peptides, and a much slower one, τ2 ≈ 102 ns, where the residual free entities fuse into large oligomers. At c = 60 mM, after about 100 ns, only 2% of the peptides is monomeric. For 60 mM and 40 mM, the decay of the free monomer in solution is identical but becomes twice slower at c = 30 mM (see Table S1). The slower aggregation at 30 mM delays also the first fusion event that causes the discontinuous growth of the largest cluster. While at concentrations 60 and 40 mM the fusion starts approximately at 60 and 80 ns (see arrows in Figure 3b), at 30 mM, it would occur on average at longer times. The size distribution of the oligomers is tracked over a timewindow of 10 ns and is plotted in Figure S1. After a transient phase, the distribution broadens and several states get populated. For small oligomers, visible shoulders are observed at the pentamer and decamer states. A similar distribution was reported in a recent work using the Martini force field but with a smaller number of monomers.32 The observed shoulders and peaks seem to indicate the presence of a critical nucleus of size n ≈ 2−10 to compare to that reported in the literature,11,33 but accurate thermodynamic calculations should be performed (see refs 11 and 33). At longer time scales, the merging of these types of oligomers make visible states of larger size, counting 30, 40, or 60 peptides. At very short times, 100 ns, providing a unique insight on how a prefibril elongates. The elementary steps of the coagulation process are represented in Figure S5. There is no unique mechanism for the prefibril to grow. In fact, we have identified at least three, not mutually exclusive, main channels: (i) a tail/head multiple assembly, (ii) a lateral branching, and (iii) a bridging driven fusion with small clusters linking together larger ones. The elongation mechanism can involve two, three, or four bodies in a coherent manner. The fusion transition completes in a few nanoseconds, and the collapse of the two, or multiple, entities begins when they reach a mutual distance 1 μs), we simplified the representation of the system. The four aggregates were each converted in a single 1597
DOI: 10.1021/acs.jpclett.9b00423 J. Phys. Chem. Lett. 2019, 10, 1594−1599
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ASSOCIATED CONTENT
S Supporting Information *
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jpclett.9b00423.
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Supporting data on aggregation process, annular structure, and hydrodynamic contribution to aggregation (PDF)
AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. ORCID
Philippe Derreumaux: 0000-0001-9110-5585 Fabio Sterpone: 0000-0003-0894-8069 Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS F.S. acknowledge funding from the ERC (FP7/2007-2013) Grant Agreement no. 258748 and support from “Initiative d’Excellence” program from the French State (Grant “DYNAMO”, ANR-11-LABX-0011-01). Part of his work was performed using HPC resources from GENCI (Grants x2016(7)076818 and Idris BigChallenge2015).
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