Molecular events during the early stages of aggregation of GNNQQNY: An all atom MD simulation study of randomly dispersed peptides

Aggregation of an Amyloidogenic Peptide on Gold Surfaces

Solid surfaces have been shown to affect the aggregation and assembly of many biomolecular systems. One important example is the formation of protein fibrils, which can occur on a range of biological and synthetic surfaces. The rate of fibrillation depends on both the protein structure and the surface chemistry, with the different molecular and oligomer structures adopted by proteins on surfaces likely to be crucial. In this paper, the aggregation of the model amyloidogenic peptide, Aβ(16-22), corresponding to a hydrophobic segment of the amyloid beta protein on a gold surface is studied using molecular dynamics simulation. Previous simulations of this peptide on gold surfaces have shown that it adopts conformations on surfaces that are quite different from those in bulk solution. These simulations show that this then leads to significant differences in the oligomer structures formed in solution and on gold surfaces. In particular, oligomers formed on the surface are low in beta-strands so are unlike the structures formed in bulk solution. When oligomers formed in solution adsorb onto gold surfaces they can then restructure themselves. This can then help explain the inhibition of Aβ(16-22) fibrillation by gold surfaces and nanoparticles seen experimentally.

Exploring the aggregation of amyloid-β 42 through Monte Carlo simulations

Coarse-grained Monte Carlo simulations are performed for a disordered protein, amyloid-β 42 to identify the interactions and understand the mechanism of its aggregation. A statistical potential is developed from a selected dataset of intrinsically disordered proteins, which accounts for the respective contributions of the bonded and non-bonded potentials. While, the bonded potential comprises the bond, bend, and dihedral constraints, the nonbonded interactions include van der Waals interactions, hydrogen bonds, and the two-body potential. The two-body potential captures the features of both hydrophobic and electrostatic interactions that brings the chains at a contact distance, while the repulsive van der Waals interactions prevent them from a collapse. Increased two-body hydrophobic interactions facilitate the formation of amorphous aggregates rather than the fibrillar ones. The formation of aggregates is validated from the interchain distances, and the total energy of the system. The aggregate is structurally characterized by the root-mean-square deviation, root-mean-square fluctuation and the radius of gyration. The aggregates are characterized by a decrease in SASA, an increase in the non-local interactions and a distinct free energy minimum relative to that of the monomeric state of amyloid-β 42. The hydrophobic residues help in nucleation, while the charged residues help in oligomerization and aggregation.

Computational and Experimental Evaluation of the Stability of a GLP-1-like Peptide in Ethanol–Water Mixtures

Aggregation resulting from the self-association of peptide molecules remains a major challenge during preformulation. Whereas certain organic solvents are known to promote aggregation, ethanol (EtOH) is capable of disrupting interactions between peptide molecules. It is unclear whether it is beneficial or counterproductive to include EtOH in formulations of short peptides. Here, we employed molecular dynamics simulations using the DAFT protocol and MARTINI force field to predict the formation of self-associated dimers and to estimate the stability of a GLP-1-like peptide (G48) in 0–80% aqueous EtOH solutions. Both simulation and experimental data reveal that EtOH leads to a remarkable increase in the conformational stability of the peptide when stored over 15 days at 27 °C. In the absence of EtOH, dimerisation and subsequent loss in conformational stability (α-helix → random coil) were observed. EtOH improved conformational stability by reducing peptide–peptide interactions. The data suggest that a more nuanced approach may be applied in formulation decision making and, if the native state of the peptide is an α-helix organic solvent, such as EtOH, may enhance stability and improve prospects of long-term storage.

Clustering and Fibril Formation during GNNQQNY Aggregation: A Molecular Dynamics Study

The precise kinetic pathways of peptide clustering and fibril formation are not fully understood. Here we study the initial clustering kinetics and transient cluster morphologies during aggregation of the heptapeptide fragment GNNQQNY from the yeast prion protein Sup35. We use a mid-resolution coarse-grained molecular dynamics model of Bereau and Deserno to explore the aggregation pathways from the initial random distribution of free monomers to the formation of large clusters. By increasing the system size to 72 peptides we could follow directly the molecular events leading to the formation of stable fibril-like structures. To quantify those structures we developed a new cluster helicity parameter. We found that the formation of fibril-like structures is a cooperative processes that requires a critical number of monomers, M⋆≈25, in a cluster. The terminal tyrosine residue is the structural determinant in the formation of helical fibril-like structures. This work supports and quantifies the two-step aggregation model where the initially formed amorphous clusters grow and, when they are large enough, rearrange into mature twisted structures. However, in addition to the nucleated fibrillation, growing aggregates undergo further internal reorganization, which leads to more compact structures of large aggregates.

Aggregation kinetics of short peptides: All-atom and coarse-grained molecular dynamics study

Peptides can aggregate into ordered structures with different morphologies. The aggregation mechanism and evolving structures are the subject of intense research. In this paper we have used molecular dynamics to examine the sequence-dependence of aggregation kinetics for three short peptides: octaalanine (Ala8), octaasparagine (Asn8), and the heptapeptide GNNQQNY (abbreviated as GNN). First, we compared the aggregation of 20 randomly distributed peptides using the coarse-grained MARTINI force field and the atomistic OPLS-AA force field. We found that the MARTINI and OPLS-AA aggregation kinetics are similar for Ala8, Asn8, and GNN. Second, we used the MARTINI force field to study the early stages of aggregation kinetics for a larger system with 72 peptides. In the initial stage of aggregation small clusters grow by monomer addition. In the second stage, when the free monomers are depleted, the dominant cluster growth path is cluster-cluster coalescence. We quantified the aggregation kinetics in terms of rate equations. Our study shows that the initial aggregation kinetics are similar for Ala8, Asn8, and GNN but the molecular details can be different, especially for MARTINI Ala8. We hypothesize that peptide aggregation proceed in two steps. In the first step amorphous aggregates are formed, and then, in the second step, they reorganize into ordered structures. We conclude that sequence-specific differences show up in the second step of aggregation.

Top-down Multiscale Approach To Simulate Peptide Self-Assembly from Monomers

Modeling peptide assembly from monomers on large time and length scales is often intractable at the atomistic resolution. To address this challenge, we present a new approach which integrates coarse-grained (CG), mixed-resolution, and all-atom (AA) modeling in a single simulation. We simulate the initial encounter stage with the CG model, while the further assembly and reorganization stages are simulated with the mixed-resolution and AA models. We have implemented this top-down approach with new tools to automate model transformations and to monitor oligomer formations. Further, a theory was developed to estimate the optimal simulation length for each stage using a model peptide, melittin. The assembly level, the oligomer distribution, and the secondary structures of melittin simulated by the optimal protocol show good agreement with prior experiments and AA simulations. Finally, our approach and theory have been successfully validated with three amyloid peptides (β-amyloid 16-22, GNNQQNY fragment from the yeast prion protein SUP35, and α-synuclein fibril 35-55), which highlight the synergy from modeling at multiple resolutions. This work not only serves as proof of concept for multiresolution simulation studies but also presents practical guidelines for further self-assembly simulations at more physically and chemically relevant scales.

On the Applicability of Force Fields To Study the Aggregation of Amyloidogenic Peptides Using Molecular Dynamics Simulations

Molecular dynamics simulations play an essential role in understanding biomolecular processes such as protein aggregation at temporal and spatial resolutions which are not attainable by experimental methods. For a correct modeling of protein aggregation, force fields must accurately represent molecular interactions. Here, we study the effect of five different force fields on the oligomer formation of Alzheimer's Aβ_16-22 peptide and two of its mutants: Aβ_16-22(F19V,F20V), which does not form fibrils, and Aβ_16-22(F19L) which forms fibrils faster than the wild type. We observe that while oligomer formation kinetics depends strongly on the force field, structural properties, such as the most relevant protein-protein contacts, are similar between them. The oligomer formation kinetics obtained with different force fields differ more from each other than the kinetics between aggregating and nonaggregating peptides simulated with a single force field. We discuss the difficulties in comparing atomistic simulations of amyloid oligomer formation with experimental observables.

Inhibitory effect of hydrophobic fullerenes on the β-sheet-rich oligomers of a hydrophilic GNNQQNY peptide revealed by atomistic simulations

Fullerenes suppress fibril-like β-sheet oligomers by interacting strongly with the nonpolar aliphatic groups of polar residues of GNNQQNY peptide, thus inhibit peptide aggregation.

Advances in the Simulation of Protein Aggregation at the Atomistic Scale

Protein aggregation into highly structured amyloid fibrils is associated with various diseases including Alzheimer's disease, Parkinson's disease, and type II diabetes. Amyloids can also have normal biological functions and, in the future, could be used as the basis for novel nanoscale materials. However, a full understanding of the physicochemical forces that drive protein aggregation is still lacking. Such understanding is crucial for the development of drugs that can effectively inhibit aberrant amyloid aggregation and for the directed design of functional amyloids. Atomistic simulations can help understand protein aggregation. In particular, atomistic simulations can be used to study the initial formation of toxic oligomers which are hard to characterize experimentally and to understand the difference in aggregation behavior between different amyloidogenic peptides. Here, we review the latest atomistic simulations of protein aggregation, concentrating on amyloidogenic protein fragments, and provide an outlook for the future in this field.