Dissecting the role of glutamine in seeding peptide aggregation

Abstract

Poly glutamine and glutamine-rich peptides play a central role in a plethora of pathological aggregation events. However, biophysical characterization of soluble oligomers -the most toxic species involved in these processes- remains elusive due to their structural heterogeneity and dynamical nature. Here, we exploit the high spatio-temporal resolution of coarse-grained simulations as a computational microscope to characterize the aggregation propensity and morphology of a series of polyglutamine and glutamine-rich peptides. Comparative analysis of ab-initio aggregation pinpointed a double role for glutamines. In the first phase, glutamines mediate seeding by pairing monomeric peptides, which serve as primers for higher-order nucleation. According to the glutamine content, these low molecular-weight oligomers may then proceed to create larger aggregates. Once within the aggregates, buried glutamines continue to play a role in their maturation by optimizing solvent-protected hydrogen bonds networks.

Keywords: Coarse grained modelling; Molecular dynamics; Peptide aggregation; Polyglutamine diseases; Toxic oligomers.

Graphical abstract

Fig. 1

Aggregation analysis for Q4 peptides. (A) Mean cluster size measured throughout 3 μs MD simulations. (B) Number of clusters classified by size. (C) Global percentage of secondary structure. Representative structures of Q4 dimers and trimers are drawn with a cartoon representation. Side chains are colored by element.

Fig. 2

Aggregation of Q11 peptides. (First row) Number of clusters classified by size. The analysis is restricted to the first 3 μs of the trajectory where all the association-dissociation events occur. (Second row) Mean cluster size measured throughout 5 μs of MD simulations. Final oligomeric structures are represented in cartoons. (Third row) Time evolution of the percentage of secondary structure content. Results correspond to Q11 (first column), Q11 + Q5 (second column) and Q11 + QEQQQ (third column).

Fig. 3

Aggregation of Q36 peptides. Snapshots of the association events for each simulated replica are shown in the left grid. Q36 monomers first contacts always involved interactions with preformed double stranded regions of their counterparts. Secondary structure plots are shown in the right panels.

Fig. 4

Aggregation analysis for the p31–43 peptide (A), C-peptide at pH = 7 (B) and C-peptide at pH = 3.2 (C). Top row: Number of clusters classified by size, notice that the y axis is represented in a logarithmic scale. For (A) and (C) the analysis is focused on the first μs of simulation where most of the association events occur. Second row: Mean cluster size is measured throughout 5 μs of MD simulations. For (A) and (C) vertical lines divide the graph in three phases, according to the aggregation rate. The aminoacidic sequence of p31–43 and C-peptide are shown for reference. Third row: Number of interchain contacts defined by residue. Leucine, phenylalanine and tyrosine contacts are grouped as hydrophobic; aspartate and glutamate as anionic. Lower row: Glutamine’s solvent accessible surface area; values are normalized by the initial SASA, when peptides are in their monomeric forms.

Fig. 5

Schematic description of the different aggregation scenarios studied. The time evolution of the aggregation is classified by peptide length for poly-Q peptides (left grid) and by glutamine content for heterogeneous peptides (right grid). Q4 showed low cluster sizes, presenting a monomer–dimer-trimer equilibrium through the simulation time. Increasing the peptide length to Q11 changed the aggregation behavior, linear peptides initially forming β-sheets then progress to β-sandwiches highly stabilized by side-chain contacts. Q36 presented loops, aggregating into amyloidogenic-like structures. In the case of heterogeneous peptides, Q-rich peptides (e.g. p31–43) formed large amorphous aggregates stabilized by internal glutamine-mediated contacts. Meanwhile Q-poor peptides (e.g. C-peptide, pH = 7) presented a hydrophobic driven aggregation limited by electrostatic repulsion resulting in intermediate cluster sizes.