Defining a Physical Basis for Diversity in Protein Self-Assemblies Using a Minimal Model

Self-assembly of proteins into ordered, fibrillar structures is a commonly observed theme in biology. It has been observed that diverse set of proteins (e.g., alpha-synuclein, insulin, TATA-box binding protein, Sup35, p53), independent of their sequence, native structure, or function could self-assemble into highly ordered structures known as amyloids. What are the crucial features underlying amyloidogenesis that make it so generic? Using coarse-grained simulations of peptide self-assembly, we argue that variation in two physical parametersbending stiffness of the polypeptide and strength of intermolecular interactionscan give rise to many of the structural features typically associated with amyloid self-assembly. We show that the interplay between these two factors gives rise to a rich phase diagram displaying high diversity in aggregated states. For certain parameters, we find a bimodal distribution for the order parameter implying the coexistence of ordered and disordered aggregates. Our findings may explain the experimentally observed variability including the “off-pathway” aggregated structures. Further, we demonstrate that sequence-dependence and protein-specific signatures could be mapped to our coarse-grained framework to study self-assembly behavior of realistic systems such as the STVIIE peptide and Aβ42. The work also provides certain guiding principles that could be used to design novel peptides with desired self-assembly properties, by tuning a few physical parameters

Defining a Physical Basis for Diversity in Protein Self-Assemblies Using a Minimal Model

Self-assembly of proteins into ordered, fibrillar structures is a commonly observed theme in biology. It has been observed that diverse set of proteins (e.g., alpha-synuclein, insulin, TATA-box binding protein, Sup35, p53), independent of their sequence, native structure, or function could self-assemble into highly ordered structures known as amyloids. What are the crucial features underlying amyloidogenesis that make it so generic? Using coarse-grained simulations of peptide self-assembly, we argue that variation in two physical parametersbending stiffness of the polypeptide and strength of intermolecular interactionscan give rise to many of the structural features typically associated with amyloid self-assembly. We show that the interplay between these two factors gives rise to a rich phase diagram displaying high diversity in aggregated states. For certain parameters, we find a bimodal distribution for the order parameter implying the coexistence of ordered and disordered aggregates. Our findings may explain the experimentally observed variability including the “off-pathway” aggregated structures. Further, we demonstrate that sequence-dependence and protein-specific signatures could be mapped to our coarse-grained framework to study self-assembly behavior of realistic systems such as the STVIIE peptide and Aβ42. The work also provides certain guiding principles that could be used to design novel peptides with desired self-assembly properties, by tuning a few physical parameters

Snapshots at various time steps of ACTH-β-end simulation system showing hetero-oligomerization.

<p>The hetero-trimer consisting of two β-end and one ACTH is stable up to end of 20 ns simulation. β-end A, β-end B, ACTH C and ACTH D are represented by blue, yellow, green and purple color, respectively.</p

Characterization of Amyloid Formation by Glucagon-Like Peptides: Role of Basic Residues in Heparin-Mediated Aggregation

Glycosaminoglycans (GAGs) have been reported to play a significant role in amyloid formation of a wide range of proteins/peptides either associated with diseases or native biological functions. The exact mechanism by which GAGs influence amyloid formation is not clearly understood. Here, we studied two closely related peptides, glucagon-like peptide 1 (GLP1) and glucagon-like peptide 2 (GLP2), for their amyloid formation in the presence and absence of the representative GAG heparin using various biophysical and computational approaches. We show that the aggregation and amyloid formation by these peptides follow distinct mechanisms: GLP1 follows nucleation-dependent aggregation, whereas GLP2 forms amyloids without any significant lag time. Investigating the role of heparin, we also found that heparin interacts with GLP1, accelerates its aggregation, and gets incorporated within its amyloid fibrils. In contrast, heparin neither affects the aggregation kinetics of GLP2 nor gets embedded within its fibrils. Furthermore, we found that heparin preferentially influences the stability of the GLP1 fibrils over GLP2 fibrils. To understand the specific nature of the interaction of heparin with GLP1 and GLP2, we performed all-atom MD simulations. Our in silico results show that the basic-nonbasic-basic (B-X-B) motif of GLP1 (K28-G29-R30) facilitates the interaction between heparin and peptide monomers. However, the absence of such a motif in GLP2 could be the reason for a significantly lower strength of interaction between GLP2 and heparin. Our study not only helps to understand the role of heparin in inducing protein aggregation but also provides insight into the nature of heparin–protein interaction

MD simulation of β-end system.

<p>All-atom MD simulation for the duration of 20 ns was performed in explicit solvent using four β-end. A) Snapshots indicating the initial (left) and final (right) states of the β-end system. B) Plot of distances between center of masses (Dij) of peptides within the β-end simulation against time. C) Contact map showing various residues in contact between peptides at t = 20 ns. Significant contacts are observed between amino acid residues in β-end A, β-end B and β-end D. D) A schematic depicting the various h-bonds observed between the peptides during the β-end simulation. The schematic showing C-terminus of β-end D is in contact with N-terminus of β-end A whereas C-terminus of β-end B is in contact with C-terminus β-end A. A salt bridge between E31 of β-end A and K28 of β-end B is represented by red dotted line.</p

Aggregation and colocalization of ACTH and β-end.

<p>A) Fluorescence photomicrographs of the anterior (A–C) and intermediate (D–F) lobes of the pituitary gland showing ACTH (A, D), β-end (B, E), and ACTH-β-end colocalized (C, F) cells. Note that presence of several double-labeled cells (arrows) in anterior as well as intermediate lobes. Scale bar = 50 µm. B) EM images of ACTH and β-end samples incubated for 14 days. The aggregations of the hormones were followed at 37°C at a concentration of 2 mg/ml in the presence of 0.4 mM LMW heparin in 5% D-mannitol (pH 5.5) and PBS under slight agitation. TEM of negative stained samples was performed. Scale bars, 500 nm.</p

MD simulation of ACTH-β-end system.

<p>All-atom MD simulation for the duration of 20 ns was performed in explicit solvent using two ACTH and two β-end. <b>A</b>) Snapshots at the left-hand side indicating the initial configuration (t = 0 ns) and at right-hand side indicating the final configuration (t = 20 ns). <b>B</b>) Plot of distances between center of masses (Dij) of peptides against time indicating close proximity of peptides A, B and D. <b>C</b>) Time progression of contact regions between β-end A and β-end B. At any given time, the regions indicated by red lines of β-end B is in contact with regions indicated by blue lines of β-end A. Similarly, the regions indicated by black lines of β-end B is in contact with regions indicated by green lines of β-end A. The first contact pair (red and blue) is seen for the entire duration of the simulation whereas the second contact pair (black and green) appears only towards end of the simulation. <b>D</b>) Time progression of contact regions between ACTH D and β-end B. At any given time, the regions indicated by red lines of ACTH D is in contact with regions indicated by blue lines of β-end B. Similarly, the regions indicated by black lines of β-end B is in contact with regions indicated by green lines of ACTH D. The first contact pair (red and blue) is seen for the initial part of the simulation whereas the second contact pair (green and black) appears only towards end of the simulation. Additional contacts are also observed for certain duration between the region of β-end B (aqua line) and ACTH D (pink line).</p

Effect of N-terminal deletion on β-end aggregation.

<p>A) CD spectra of full-length β-end and β-end (6–31) at 2 mg/ml concentration in 5% D-mannitol, 0.01% sodium azide, pH 5.5 in presence and absence of 400 µM of LMW heparin. In the absence of heparin, both peptides remain mostly unstructured up to one week of incubation at 37°C. In presence of heparin, the full-length β-end showing structural transition from α-helix at d0 to mostly β-sheet at d4; while the β-end (6–31), which was in a mixed conformation of random coil and helix at d0, showing mostly α-helical conformation at d4. Both peptides show mostly β-sheet conformation at d7. (B) The ThT fluorescence study of one week incubated sample in presence of heparin showing less ThT binding of β-end (6–31) compared to full-length β-end. C) EM studies of 7 days incubated samples showing numerous amyloid-like fibrils for full-length β-end and fewer fibrils for β-end (6–31) in presence of heparin. Any amyloid-like fibrils are not observed in absence of heparin for both the peptides. D) AFM images of the full-length β-end and β-end (6–31) in presence and absence of heparin after a 7-day incubation showing more number of fibrils in case of the full length β-end sample as compared to the β-end (6–31) sample. Scale bars are of 500 nm.</p

Analysis of the interpeptide interactions in ACTH-β-end system.

<p>A) Contact map showing various residues in contact within the ACTH-β-end simulation box at t = 20 ns. B) Schematic representation of the stable hydrogen bonds observed between the peptides during the simulation.</p

MD simulation of ACTH system.

<p>A) Snapshots at the left-hand side indicating initial state (t = 0 ns) and at the right-hand side indicating final state (t = 20 ns) of the simulation. B) Plot of distances between center of masses (Dij) of peptides within the ACTH simulation against time showing large interpeptide distances during simulation. C) Contact map at t = 20 ns snapshot showing residues in contact. Negligible interpeptide contacts are observed suggesting that peptides are not self-assembling <i>in silico</i>.</p