Structures and Free-Energy Landscapes of the Wild Type and Mutants of the Aβ21–30 Peptide Are Determined by an Interplay between Intrapeptide Electrostatic and Hydrophobic Interactions

Abstract

The initial events in protein aggregation involve fluctuations that populate monomer conformations which lead to oligomerization and fibril assembly. The highly populated structures, driven by a balance between hydrophobic and electrostatic interactions in the protease-resistant wild type Aβ 21-30 -peptide and mutants E22Q (Dutch), D23N (Iowa), and K28N, are analyzed using molecular dynamics simulations. Intra-peptide electrostatic interactions were connected to calculated pK a values that compare well with the experimental estimates. The pK a values of the titratable residues show that E22 and D23 side-chains form salt-bridges only infrequently with the K28 side-chain. Contacts between E22-K28 are more probable in "dried" salt-bridges whereas D23-K28 contacts are more probable in solvated salt-bridges. The strength of the intra-peptide hydrophobic interactions increase as D23N<WT<E22Q<K28A. Free energy profiles and disconnectivity representation of the energy landscapes show that the monomer structures partition into four distinct basins. The hydrophobic interactions cluster the Aβ 21-30 -peptide into two basins, differentiated by the relative position of the DVG(23-25) and GSN(25-27) fragments about the G25 residue. The E22Q mutation increases the population with intact VGSN turn, compared to the WT peptide. The increase in the population of the structures, in the aggregation-prone Basin I in E22Q, that occurs solely due to the difference in charge states between the Dutch mutant and the WT, gives a structural explanation of the somewhat larger aggregation rate in the mutant. The D23N mutation dramatically reduces the intra-peptide interactions. The K28A mutation increases the intra-peptide hydrophobic interactions that promotes population of structures in Basin I and Basin II whose structures are characterized by hydrophobic interaction between V24 and K28 side chains but with well-separated ends of the backbone atoms in the VGSN turn. The intra-peptide electrostatic interactions in the WT and E22Q peptides roughen the free energy surface compared to the K28A peptide. The D23N mutation has a flat free energy surface, corresponding to an increased population of random coil-like structures with weak hydrophobic and electrostatic Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. interactions. We propose that mutations or sequences which enhance the probability of occupying Basin I would promote aggregation of Aβ-peptides. NIH Public Access

Salts drive controllable multilayered upright assembly of amyloid-like peptides at mica/water interface

“…With an aggregate of 1.7 μs of SMD simulations, we found that peptide bindings on hydrophobic (H) sides are significantly more stable than bindings on hydrophilic (P) sides, which is concordant with our previous findings that the hydrophobic interaction dominates the longitudinal growth of GAV-9 nanofilaments on mica (26). This significance of hydrophobic interactions (and lowering of the desolvation penalty) was also emphasized in the studies on charged amyloid peptides aggregation of Thirumalai and coworkers (30,31). The binding free energies are also comparable on the H edges for both the AP and P2 models (ΔG bind ≈ −32 kcal/mol).…”

“…The AP model stabilizes the binding by ∼12 kcal/mol for a single peptide, whereas the P2 model stabilizes binding by only ∼4 kcal/mol. This indicates that the ionic strength (i.e., ∼6 Å of Debye length) might not be enough to screen out electrostatic repulsion in the P2 model between shortdistanced (<5 Å) peptides along the transversal hydrophilic sides (linked by backbone hydrogen bonds), although for the relatively long-distanced (∼10 Å) peptides along the longitudinal direction (due to bulky side chains), the electrostatic repulsion is no longer a major force due to the longer distance and more effective screening; thus, both the AP and P2 models give similar binding affinities due to their comparable hydrophobic interactions (30,31). Our PMF results therefore further support that the AP model is thermodynamically more stable than the P2 model for the upper layers, which largely comes from the aid of a more supportive transversal lateral interaction in its antiparallel arrangement.…”

Characterization of Amyloid β Fibrils with An Aqueous Two-Phase System: Implications of Fibril Formation

“…Much work on surface properties of protein molecules, and the evaluation of hydrophobicity by the hydrophobic fluorescence probe [7] or by thermodynamic approaches [8] has been reported. Conventionally, the summation of hydrophobicity of the constituent amino acids gave the hydrophobicity of the protein as a whole [9], which is not useful in explaining the hydrophobic interaction between proteins as shown in Fig.1.…”

Effect of timescale on energy landscape: Distinction between free-energy landscape and potential of mean force

“…Thus, the stable structures of proteins can be predicted as minima of the free-energy landscape, and it is possible to elucidate specific interactions that give rise to the stable structures [7,[22][23][24]. It is, in a sense, surprising that these two different concepts, one being a dynamical concept of force and the other being a statistical distribution, can be given by the same energy function.…”

“…Its application can be found in the studies of, for example, large molecules, such as clusters [1,2] and proteins [3][4][5][6][7][8][9][10], diffusion in oxide networks [11], fullerene materials [12], formation process of gels and glasses [13], chemical and biological separation through micro-and nanofluidic systems [14], and epigenetics in developmental biology [15][16][17]. Two important roles of the energy landscape can be pointed out.…”