Stability of Iowa mutant and wild type Aβ-peptide aggregates

Abstract

Recent experiments indicate a connection between the structure of amyloid aggregates and their cytotoxicity as related to neurodegenerative diseases. Of particular interest is the Iowa Mutant, which causes early-onset of Alzheimer's disease. While wild-type Amyloid β-peptides form only parallel beta-sheet aggregates, the mutant also forms meta-stable antiparallel beta sheets. Since these structural variations may cause the difference in the pathological effects of the two Aβ-peptides, we have studied in silico the relative stability of the wild type and Iowa mutant in both parallel and antiparallel forms. We compare regular molecular dynamics simulations with such where the viscosity of the samples is reduced, which, we show, leads to higher sampling efficiency. By analyzing and comparing these four sets of all-atom molecular dynamics simulations, we probe the role of the various factors that could lead to the structural differences. Our analysis indicates that the parallel forms of both wild type and Iowa mutant aggregates are stable, while the antiparallel aggregates are meta-stable for the Iowa mutant and not stable for the wild type. The differences result from the direct alignment of hydrophobic interactions in the in-register parallel oligomers, making them more stable than the antiparallel aggregates. The slightly higher thermodynamic stability of the Iowa mutant fibril-like oligomers in its parallel organization over that in antiparallel form is supported by previous experimental measurements showing slow inter-conversion of antiparallel aggregates into parallel ones. Knowledge of the mechanism that selects between parallel and antiparallel conformations and determines their relative stability may open new avenues for the development of therapies targeting familial forms of early-onset Alzheimer's disease. © 2014 AIP Publishing LLC.

The Structural Basis of Oncogenic Mutations G12, G13 and Q61 in Small GTPase K-Ras4B

“…Over the years molecular dynamics (MD) simulations have been carried out to understand the structure and function of wild-type (WT) and mutated proteins 25 26 27 , particularly those cancer-related proteins 28 29 30 31 32 33 . These have shown that the landscape of the proteins changes following mutations 34 35 36 .…”

Effect of single point mutations in a form of systemic amyloidosis

“…While the averaged RMSD, radius of gyration, and solvent-accessible surface area values are similar for all mass-reduced systems, and are within the error bars of the reference system, there is a systematic increase in SASA and RMSD values with decreasing k. Comparing these differences as function of the scaling parameter, we selected k 5 0.5 s for our simulations as this value allows us to be consistent with our previous work. 26 …”

“…The selected snapshots serve as a start point for three independent trajectories (with different velocity distributions) of 50 ns length that relies on the Amber12 software and the ff99SB 37 forcefield. 26,53 Approximations of the DG values are calculated from the last 10 ns of these trajectories and exclude contributions from the conformational entropy. This is an often used approximation in amyloid studies 29,30 and partially motivated by the difficulties in estimating the conformational entropy from a normal mode analysis using a rigid-rotor harmonic-oscillator ideal-gas approximation of the system that leads to large systematic and statistical errors.…”

Oncogenic G12D mutation alters local conformations and dynamics of K-Ras

“…G12 is the most frequently mutated residue (89%), which most prevalently mutates to aspartate (G12D, 36%) followed by valine (G12V, 23%) and cysteine (G12C, 14%) 3,10 . This residue is located at the protein active site, which consists of a phosphate binding loop (P-loop, residues 10-17) and switch I (SI, residues [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] and II (SII, residues 60-74) regions. The active site residues are bound to the phosphate groups of GTP and are responsible for the GTPase function of K-Ras.…”

“…Recent studies suggest that utilizing protein dynamics data is a successful approach for understanding the effects of mutations on the structure, dynamics and function of proteins [34][35][36] . Particularly in drug discovery, dynamics data from oncogenic proteins 22,[37][38][39][40] have helped identify cryptic or allosteric binding sites [41][42][43][44] .…”