One of the main research topics related to Alzheimer's disease is the aggregation of the amyloid-β peptide, which was shown to follow different pathways for the two major alloforms of the peptide, Aβ40 and the more toxic Aβ42. Experimental studies emphasized that oligomers of specific sizes appear in the early aggregation process in different quantities and might be the key toxic agents for each of the two alloforms. We use transition networks derived from all-atom molecular dynamics simulations to show that the oligomers leading to the type of oligomer distributions observed in experiments originate from compact conformations. Extended oligomers, on the other hand, contribute more to the production of larger aggregates thus driving the aggregation process. We further demonstrate that differences in the aggregation pathways of the two Aβ alloforms occur as early as during the dimer stage. The higher solvent-exposure of hydrophobic residues in Aβ42 oligomers contributes to the different aggregation pathways of both alloforms and also to the increased cytotoxicity of Aβ42.

Triosephosphate isomerase (TIM) is a proficient catalyst of the reversible isomerization of dihydroxyacetone phosphate (DHAP) to d-glyceraldehyde phosphate (GAP), via general base catalysis by E165. Historically, this enzyme has been an extremely important model system for understanding the fundamentals of biological catalysis. TIM is activated through an energetically demanding conformational change, which helps position the side chains of two key hydrophobic residues (I170 and L230), over the carboxylate side chain of E165. This is critical both for creating a hydrophobic pocket for the catalytic base and for maintaining correct active site architecture. Truncation of these residues to alanine causes significant falloffs in TIM’s catalytic activity, but experiments have failed to provide a full description of the action of this clamp in promoting substrate deprotonation. We perform here detailed empirical valence bond calculations of the TIM-catalyzed deprotonation of DHAP and GAP by both wild-type TIM and its I170A, L230A, and I170A/L230A mutants, obtaining exceptional quantitative agreement with experiment. Our calculations provide a linear free energy relationship, with slope 0.8, between the activation barriers and Gibbs free energies for these TIM-catalyzed reactions. We conclude that these clamping side chains minimize the Gibbs free energy for substrate deprotonation, and that the effects on reaction driving force are largely expressed at the transition state for proton transfer. Our combined analysis of previous experimental and current computational results allows us to provide an overview of the breakdown of ground-state and transition state effects in enzyme catalysis in unprecedented detail, providing a molecular description of the operation of a hydrophobic clamp in triosephosphate isomerase.

Conformational changes are crucial for the catalytic action of many enzymes. A prototypical and well-studied example is loop opening and closure in triosephosphate isomerase (TIM), which is thought to determine the rate of catalytic turnover in many circumstances. Specifically, TIM loop 6 “grips” the phosphodianion of the substrate and, together with a change in loop 7, sets up the TIM active site for efficient catalysis. Crystal structures of TIM typically show an open or a closed conformation of loop 6, with the tip of the loop moving ∼7 Å between conformations. Many studies have interpreted this motion as a two-state, rigid-body transition. Here, we use extensive molecular dynamics simulations, with both conventional and enhanced sampling techniques, to analyze loop motion in apo and substrate-bound TIM in detail, using five crystal structures of the dimeric TIM from Saccharomyces cerevisiae. We find that loop 6 is highly flexible and samples multiple conformational states. Empirical valence bond simulations of the first reaction step show that slight displacements away from the fully closed-loop conformation can be sufficient to abolish most of the catalytic activity; full closure is required for efficient reaction. The conformational change of the loops in TIM is thus not a simple “open and shut” case and is crucial for its catalytic action. Our detailed analysis of loop motion in a highly efficient enzyme highlights the complexity of loop conformational changes and their role in biological catalysis.

Metal ions are both ubiquitous to and crucial in biology. In classical simulations, they are typically described as simple van der Waals spheres, making it difficult to provide reliable force field descriptions for them. An alternative is given by nonbonded dummy models, in which the central metal atom is surrounded by dummy particles that each carry a partial charge. While such dummy models already exist for other metal ions, none is available yet for Cu2+ because of the challenge to reproduce the Jahn–Teller distortion. This challenge is addressed in the current study, where, for the first time, a dummy model including a Jahn–Teller effect is developed for Cu2+. We successfully validate its usefulness by studying metal binding in two biological systems: the amyloid-β peptide and the mixed-metal enzyme superoxide dismutase. We believe that our parameters will be of significant value for the computational study of Cu2+-dependent biological systems using classical models.

We have previously performed empirical valence bond calculations of the kinetic activation barriers, ΔG‡calc, for the deprotonation of complexes between TIM and the whole substrate glyceraldehyde-3-phosphate (GAP, 28683550J. Am. Chem. Soc.20171391051410525). We now extend this work to also study the deprotonation of the substrate pieces glycolaldehyde (GA) and GA·HPi [HPi = phosphite dianion]. Our combined calculations provide activation barriers, ΔG‡calc, for the TIM-catalyzed deprotonation of GAP (12.9 ± 0.8 kcal·mol–1), of the substrate piece GA (15.0 ± 2.4 kcal·mol–1), and of the pieces GA·HPi (15.5 ± 3.5 kcal·mol–1). The effect of bound dianion on ΔG‡calc is small (≤2.6 kcal·mol–1), in comparison to the much larger 12.0 and 5.8 kcal·mol–1 intrinsic phosphodianion and phosphite dianion binding energy utilized to stabilize the transition states for TIM-catalyzed deprotonation of GAP and GA·HPi, respectively. This shows that the dianion binding energy is essentially fully expressed at our protein model for the Michaelis complex, where it is utilized to drive an activating change in enzyme conformation. The results represent an example of the synergistic use of results from experiments and calculations to advance our understanding of enzymatic reaction mechanisms.

Growing evidence links neurodegenerative diseases to metal exposure. Aberrant metal ion concentrations have been noted in Alzheimer's disease (AD) brains, yet the role of metals in AD pathogenesis remains unresolved. A major factor in AD pathogenesis is considered to be aggregation of and amyloid formation by amyloid-β (Aβ) peptides. Previous studies have shown that Aβ displays specific binding to Cu(II) and Zn(II) ions, and such binding has been shown to modulate Aβ aggregation. Here, we use nuclear magnetic resonance (NMR) spectroscopy to show that Mn(II) ions also bind to the N-terminal part of the Aβ(1-40) peptide, with a weak binding affinity in the milli- to micromolar range. Circular dichroism (CD) spectroscopy, solid state atomic force microscopy (AFM), fluorescence spectroscopy, and molecular modeling suggest that the weak binding of Mn(II) to Aβ may not have a large effect on the peptide's aggregation into amyloid fibrils. However, identification of an additional metal ion displaying Aβ binding reveals more complex AD metal chemistry than has been previously considered in the literature.

Modeling metalloproteins often requires classical molecular dynamics (MD) simulations in order to capture their relevant motions, which in turn necessitates reliable descriptions of the metal centers involved. One of the most successful approaches to date is provided by the “cationic dummy model”, where the positive charge of the metal ion is transferred toward dummy particles that are bonded to the central metal ion in a predefined coordination geometry. While this approach allows for ligand exchange, and captures the correct electrostatics as demonstrated for different divalent metal ions, current dummy models neglect ion-induced dipole interactions. In the present work, we resolve this weakness by taking advantage of the recently introduced 12–6–4 type Lennard-Jones potential to include ion-induced dipole interactions. We revise our previous dummy model for Mg2+ and demonstrate that the resulting model can simultaneously reproduce the experimental solvation free energy and metal–ligand distances without the need for artificial restraints or bonds. As ion-induced dipole interactions become particularly important for highly charged metal ions, we develop dummy models for the biologically relevant ions Al3+, Fe3+, and Cr3+. Finally, the effectiveness of our new models is demonstrated in MD simulations of several diverse (and highly challenging to simulate) metalloproteins.

The amphiphilic nature of the amyloid-β (Aβ) peptide associated with Alzheimer's disease facilitates various interactions with biomolecules such as lipids and proteins, with effects on both structure and toxicity of the peptide. Here, we investigate these peptide-amphiphile interactions by experimental and computational studies of Aβ(1-40) in the presence of surfactants with varying physicochemical properties. Our findings indicate that electrostatic peptide-surfactant interactions are required for coclustering and structure induction in the peptide and that the strength of the interaction depends on the surfactant net charge. Both aggregation-prone peptide-rich coclusters and stable surfactant-rich coclusters can form. Only Aβ(1-40) monomers, but not oligomers, are inserted into surfactant micelles in this surfactant-rich state. Surfactant headgroup charge is suggested to be important as electrostatic peptide-surfactant interactions on the micellar surface seems to be an initiating step toward insertion. Thus, no peptide insertion or change in peptide secondary structure is observed using a nonionic surfactant. The hydrophobic peptide-surfactant interactions instead stabilize the Aβ monomer, possibly by preventing self-interaction between the peptide core and C-terminus, thereby effectively inhibiting the peptide aggregation process. These findings give increased understanding regarding the molecular driving forces for Aβ aggregation and the peptide interaction with amphiphilic biomolecules.