We used simulations and NMR experiments to investigate the diverse structure of amyloid-尾 (A尾) peptide in the soluble non-aggregated form in order to better understand this peptide's role in Alzheimer's disease. Because amyloid-尾 is intrinsically disordered in its monomeric state, the combination of molecular dynamics simulation and NMR spectroscopy was crucial to determining the individual conformations that make up the amyloid-尾 structural ensemble. Initially we focused on amyloid-尾 1-42 (A尾42), which is the most toxic form of amyloid-尾. We collected homonuclear Nuclear Overhauser Effect (NOE) data on the peptide, and used extensive molecular dynamics simulations to characterize its conformational ensemble. We found that the conformational ensemble of A尾42 is extremely heterogeneous. However, it also contains many structured populations with long-range NOE contacts. This is in contrast to A尾21-30, an amyloid-尾 fragment. A尾21-30 is mostly extended and unstructured, with no long- range NOEs measured. Next we characterized A尾40, another common form of amyloid-尾, which is less toxic and aggregation prone than A尾42. Again we saw many long-range NOEs and structured conformations in the A尾40 ensemble, but the most populated conformations for A尾40 and A尾42 were quite different. From our simulations we had seen that A尾42 adopts a 尾-turn and 尾-strand, which together form the most common long-range interaction of the peptide, and that this turn is consistent with the same bend and 尾-strand segment seen in the aggregated form of the peptide. A尾40 also adopts many different long-range 尾-strand conformations, however, none of them are similar to the fibril-like turn and 尾-strand seen in the A尾42 ensemble. This is one possible explanation for the greater aggregation rate and toxicity of A尾42.
Amyloid-尾 presents a difficult case for characterizing an intrinsically disordered disease protein because it contains many structured conformations within its ensemble. We therefore decided to examine the effectiveness of different computational methods for determining the conformational ensemble of this intrinsically disordered protein. We compared the knowledge- based approach to our de novo molecular dynamics approach. The knowledge-based approach randomly generates an ensemble and refines it to fit the NMR data. The de novo molecular dynamics approach, on the other hand, uses no experimental information to form the amyloid-尾 ensemble. In both methods, we compare the simulated ensemble to the experimental data after it is created. We found that the knowledge-based approach is highly dependent on the starting pool of structures that it refines, and that a randomly generated pool does not contain structured conformations which are able to fit the NMR data. We also found that certain types of NMR data, like J-coupling constants and NOEs, do a much better job of distinguishing between vastly different ensembles than other types of NMR data like chemical shifts, which are calculated to be the same for both unstructured and heterogeneous structured ensembles. We did find that the knowledge-based approach was useful for further refining the molecular dynamics simulation ensemble to give a better fit to the NMR data. This refinement yielded a slightly different picture of the A尾40 and A尾42 monomer conformational ensembles. The refined A尾42 ensemble still contains the fibril-like turn and 尾-strand as its major feature, but in the refined A尾40 ensemble we see many fewer 尾-strands than in the molecular dynamics ensemble. Our revised picture of the two peptides shows that A尾40 is less structured than A尾42, with the most populated 尾-strand of A尾40 forming near its N-terminus. A尾42, with two additional residues at the C-terminus, forms more C-terminal hydrophobic interactions, often adopting a large loop that nucleates a fibril-like turn and 尾-strand near the middle of the peptide sequence. Thus, the A尾42 C-terminus does not form a 尾-strand itself, but promotes 尾-structure at a different region of the sequence, while preventing the type of 尾-strands formed in the A尾40 ensemble.
After fully characterizing the amyloid-尾 monomer ensemble, we were interested in studying an oligomer of amyloid-尾, which is believed to be the toxic agent in Alzheimer's disease. In collaboration with the Schaffer group, we assessed the toxicity of an A尾42 oligomer, known as the globulomer, on cultures of human cortical neurons. This oligomer, which can be prepared consistently and does not aggregate to form fibrils, was found to induce neuronal cell death, indicating that it could be a toxic complex of amyloid-尾. This led us to an investigation of the A尾42 globulomer structure, known to consist of 尾-sheets. One proposed model of the globulomer is based on NMR data from a small globulomer precursor. Another model of the globulomer derives from coarse grain simulations of amyloid-尾 prefibrils. We used molecular dynamics simulations to begin a comparison of these two models. Based on our preliminary simulations, the prefibrillar model seems to maintain a more stable 尾-sheet structure than the NMR-based model. However, so far the NMR-based model has only been simulated as a dimer unit, and may be more stable when more chains are added. We have also calculated NMR observables from each of the two models and we find that J-coupling and amide exchange experiments may be useful in determining which model more accurately represents the globulomer. Future NMR experiments as well as calculation of NOE data from the simulations will help to form a better picture of this toxic Alzheimer's oligomer. Like the amyloid-尾 monomer, the oligomer may occupy a range of conformational states that form a diverse ensemble, and therefore molecular dynamics simulations as well as NMR data are crucial to fully representing its structure.