Mechanistic Kinetic Model Reveals How Amyloidogenic Hydrophobic Patches Facilitate the Amyloid-β Fibril Elongation

Helix-to-sheet transition of the Aβ42 peptide revealed using an enhanced sampling strategy and Markov state model

The self-assembly of Aβ peptides into toxic oligomers and fibrils is the primary cause of Alzheimer's disease. Moreover, the conformational transition from helix to sheet is considered a crucial step in the aggregation of Aβ peptides. However, the structural details of this process still remain unclear due to the heterogeneity and transient nature of the Aβ peptides. In this study, we developed an enhanced sampling strategy that combines artificial neural networks (ANN) with metadynamics to explore the conformational space of the Aβ42 peptides. The strategy consists of two parts: applying ANN to optimize CVs and conducting metadynamics based on the resulting CVs to sample conformations. The results showed that this strategy achieved better sampling performance in terms of the distribution of sampled conformations. The sampling efficiency is increased by 10-fold compared to our previous Hamiltonian Exchange Molecular Dynamics (MD) and by 1000-fold compared to ordinary MD. Based on the sampled conformations, we constructed a Markov state model to understand the detailed transition process. The intermediate states in this process are identified, and the connecting paths are analyzed. The conformational transitions in D23-K28 and M35-V40 are proven to be crucial for aggregation. These results are helpful in clarifying the mechanism and process of Aβ42 peptide aggregation. D23-K28 and M35-V40 can be identified as potential targets for screening and designing inhibitors of Aβ peptide aggregation.

Aggregation Dynamics Characteristics of Seven Different Aβ Oligomeric Isoforms-Dependence on the Interfacial Interaction

The aggregation of β-amyloid (Aβ) peptides has been confirmed to be associated with the onset of Alzheimer's disease (AD). Among the three phases of Aβ aggregation, the lag phase has been considered to be the best time for early Aβ pathological deposition clinical intervention and prevention for potential patients with normal cognition. Aβ peptide exists in various lengths in vivo, and Aβ oligomer in the early lag phase is neurotoxic but polymorphous and metastable, depending on Aβ length (isoform), molecular weight, and specific phase, and therefore hardly characterized experimentally. To cope with the problem, molecular dynamics simulation was used to investigate the aggregation process of five monomers for each of the seven common Aβ isoforms during the lag phase. Results showed that Aβ(1-40) and Aβ(1-38) monomers aggregated faster than their truncated analogues Aβ(4-40) and Aβ(4-38), respectively. However, the aggregation rate of Aβ(1-42) was slower than that of its truncated analogues Aβ(4-42) rather than that of Aβpe(3-42). More importantly, Aβ(1-38) is first predicted as more likely to form stable hexamer than the remaining five Aβ isoforms, as Aβ(1-42) does. It is hydrophobic interaction mainly (>50%) from the interfacial β1 and β2 regions of two reactants, pentamer and monomer, aggregated by Aβ(1-38)/Aβ(1-42) rather than by other Aβ isoforms, that drives the hexamer stably as a result of the formation of the effective hydrophobic collapse. This paper provides new insights into the aggregation characteristics of Aβ with different lengths and the conditions necessary for Aβ to form oligomers with a high molecular weight in the early lag phase, revealing the dependence of Aβ hexamer formation on the specific interfacial interaction.

Challenges in studying the liquid-to-solid phase transitions of proteins using computer simulations

"Membraneless organelles," also referred to as biomolecular condensates, perform a variety of cellular functions and their dysregulation is implicated in cancer and neurodegeneration. In the last two decades, liquid-liquid phase separation (LLPS) of intrinsically disordered and multidomain proteins has emerged as a plausible mechanism underlying the formation of various biomolecular condensates. Further, the occurrence of liquid-to-solid transitions within liquid-like condensates may give rise to amyloid structures, implying a biophysical link between phase separation and protein aggregation. Despite significant advances, uncovering the microscopic details of liquid-to-solid phase transitions using experiments remains a considerable challenge and presents an exciting opportunity for the development of computational models which provide valuable, complementary insights into the underlying phenomenon. In this review, we first highlight recent biophysical studies which provide new insights into the molecular mechanisms underlying liquid-to-solid (fibril) phase transitions of folded, disordered and multi-domain proteins. Next, we summarize the range of computational models used to study protein aggregation and phase separation. Finally, we discuss recent computational approaches which attempt to capture the underlying physics of liquid-to-solid transitions along with their merits and shortcomings.