Characterization of the Conformations of Amyloid Beta 42 in Solution That May Mediate Its Initial Hydrophobic Aggregation

Ultrasensitive Assays Detect Different Conformations of Plasma β Amyloids

With the developments of ultrasensitive technologies such as immunomagnetic reduction (IMR) assay, single molecule array (SIMOA) assay, electrochemiluminescence immunoassay (ECLIA), the assay of blood-based amyloid 1-42 (Aβ1-42) becomes possible. However, the changes in measured plasma Aβ1-42 concentrations in Alzheimer's disease (AD) compared to cognitively unimpaired subjects (CU) are inconsistent. A possible reason for the inconsistency regarding various conformations of Aβ1-42 in plasma is explored in this study. Three samples with equal amounts of Aβ1-42 but different proportions of monomers and oligomers of Aβ1-42 were prepared. The Aβ1-42 composition of monomers and oligomers in samples was analyzed with Western blot. Identically diluted versions of these three samples were assayed with IMR and SIMOA for Aβ1-42 concentrations. The three diluted samples showed similar levels of Aβ1-42 assayed with IMR, whereas much lower levels for samples with more oligomers assayed with SIOMA. The results imply that IMR detects both monomers and oligomers of Aβ1-42. The measured levels of Aβ1-42 are independent of the proportions of monomer or oligomer Aβ1-42 but depend on the total amounts of Aβ1-42. In the case of SIMOA, monomers of Aβ1-42 are the primary target measured. By comparing Aβ1-42 concentrations of the plasma using IMR and SIMOA, the significant difference in plasma Aβ1-42 levels using IMR in AD compared to CU is mainly due to the formations of oligomeric Aβ1-42. Therefore, if the target molecules are monomers of Aβ1-42, SIMOA is the method of choice. Still, if the target molecules should include monomers, small and large oligomers, IMR would be an optimal consideration. In the future, the clinical implications of the proportion of oligomeric Aβ1-42 need to be elucidated.

Molecular Simulation of the Binding of Amyloid Beta to Apolipoprotein A-I in High-Density Lipoproteins

Disrupted clearance of amyloid beta (Aβ) from the brain enhances its aggregation and formation of amyloid plaques in Alzheimer's disease. The most abundant protein constituent of circulating high-density lipoprotein (HDL) particles, apoA-I, readily crosses the blood-brain barrier from periphery circulation, exhibits low-micromolar binding affinity for soluble, neurotoxic forms of Aβ, and modulates Aβ aggregation and toxicity in vitro. Its highly conserved N-terminal sequence, 42LNLKLLD48 ('LN'), has been proposed as a binding region for Aβ. However, high-resolution structural characterisation of the mechanism of HDL-Aβ interaction is very difficult to attain. Molecular dynamics simulations were conducted to investigate for the first time the interaction of Aβ and the 'LN' segment of apoA-I. Favourable binding of Aβ by HDLs was found to be driven by hydrophobic and hydrogen-bonding interactions predominantly between the 'LN' segment of apoA-I and Aβ. Preferential binding of Aβ may proceed in small, protein-rich HDLs whereby solvent-exposed hydrophobic 'LN' segments of apoA-I interact specifically with Aβ, stabilising it on the HDL surface in a possibly non-amyloidogenic conformation, facilitating effective Aβ clearance. These findings rationalise the potentially therapeutic role of HDLs in reducing Aβ aggregation and toxicity, and of peptide mimics of the apoA-I interacting region in blocking Aβ aggregation.

β-Hairpin Alignment Alters Oligomer Formation in Aβ-Derived Peptides

Amyloid-β (Aβ) forms heterogeneous oligomers, which are implicated in the pathogenesis of Alzheimer's disease (AD). Many Aβ oligomers consist of β-hairpin building blocks─Aβ peptides in β-hairpin conformations. β-Hairpins of Aβ can adopt a variety of alignments, but the role that β-hairpin alignment plays in the formation and heterogeneity of Aβ oligomers is poorly understood. To explore the effect of β-hairpin alignment on the oligomerization of Aβ peptides, we designed and studied two model peptides with two different β-hairpin alignments. Peptides Aβm17-36 and Aβm17-35 mimic two different β-hairpins that Aβ can form, the Aβ17-36 and Aβ17-35 β-hairpins, respectively. These hairpins are similar in composition but differ in hairpin alignment, altering the facial arrangements of the side chains of the residues that they contain. X-ray crystallography and SDS-PAGE demonstrate that the difference in facial arrangement between these peptides leads to distinct oligomer formation. In the crystal state, Aβm17-36 forms triangular trimers that further assemble to form hexamers, while Aβm17-35 forms tetrameric β-barrels. In SDS-PAGE, Aβm17-36 assembles to form a ladder of oligomers, while Aβm17-35 either assembles to form a dimer or does not assemble at all. The differences in the behavior of Aβm17-36 and Aβm17-35 suggest β-hairpin alignment as a source of the observed heterogeneity of Aβ oligomers.

Mutexa: A Computational Ecosystem for Intelligent Protein Engineering

Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This Review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, researchers will seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins through a finely-tuned computational machine, akin to Amazon Alexa's role as a versatile virtual assistant. The technical foundation of Mutexa has been established through the development of a database that combines and relates enzyme structures and their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure-function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of nonelectrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and fundamental challenges in our endeavor to develop new Mutexa applications that assist the identification of beneficial mutants in protein engineering.

Identification of hybrid amyloid strains assembled from amyloid-βand human islet amyloid polypeptide

Cross-fibrillation of amyloid-β(Aβ) peptides and human islet amyloid polypeptides (hIAPP) has revealed a close correlation between Alzheimer's disease and type 2 diabetes (T2D). Importantly, different amyloid strains are likely to lead to the clinical pathological heterogeneity of degenerative diseases due to toxicity. However, given the complicated cross-interactions between different amyloid peptides, it is still challenging to identify the polymorphism of the hybrid amyloid strains and reveal mechanistic insights into aggregation, but highly anticipated due to their significance. In this study, we investigated the cross-fibrillation of Aβpeptides and different hIAPP species (monomers, oligomers, and fibrils) using combined experimental and simulation approaches. Cross-seeding and propagation of different amyloid peptides monitored by experimental techniques proved that the three species of hIAPP aggregates have successively enhanced Aβfibrillation, especially for hIAPP fibrils. Moreover, the polymorphism of these morphologically similar hybrid amyloid strains could be distinguished by testing their mechanical properties using quantitative nanomechanical mapping, where the assemblies of Aβ-hIAPP fibrils exhibited the high Young's modulus. Furthermore, the enhanced internal molecular interactions andβ-sheet structural transformation were proved by exploring the conformational ensembles of Aβ-hIAPP heterodimer and Aβ-hIAPP decamer using molecular dynamic simulations. Our findings pave the way for identifying different hybrid amyloid strains by quantitative nanomechanical mapping and molecular dynamic simulations, which is important not only for the precise classification of neurodegenerative disease subtypes but also for future molecular diagnosis and therapeutic treatment of multiple interrelated degenerative diseases.

Identification of Catechins' Binding Sites in Monomeric Aβ42 through Ensemble Docking and MD Simulations

The assembly of the amyloid-β peptide (Aβ) into toxic oligomers and fibrils is associated with Alzheimer's disease and dementia. Therefore, disrupting amyloid assembly by direct targeting of the Aβ monomeric form with small molecules or antibodies is a promising therapeutic strategy. However, given the dynamic nature of Aβ, standard computational tools cannot be easily applied for high-throughput structure-based virtual screening in drug discovery projects. In the current study, we propose a computational pipeline-in the framework of the ensemble docking strategy-to identify catechins' binding sites in monomeric Aβ₄₂. It is shown that both hydrophobic aromatic interactions and hydrogen bonding are crucial for the binding of catechins to Aβ₄₂. Additionally, it has been found that all the studied ligands, especially EGCG, can act as potent inhibitors against amyloid aggregation by blocking the central hydrophobic region of Aβ. Our findings are evaluated and confirmed with multi-microsecond MD simulations. Finally, it is suggested that our proposed pipeline, with low computational cost in comparison with MD simulations, is a suitable approach for the virtual screening of ligand libraries against Aβ.

Metastable alpha-rich and beta-rich conformations of small Aβ42 peptide oligomers

Probing the structures of amyloid-β (Aβ) peptides in the early steps of aggregation is extremely difficult experimentally and computationally. Yet, this knowledge is extremely important as small oligomers are the most toxic species. Experiments and simulations on Aβ42 monomer point to random coil conformations with either transient helical or β-strand content. Our current conformational description of small Aβ42 oligomers is funneled toward amorphous aggregates with some β-sheet content and rare high energy states with well-ordered assemblies of β-sheets. In this study, we emphasize another view based on metastable α-helix bundle oligomers spanning the C-terminal residues, which are predicted by the machine-learning AlphaFold2 method and supported indirectly by low-resolution experimental data on many amyloid polypeptides. This finding has consequences in developing novel chemical tools and to design potential therapies to reduce aggregation and toxicity.

Aβ-oligomers: A potential therapeutic target for Alzheimer's disease

The cascade of amyloid formation relates to multiple complex events at the molecular level. Previous research has established amyloid plaque deposition as the leading cause of Alzheimer's disease (AD) pathogenesis, detected mainly in aged population. The primary components of the plaques are two alloforms of amyloid-beta (Aβ), Aβ_1-42 and Aβ_1-40 peptides. Recent studies have provided considerable evidence contrary to the previous claim indicating that amyloid-beta oligomers (AβOs) as the main culprit responsible for AD-associated neurotoxicity and pathogenesis. In this review, we have discussed the primary features of AβOs, such as assembly formation, the kinetics of oligomer formation, interactions with various membranes/membrane receptors, the origin of toxicity, and oligomer-specific detection methods. Recently, the discovery of rationally designed antibodies has opened a gateway for using synthesized peptides as a grafting component in the complementarity determining region (CDR) of antibodies. Thus, the Aβ sequence motif or the complementary peptide sequence in the opposite strand of the β-sheet (extracted from the Protein Data Bank: PDB) helps design oligomer-specific inhibitors. The microscopic event responsible for oligomer formation can be targeted, and thus prevention of the overall macroscopic behaviour of the aggregation or the associated toxicity can be achieved. We have carefully reviewed the oligomer formation kinetics and associated parameters. Besides, we have depicted a thorough understanding of how the synthesized peptide inhibitors can impede the early aggregates (oligomers), mature fibrils, monomers, or a mixture of the species. The oligomer-specific inhibitors (peptides or peptide fragments) lack in-depth chemical kinetics and optimization control-based screening. In the present review, we have proposed a hypothesis for effectively screening oligomer-specific inhibitors using the chemical kinetics (determining the kinetic parameters) and optimization control strategy (cost-dependent analysis). Further, it may be possible to implement the structure-kinetic-activity-relationship (SKAR) strategy instead of structure-activity-relationship (SAR) to improve the inhibitor's activity. The controlled optimization of the kinetic parameters and dose usage will be beneficial for narrowing the search window for the inhibitors.