Can local heating and molecular crowders disintegrate amyloid aggregates?

The present study employs a blend of molecular dynamics simulations and a theoretical model to explore the potential disintegration mechanism of a matured Aβ octamer, aiming to offer a strategy to combat Alzheimer's disease. We investigate local heating and crowding effects on Aβ disintegration by selectively heating key Aβ segments and varying the concentration of sodium dodecyl sulphate (SDS), respectively. Despite initiation of disruption, Aβ aggregates resist complete disintegration during local heating due to rapid thermal energy distribution to the surrounding water. Conversely, although SDS molecules effectively inhibit Aβ aggregation at higher concentration through micelle formation, they fail to completely disintegrate the aggregate due to the exceedingly high energy barrier. To address the sampling challenge posed by the formidable energy barrier, we have performed well-tempered metadynamics simulations. Simulations reveal a multi-step disintegration mechanism for the Aβ octamer, suggesting a probable sequence: octamer → pentamer/hexamer ⇌ tetramer → monomer, with a rate-determining step constituting 45 kJ mol-1 barrier during the octamer to pentamer/hexamer transition. Additionally, we have proposed a novel two-state mean-field model based on Ising spins that offers an insight into the kinetics of the Aβ growth process and external perturbation effects on disintegration. Thus, the current simulation study, coupled with the newly introduced mean-field model, offers an insight into the detailed mechanisms underlying the Aβ aggregation process, guiding potential strategies for effective disintegration of Aβ aggregates.

Exploring novel lead scaffolds for SGLT2 inhibitors: Insights from machine learning and molecular dynamics simulations

Sodium-glucose cotransporter 2 (SGLT2) plays a pivotal role in mediating glucose reabsorption within the renal filtrate, representing a well-known target in type 2 diabetes and heart failure. Recent emphasis has been directed toward designing SGLT2 inhibitors, with C-glycoside inhibitors emerging as front-runners. The architecture of SGLT2 has been successfully resolved using cryo-electron microscopy. However, comprehension of the pharmacophores within the binding site of SGLT2 remains unclear. Here, we use machine learning and molecular dynamics simulations on SGLT2 bound with its inhibitors in preclinical or clinical development to shed light on this issue. Our dataset comprises 1240 SGLT2 inhibitors amalgamated from diverse sources, forming the basis for constructing machine learning models. SHapley Additive exPlanation (SHAP) elucidates the crucial fragments that contribute to inhibitor activity, specifically Morgan_3, 162, 310, 325, 366, 470, 597, 714, 926, and 975. Furthermore, the computed binding free energies and per-residue contributions for SGLT2-inhibitor complexes unveil crucial fragments of inhibitors that interact with residues Asn-75, His-80, Val-95, Phe-98, Val-157, Leu-274, and Phe-453 in the binding site of SGLT2. This comprehensive investigation enhances understanding of the binding mechanism for SGLT2 inhibitors, providing a robust framework for evaluating and discovering novel lead scaffolds within this domain.

Binary Matrix Method to Enumerate, Hierarchically Order, and Structurally Classify Peptide Aggregation

Protein aggregation is a common and complex phenomenon in biological processes, yet a robust analysis of this aggregation process remains elusive. The commonly used methods such as center-of-mass to center-of-mass (COM-COM) distance, the radius of gyration (R_g), hydrogen bonding (HB), and solvent accessible surface area do not quantify the aggregation accurately. Herein, a new and robust method that uses an aggregation matrix (AM) approach to investigate peptide aggregation in a MD simulation trajectory is presented. An nxn two-dimensional AM is created by using the interpeptide C_α-C_α cutoff distances, which are binarily encoded (0 or 1). These aggregation matrices are analyzed to enumerate, hierarchically order, and structurally classify the aggregates. Comparison of the present AM method suggests that it is superior to the HB method since it can incorporate nonspecific interactions and the R_g and COM-COM methods since the cutoff distance is independent of the length of the peptide. More importantly, the present method can structurally classify the peptide aggregates, which the conventional R_g, COM-COM, and HB methods fail to do. The unique selling point of this method is its ability to structurally classify peptide aggregates using two-dimensional matrices.

Conformational-Specific Self-Assembled Peptides as Dual-Mode, Multi-target Inhibitors and Detectors for Different Amyloid Pro-teins

Prevention and detection of misfolded amyloid proteins and their β-structure-rich aggregates are the two promising but differ-ent (pre)clinical strategies to treat and diagnose neurodegenerative diseases including Alzheimer’s diseases (AD) and...

Amyloid-Like Peptide Aggregates

Self-assembly of proteins and peptides into the amyloid fold is a widespread phenomenon in the natural world. The structural hallmark of self-assembly into amyloid fibrillar assemblies is the cross-beta motif, which conveys distinct morphological and mechanical properties. The amyloid fibril formation has contrasting results depending on the organism, in the sense that it can bestow an organism with the advantages of mechanical strength and improved functionality or, on the contrary, could give rise to pathological states. In this chapter we review the existing information on amyloid-like peptide aggregates, which could either be derived from protein sequences, but also could be rationally or de novo designed in order to self-assemble into amyloid fibrils under physiological conditions. Moreover, the development of self-assembled fibrillar biomaterials that are tailored for the desired properties towards applications in biomedical or environmental areas is extensively analyzed. We also review computational studies predicting the amyloid propensity of the natural amino acid sequences and the structure of amyloids, as well as designing novel functional amyloid materials.

Molecular basis for the inhibitory effects of 5-hydroxycyclopenicillone on the conformational transition of Aβ40 monomer

Previous studies have indicated that 5-hydroxycyclopenicillone (HCP), an active compound derived from marine sponge, could inhibit oligomerization of amyloid β-protein (Aβ). However, the molecular basis for the interaction between HCP and Aβ remains unclear. Herein, all-atom molecular dynamics (MD) simulations were used to explore the conformational conversion of an Aβ₄₀ monomer at different concentrations (0-40 mM) of HCP at the atomic level. It is confirmed that the conformational transition of the Aβ₄₀ monomer is prevented by HCP in a concentration-dependent manner in silico. In 40 mM HCP solution, the initial α-helix-rich conformation of Aβ₄₀ monomer is kept under the action of HCP. The intra-peptide hydrophobic collapse and D23-K28 salt bridge are prevented by HCP. Moreover, it is indicated that the non-polar binding energy dominates the binding between HCP and Aβ₄₀ monomer as evaluated by molecular mechanics Poisson-Boltzmann surface area method. And, the residues of F4, Y10, V12, L17 and L34 in Aβ₄₀ might contribute to the binding energy in HCP-Aβ₄₀ complex. All these results elucidate the molecular mechanism underlying the inhibitory effects of HCP against the conformational transformation of Aβ₄₀, providing a support that HCP may be developed as a potential anti-Aβ compound for the treatment of Aβ-related diseases.Communicated by Ramaswamy H. Sarma.

Ferrocene-modified peptides as inhibitors against insulin amyloid aggregation based on molecular simulation

Peptide-based inhibitors have gradually been implicated as drugs for treating protein folding diseases because of their favorable biocompatibility and low toxicity. To develop potential therapeutic strategies for amyloid-related disorders, short peptides modified by Fc, ferrocene-l-Phe-l-Phe (Fc-FF) and ferrocene-l-Phe-l-Tyr (Fc-FY), were used as inhibitors for the investigation of the aggregation behavior of insulin. Firstly, molecular docking predicted the interaction between both Fc-peptides and insulin. Then, the experimental data from ThT, DLS, CD and TEM confirmed that Fc-FF and Fc-FY effectively inhibited insulin fibrillation and disaggregated mature insulin fibrils. Based on a dose-dependent manner, both Fc-peptides can strongly inhibit insulin fibrillation, extend lag phase time, reduce final fibril formation (beyond 99% by Fc-peptides of 400 µM), decrease the formation of high-content β-sheet structures and reduce the size of insulin fibrils. Additionally, we found that compared with Fc-FY, the better inhibitory effect of Fc-FF at concentration below 400 µM was mainly resulted from the difference in π-π interaction and hydrogen bonds between Fc-peptides and insulin, according to molecular dynamics analysis. Our results demonstrated Fc-peptides, Fc-FF and Fc-FY, may play effective roles in the development of new therapeutic drugs or strategies for amyloid-related disorders, and the molecular dynamics simulation may be helpful for designing appropriate inhibitors of anti-amyloidosis diseases.

Interactions between Curcumin Derivatives and Amyloid-β Fibrils: Insights from Molecular Dynamics Simulations

The aggregation of amyloid-β (Aβ) peptides into senile plaques is a hallmark of Alzheimer's disease (AD) and is hypothesized to be the primary cause of AD related neurodegeneration. Previous studies have shown the ability of curcumin to both inhibit the aggregation of Aβ peptides into oligomers or fibrils and reduce amyloids in vivo. Despite the promise of curcumin and its derivatives to serve as diagnostic, preventative, and potentially therapeutic AD molecules, the mechanism by which curcumin and its derivatives bind to and inhibit Aβ fibrils' formation remains elusive. Here, we investigated curcumin and a set of curcumin derivatives in complex with a hexamer peptide model of the Aβ1-42 fibril using nearly exhaustive docking, followed by multi-ns molecular dynamics simulations, to provide atomistic-detail insights into the molecules' binding and inhibitory properties. In the vast majority of the simulations, curcumin and its derivatives remain firmly bound in complex with the fibril through primarily three different principle binding modes, in which the molecules interact with residue domain 17LVFFA21, in line with previous experiments. In a small subset of these simulations, the molecules partly dissociate the outermost peptide of the Aβ1-42 fibril by disrupting β-sheets within the residue domain 12VHHQKLVFF20. A comparison between binding modes leading or not leading to partial dissociation of the outermost peptide suggests that the latter is attributed to a few subtle key structural and energetic interaction-based differences. Interestingly, partial dissociation appears to be either an outcome of high affinity interactions or a cause leading to high affinity interactions between the molecules and the fibril, which could partly serve as a compensation for the energy loss in the fibril due to partial dissociation. In conjunction with this, we suggest a potential inhibition mechanism of Αβ1-42 aggregation by the molecules, where the partially dissociated 16KLVFF20 domain of the outermost peptide could either remain unstructured or wrap around to form intramolecular interactions with the same peptide's 29GAIIG33 domain, while the molecules could additionally act as a patch against the external edge of the second outermost peptide's 16KLVFF20 domain. Thereby, individually or concurrently, these could prohibit fibril elongation.

Revealing the Mechanism of EGCG, Genistein, Rutin, Quercetin, and Silibinin Against hIAPP Aggregation via Computational Simulations

To inhibit hIAPP aggregation and reduce toxicity of its oligomers are one of the potential strategies for the treatment of Type 2 diabetes (T2D). It has been reported that there is an effective inhibitory effect on hIAPP aggregation by five natural flavonoids, including Genistein, Rutin, Quercetin, Epigallocatechin gallate (EGCG), and Silibinin, which are widely found in our daily food. However, the detailed mechanisms to inhibit hIAPP aggregation remain unclear. Here, we explore the mechanisms of the five flavonoids against hIAPP aggregation by molecular docking and molecular dynamics simulations. We show that these flavonoids can disaggregate Chain A and Chain B of hIAPP to reduce the extended conformation by binding with two regions of hIAPP, Leu₁₂-Ala₁₃-Asn₁₄ and Asn₃₁-Val₃₂-Gly₃₃-Ser₃₄-Asn₃₅, with the inhibitory ability of Genistein > Rutin > Quercetin > EGCG > Silibinin. These five compounds exhibit a common mechanism for disaggregation of the hIAPP pentamer; that is, they loosen the two nearest peptide chains to potentially destroy the hIAPP oligomer. Mutations of eight key residues remarkably affected by the flavonoids indicate that the secondary structures of the hIAPP pentamer change from β-sheet to be random coil, thereby to destroy its structural stability; moreover, the 28th (Ser), 12th (Leu) and 32nd (Val) amino acids exhibit significant effects on structural stability of the hIAPP pentamer, providing an important hint that these amino acids can be considered as potential targets for design of new candidate inhibitors against hIAPP oligomers. This work is beneficial to understanding of mechanism of these inhibits against hIAPP aggregation and will facilitate screening, modification, and design of new inhibitors.

The interactions of an Aβ protofibril with a cholesterol-enriched membrane and involvement of neuroprotective carbazolium-based substances

Recent studies have shown that the aggregation of the amyloid-beta peptide (Aβ) in the brain cell membrane is responsible for the emergence of Alzheimer's disease (AD); the exploration of effective factors involved in the extension of the aggregation process and alternatively the examination of an effective inhibitor via theoretical and experimental tools are among the main research topics in the field of AD treatment. Therefore, in this study, we used all-atom molecular dynamics (MD) simulations to clarify the impact of cell membrane cholesterol on the interaction of Aβ with 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) as a membrane model. Moreover, the effect of the P7C3-S243 molecule on the abovementioned process was investigated. The simulation results disclosed the neuroprotective property of the P7C3-S243 molecule. The MD simulation results indicate that the interaction of cholesterol molecules with the Aβ oligomer is negligible and cannot enhance membrane rupture. However, strong hydrogen bonding between the POPC molecules and the oligomers led to membrane perturbation. According to our modellings, the P7C3-S243 molecular layer can protect the cell membrane by inhibiting the direct interaction between the bilayer and Aβ. In addition, free-energy calculations were conducted to determine the possible penetration of Aβ fibrils into the cholesterol-enriched membrane.

2-Aryl-3-(arylideneamino)-1,2-dihydroquinazoline-4(3H)-ones as inhibitors of cholinesterases and self-induced β-amyloid (Aβ) aggregation: biological evaluations and mechanistic insights from molecular dynamics simulations

A series of 2-aryl-3-(arylideneamino)-1,2-dihydroquinazoline-4(3H)-ones were evaluated as inhibitors of acetylcholinesterase (AChE), butyrylcholinesterase (BuChE) and self-induced β-amyloid (Aβ) aggregation. All the compounds were found to inhibit both forms of cholinesterase (IC50 in the range 4-32 μM) with some selectivity for BuChE. Most of the compounds also showed self-induced Aβ aggregation inhibitory activities, which were comparable or higher than those obtained for reference compounds, curcumin and myricetin. Docking and molecular dynamics (MD) simulation experiments suggested that the compounds are able to disrupt the dimer form of Aβ.