Molecular dynamics study of conformation transition from helix to sheet of Aβ42 peptide

Zhou, Min; Wen, Hu; Lei, Huimin; Zhang, Tao

doi:10.1016/j.jmgm.2021.108027

Cited by 12 publications

(15 citation statements)

References 40 publications

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“…Starting from a helix-rich Aβ42 structure, biased towards the conformation in the membrane environment 47,68 (PDB ID 1Z0Q), we identified the most suitable adaptive settings to simulate Aβ42 and achieve secondary structure contents expected in aqueous phase. After approximately 64 µs of adaptive MDs, the free A42 diverged substantially from the initial structure, increasing the total amount of random coils and -strands while decreasing the ratio of -helices, becoming closer to experimental values and previous reports 47,52,69 . We identified two regions of Aβ42 that were more prone to form β-strands (mainly residues 2-8 and 30-41).…”

Section: Discussionsupporting

confidence: 86%

CoVAMPnet: Comparative Markov State Analysis for Studying Effects of Drug Candidates on Disordered Biomolecules

Marques

Legrand

Sedlář³

et al. 2023

Preprint

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Alzheimer's disease (AD) is characterized by the deposition of misfolded tau and amyloid-beta (Aβ). Tramiprosate (TMP) and its metabolite 3-sulfopropanoic acid (SPA) are phase 3 therapeutics believed to target Aβ oligomers. It is of paramount importance to understand how TMP/SPA modulate the conformations of Aβ. Here, we studied the Aβ42 alone and in the presence of TMP or SPA by adaptive sampling molecular dynamics. Next, to quantify the effects of drug candidates on Aβ42, we developed a novel Comparative Markov State Analysis (CoVAMPnet) approach: ensembles of learned Markov state models were aligned across different systems based on a solution to an optimal transport problem, and the directional importance of inter-residue distances for assignment to the Markov states was assessed by a discriminative analysis of aggregated neural network gradients. TMP/SPA shifted Aβ42 towards more structured conformations by interacting non-specifically with charged residues and destabilizing salt bridges involved in oligomerization. SPA impacted Aβ42 the most, preserving α-helices and suppressing aggregation-prone β-strands. Experimental biophysical analyses showed mild effects of TMP/SPA on Aβ42, and activity enhancement by the endogenous metabolization of TMP into SPA. The CoVAMPnet method is broadly applicable to study the effects of drug candidates on conformational behavior of intrinsically disordered biomolecules.

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Section: Discussionsupporting

confidence: 86%

CoVAMPnet: Comparative Markov State Analysis for Studying Effects of Drug Candidates on Disordered Biomolecules

Marques

Legrand

Sedlář³

et al. 2023

Preprint

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“…Earlier studies have shown that the segment 21‐NNFGAIL‐27 is essential for the amyloidogenicity of IAPP 54 . Notably, the region Y10‐A21 in the middle of Aβ42 peptide plays an important role in initializing the aggregation of Aβ peptides 55 . The S‐fold in the structure of IAPP resembles polymorphs of AD‐associated Aβ fibrils, which is suggested to be related to the epidemiological link between AD and T2D 54 .…”

Section: Resultsmentioning

confidence: 92%

“…The drug candidates were further screened by molecular docking. The three‐dimensional (3D) structure of IAPP protein was obtained from Protein Data Bank (PDB) (https://www.rcsb.org/), 54 and the 3D structure of Aβ42 peptide was adopted from a previous study 55 . Molecular docking between the screened drug candidates and IAPP or Aβ42 peptide were performed by the AutoDock Tools 56 and the AutoDock Vina 57 packages.…”

Section: Methodsmentioning

confidence: 99%

Hypericin as a potential drug for treating Alzheimer's disease and type 2 diabetes with a view to drug repositioning

et al. 2023

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AimsAlzheimer's disease (AD) and type 2 diabetes (T2D) are two of the most common diseases in elderly population and they have a high rate of comorbidity. Study has revealed that T2D is a major risk factor of AD, and thus exploring therapeutic approaches that can target both diseases has drawn much interest in recent years. In this study, we tried to explore drugs that could be potentially used to prevent or treat both AD and T2D via a drug repositioning approach.MethodsWe first searched the known drugs that may be effective to T2D treatment based on the network distance between the T2D‐associated genes and drugs deposited in the DrugBank database. Then, via molecular docking, we further screened these drugs by examining their interaction with islet amyloid polypeptide (IAPP) and Aβ42 peptide, the key components involved in the pathogenesis of T2D or AD. Finally, the binding between the selected drug candidates and the target proteins was verified by molecular dynamics (MD) simulation; and the potential function of the drug candidates and the corresponding targets were analyzed.ResultsFrom multiple resources, 734 T2D‐associated genes were collected, and a list of 1109 drug candidates for T2D was obtained. We found that hypericin had the lowest binding energy and the most stable interaction with either IAPP or Aβ42 peptide. In addition, we also found that the target genes regulated by hypericin were differentially expressed in the tissues related to the two diseases.ConclusionOur results show that hypericin may be able to bind with IAPP and Aβ42 stably and prevent their accumulation, and thus could be a promising drug candidate for treating the comorbidity of AD and T2D.

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“…Markov state models (MSMs) are a popular model for extracting kinetic information from unbiased molecular dynamics simulations. Recent studies include a wide range of applications, such as understanding protein association kinetics, , enzyme dynamics, ion binding mechanisms, , hydrogen bond dynamics, drug binding mechanisms for drug discovery, − mutational effects on conformational dynamics, − kinetics of intrinsically disordered proteins, protein folding, and understanding allostery. − Estimating a MSM proceeds by first collecting a data set of unbiased molecular dynamics (MD) simulations, then associating each molecular conformation with discrete states, counting transitions between states separated by the temporal resolution of the model (the lag time, τ), and then deriving transition probabilities between states . The final model is summarized by transition matrix T , where the elements T ij are the conditional probabilities of being in state i at time t and then transitioning to a state j at time t + τ: T ij (τ) = P ( j , t = t + τ| i , t = t ).…”

Section: Introductionmentioning

confidence: 99%

Markov State Models: To Optimize or Not to Optimize

Arbon,

Zhu,

Mey

2024

J. Chem. Theory Comput.

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Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins including protein folding. With all statistical and machine learning (ML) models, choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or hyperparameters, are often evaluated by expert judgment or, in the case of MSMs, by maximizing variational scores such as the VAMP-2 score. Modern ML and statistical pipelines often use automatic hyperparameter selection techniques ranging from the simple, choosing the best score from a random selection of hyperparameters, to the complex, optimization via, e.g., Bayesian optimization. In this work, we ask whether it is possible to automatically select MSM models this way by estimating and analyzing over 16,000,000 observations from over 280,000 estimated MSMs. We find that differences in hyperparameters can change the physical interpretation of the optimization objective, making automatic selection difficult. In addition, we find that enforcing conditions of equilibrium in the VAMP scores can result in inconsistent model selection. However, other parameters that specify the VAMP-2 score (lag time and number of relaxation processes scored) have only a negligible influence on model selection. We suggest that model observables and variational scores should be only a guide to model selection and that a full investigation of the MSM properties should be undertaken when selecting hyperparameters.

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Molecular dynamics study of conformation transition from helix to sheet of Aβ42 peptide

Cited by 12 publications

References 40 publications

CoVAMPnet: Comparative Markov State Analysis for Studying Effects of Drug Candidates on Disordered Biomolecules

CoVAMPnet: Comparative Markov State Analysis for Studying Effects of Drug Candidates on Disordered Biomolecules

Hypericin as a potential drug for treating Alzheimer's disease and type 2 diabetes with a view to drug repositioning

Markov State Models: To Optimize or Not to Optimize

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