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

Wang, Yu; Lv, Yonghui; Jin, Liang; Liang, Guizhao

doi:10.1007/s12539-019-00352-9

Cited by 20 publications

(20 citation statements)

References 36 publications

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“…225 A combined molecular docking and MD simulation was conducted to study the binding behaviors of five natural flavonoidsgenistein, rutin, quercetin, EGCG, and silibinin to hIAPP pentamer. 182 While these flavonoids have different binding sites and affinity to hIAPP pentamer, they also showed favorable binding to two common regions, L 12 -A 13 -N 14 and N 31 -V 32 -G 33 -S 34 -N 35 , in hIAPP, which greatly disturbed β-sheet structures and even association of hIAPP peptides to different extents. The computationally identified inhibitory capacity of these flavonoids on hIAPP aggregation was in a decreasing order of genistein > rutin > quercetin > EGCG > silibinin.…”

Section: Hiapp Aggregation Inhibitorsmentioning

confidence: 98%

“…), FLPNF (adapted with permission from ref ; copyright 2019 Yue Shi et al), pyridazinylpyrazolone (adapted with permission from ref ; copyright 2018 Elsevier Inc.), and CID636525 (adapted with permission from ref ; copyright 2017 Elsevier Inc.) with hIAPP monomers. (b) Molecular simulations of SWCNT-OHs (reprinted with permission from ref ; copyright 2018 American Chemical Society), dopamine (reprinted with permission from ref ; copyright 2019 American Chemical Society), resveratrol (reprinted with permission from ref ; copyright 2014 American Chemical Society), morin (adapted with permission from ref ; copyright 2015 PCCP Owner Societies), tanshinones (adapted with permission from ref ; copyright 2018 The Royal Society of Chemistry), natural flavonoids (reprinted with permission from ref ; copyright 2020 International Association of Scientists in the Interdisciplinary Areas), genistein (adapted with permission from ref ; copyright 2018 American Chemical Society), and insulin (adapted with permission from ref ; copyright 2018 The Royal Society of Chemistry) with hIAPP oligomers.…”

Section: Computational Studies Of Hiapp Inhibitorsmentioning

confidence: 99%

“…Another REMD simulation of dopamine with high-ordered hIAPP protofibrils revealed that dopamine preferentially bound to three highly amyloidogenic regions 11 RLANFLVH 18 , 22 NFGAIL 27 , and 30 TNVGSNT 36 of hIAPP to prevent the formation of β-sheets and destabilize small hIAPP dimers and large hIAPP protofibrils, thus hindering the amyloid formation of hIAPP . A combined molecular docking and MD simulation was conducted to study the binding behaviors of five natural flavonoidsgenistein, rutin, quercetin, EGCG, and silibininto hIAPP pentamer . While these flavonoids have different binding sites and affinity to hIAPP pentamer, they also showed favorable binding to two common regions, L 12 -A 13 -N 14 and N 31 -V 32 -G 33 -S 34 -N 35 , in hIAPP, which greatly disturbed β-sheet structures and even association of hIAPP peptides to different extents.…”

Section: Computational Studies Of Hiapp Inhibitorsmentioning

confidence: 99%

“…43 This dual and strong inhibition ability of genistein mainly stems from the inhibition of the structural transition of Aβ and hIAPP from other structures to β-sheets as judged by cyclic dichroism, thus inhibiting their amyloid formation, while the cell protection effect of genistein is attributed to the inhibition of membrane disruption induced by amyloid peptides, as confirmed by large unilamellar vesicle (LUV) membrane leakage assay. Silibinin, the main constituent from milk thistle seeds, greatly inhibited the formation of both hIAPP fibrils, by 84.8%, 182 and hIAPP oligomers. 53 The silibinin-induced inhibition effect mainly stemmed from disruption of the structural transition of hIAPP monomers from random coil to α-helix via a strong binding of silibinin to the S 28 residue.…”

Section: Hiapp Aggregation Inhibitorsmentioning

confidence: 99%

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Introduction and Fundamentals of Human Islet Amyloid Polypeptide Inhibitors

Tang

Zhang

et al. 2020

ACS Appl. Bio Mater.

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Type 2 diabetes (T2D) is a common protein misfolding disease (PMD), and its pathogenesis is considered to be tightly associated with the aggregation of the disease-causative hIAPP (or amylin). Numerous studies have shown a possible pathological link between hIAPP aggregation and β-cell death; thus, different-level strategies from basic research to clinical bench applications have been applied to discover and design different types of inhibitors for preventing hIAPP aggregation and toxicity. This review surveys recent and important advances of hIAPP aggregation inhibitors in the context of amyloid aggregation, toxicity, and inhibition. Many hIAPP inhibitors have been explored to exert different inhibitory functions on hIAPP aggregation via different pathways. A further overview of molecular simulations of inhibitor–hIAPP systems highlights some consensus binding sequences and structures of hIAPP for different inhibitors, which provide molecular insights into well-defined binding targets and binding-induced inhibition mechanisms for structural-based design of hIAPP inhibitors. In a broader view, while anti-aggregation inhibitors hold substantial promise in the prevention of PMDs, many challenges still remain and need to be addressed for advancing our fundamental understanding of amyloid aggregation and practical design of clinically relevant inhibitors to treat PMDs.

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Section: Hiapp Aggregation Inhibitorsmentioning

confidence: 98%

Section: Computational Studies Of Hiapp Inhibitorsmentioning

confidence: 99%

Section: Computational Studies Of Hiapp Inhibitorsmentioning

confidence: 99%

Section: Hiapp Aggregation Inhibitorsmentioning

confidence: 99%

See 2 more Smart Citations

Introduction and Fundamentals of Human Islet Amyloid Polypeptide Inhibitors

Tang

Zhang

et al. 2020

ACS Appl. Bio Mater.

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“…Moreover, they accurately counted the number of persons suffering from diabetes by using SVM. Reference [16] developed a tele-ecg system with Hadoop and big data framework by using mining techniques for processing and classifying datasets related to cardiovascular disease. Although Hadoop has been developed, some of the issues in handling large datasets raised concerns in terms of server handling.…”

Section: Introductionmentioning

confidence: 99%

Distributed Healthcare Framework Using MMSM-SVM and P-SVM Classification

Sujitha¹,

Paramasivan²

2022

Computers, Materials &Amp; Continua

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With the modernization of machine learning techniques in healthcare, different innovations including support vector machine (SVM) have predominantly played a major role in classifying lung cancer, predicting coronavirus disease 2019, and other diseases. In particular, our algorithm focuses on integrated datasets as compared with other existing works. In this study, parallel-based SVM (P-SVM) and multiclass-based multiple submodels (MMSM-SVM) were used to analyze the optimal classification of lung diseases. This analysis aimed to find the optimal classification of lung diseases with id and stages, such as key-value pairs in MapReduce combined with P-SVM and MMSVM for binary and multiclasses, respectively. For nonlinear classification, kernel clustering-based SVM embedded with multiple submodels was developed. Both algorithms were developed using Apache spark environment, and data for the analysis were retrieved from microscope lab, UCI, Kaggle, and General Thoracic surgery database along with some electronic health records related to various lung diseases to increase the dataset size to 5 GB. Performance measures were conducted using a 5 GB dataset with five nodes. Dataset size was finally increased, and task analysis and CPU utilization were measured.

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Mechanism of five flavonoids inhibiting NtMGAM based on molecular simulations

Wang

Zheng

Liang

2022

J Chinese Chemical Soc

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Recent studies have showed that five flavonoids (Epigallocatechin gallate [EGCG], Rutin, Silibinin, Genistein, and Quercetin) are potential therapeutic agents for type II diabetes mellitus (T2DM). Our previous study revealed the mechanism of these five flavonoids with human islet amyloid peptide, but the mechanism of action with other T2DM-related target proteins is unknown. In this study, we investigate the competitive inhibition mechanism of N-terminal catalytic domain maltase glucoseamylase (NtMGAM), which is another important target of T2DM, with the five types of flavonoids through molecular docking, molecular dynamics, and steered molecular dynamics. Our results show that the NtMGAM active pocket tends to bind to the aromatic nucleus. Moreover, EGCG and Rutin can potentially inhibit NtMGAM, and both molecules exhibit a larger binding capability with the active pocket than Acarbose. Compared with the hexasaccharide group, the pentasaccharide group has the advantage to bind with the NtMGAM active pocket, suggesting that the structure embedded into the active pocket should be inadvisable to oversize. Meanwhile, solvent molecules are a crucial factor affecting the binding stability of inhibitor with NtMGAM. In conclusion, this work is helpful to provide a theoretical basis for screening, modifying, and designing new therapeutic or preventive drugs targeting T2DM.

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

Cited by 20 publications

References 36 publications

Introduction and Fundamentals of Human Islet Amyloid Polypeptide Inhibitors

Introduction and Fundamentals of Human Islet Amyloid Polypeptide Inhibitors

Distributed Healthcare Framework Using MMSM-SVM and P-SVM Classification

Mechanism of five flavonoids inhibiting NtMGAM based on molecular simulations

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