Natural Compounds against Neurodegenerative Diseases: Molecular Characterization of the Interaction of Catechins from Green Tea with Aβ1–42, PrP106–126, and Ataxin‐3 Oligomers

Sironi, Erika; Colombo, Laura; Lompo, Angela; Messa, Massimo; Bonanomi, Marcella; Regonesi, Maria Elena; Salmona, Mario; Airoldi, Cristina

doi:10.1002/chem.201403188

Cited by 39 publications

(56 citation statements)

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“…9,10,[22][23][24][25][26][27][28][29][30][31] STD exploits the protein magnetization as a reservoir and upon saturation of the protein magnetization, crossrelaxation between the protein and ligand protons causes intensity enhancements for the ligand signals. The efficiency of the intensity enhancement of ligand proton predominantly depends on the distance between the proton and protein.…”

Section: Introductionmentioning

confidence: 99%

Molecular Level Insights on Collagen–Polyphenols Interaction Using Spin–Relaxation and Saturation Transfer Difference NMR

et al. 2015

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Interaction of small molecules with collagen has far reaching consequences in biological and industrial processes. The interaction between collagen and selected polyphenols, viz., gallic acid (GA), pyrogallol (PG), catechin (CA), and epigallocatechin gallate (EGCG), has been investigated by various solution NMR measurements, viz., (1)H and (13)C chemical shifts (δH and δC), (1)H nonselective spin-lattice relaxation times (T1NS) and selective spin-lattice relaxation times (T1SEL), as well as spin-spin relaxation times (T2). Furthermore, we have employed saturation transfer difference (STD) NMR method to monitor the site of GA, CA, PG, and EGCG which are in close proximity to collagen. It is found that -COOH group of GA provides an important contribution for the interaction of GA with collagen, as evidenced from (13)C analysis, while PG, which is devoid of -COOH group in comparison to GA, does not show any significant interaction with collagen. STD NMR data indicates that the resonances of A-ring (H2', H5' and H6') and C-ring (H6 and H8) protons of CA, and A-ring (H2' and H6'), C-ring (H6 and H8), and D-ring (H2″and H6″) protons of EGCG persist in the spectra, demonstrating that these protons are in spatial proximity to collagen, which is further validated by independent proton spin-relaxation measurement and analysis. The selective (1)H T1 measurements of polyphenols in the presence of protein at various concentrations have enabled us to determine their binding affinities with collagen. EGCG exhibits high binding affinity with collagen followed by CA, GA, and PG. Further, NMR results propose that presence of gallic acid moiety in a small molecule increases its affinity with collagen. Our experimental findings provide molecular insights on the binding of collagen and plant polyphenols.

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Section: Introductionmentioning

confidence: 99%

Molecular Level Insights on Collagen–Polyphenols Interaction Using Spin–Relaxation and Saturation Transfer Difference NMR

et al. 2015

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“…Figure (B) showed that EGCG at high concentrations (≥50 μg/mL) dramatically reduced final surface pressure of SCN dispersion (7.40 to 3.93 mN/m). Considering the critical role of hydrophobic amino residues in protein surfactant (Damodaran, ), it was reasonable to postulate that EGCG initiated this process by limiting exposure of hydrophobic amino residues through either direct binding (Jobstl, Howse, Fairclough, & Williamson, ) or reducing protein conformational flexibility (Sironi et al., ), which led to suppressed protein absorption and reduction of SCN surface concentration (Sausse, Aguie‐Beghin, & Douillard, ). SCN‐EGCG interactions can also reduce effective SCN level in solution by stimulating micelle formation (Haratifar & Corredig, ).…”

Section: Resultsmentioning

confidence: 99%

Interactions Between Flavonoid‐Rich Extracts and Sodium Caseinate Modulate Protein Functionality and Flavonoid Bioaccessibility in Model Food Systems

Elegbede

Jones

et al. 2018

Journal of Food Science

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As information on the beneficial health effects of flavonoids expands, it is likely that usage of these ingredients in consumer foods will increase. However, the necessary levels to provide such benefits may exceed those that begin to impact functionality of the macronutrients such as proteins. Flavonoid inclusion within protein matrices may modulate protein functionality in a food system and modify critical consumer traits or delivery of these beneficial plant-derived components. The product matrices utilized in this study offer relevant model systems to evaluate how fortification with flavonoid-rich extracts allows for differing effects on formability and stability of the protein-based systems, and on bioaccessibility of fortified flavonoid extracts.

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“…This result aligns with past publications: Manthalkar et al [48] stated "Bior4. 4 gives the best classification performance" and Prabhakar and Reddy [49] also found bior4.4 performed the best among all wavelets. …”

Section: Optimal Waveletmentioning

confidence: 91%

“…For example, researchers found antioxidants in tea may be capable of protecting against diseases: Alzheimer's disease [2], Parkinson's disease [3], neurodegenerative disease [4], blood pressure and cardiovascular disease [5], colon cancer [6], breast cancer [7], lung cancer [8], etc.…”

Section: Introductionmentioning

confidence: 99%

Identification of Green, Oolong and Black Teas in China via Wavelet Packet Entropy and Fuzzy Support Vector Machine

Wang

Yang

Zhang

et al. 2015

Entropy

104

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Abstract:To develop an automatic tea-category identification system with a high recall rate, we proposed a computer-vision and machine-learning based system, which did not require expensive signal acquiring devices and time-consuming procedures. We captured 300 tea images using a 3-CCD digital camera, and then extracted 64 color histogram features and 16 wavelet packet entropy (WPE) features to obtain color information and texture information, respectively. Principal component analysis was used to reduce features, which were fed into a fuzzy support vector machine (FSVM). Winner-take-all (WTA) was introduced to help the classifier deal with this 3-class problem. The 10 × 10-fold stratified cross-validation results show that the proposed FSVM + WTA method yields an overall recall rate of 97.77%, higher than 5 existing methods. In addition, the number of reduced features is only five, less than or equal to existing methods. The proposed method is effective for tea identification.

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Natural Compounds against Neurodegenerative Diseases: Molecular Characterization of the Interaction of Catechins from Green Tea with Aβ1–42, PrP106–126, and Ataxin‐3 Oligomers

Cited by 39 publications

References 50 publications

Molecular Level Insights on Collagen–Polyphenols Interaction Using Spin–Relaxation and Saturation Transfer Difference NMR

Molecular Level Insights on Collagen–Polyphenols Interaction Using Spin–Relaxation and Saturation Transfer Difference NMR

Interactions Between Flavonoid‐Rich Extracts and Sodium Caseinate Modulate Protein Functionality and Flavonoid Bioaccessibility in Model Food Systems

Identification of Green, Oolong and Black Teas in China via Wavelet Packet Entropy and Fuzzy Support Vector Machine

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