Inhibition of amyloid fibrillation of γD-crystallin model peptide by the cochineal Carmine

Abu-Hussien, Malak; Viswanathan, Guru KrishnaKumar; Borisover, Lia; Engel, Hamutal; Zayit‐Soudry, Shiri; Gazit, Ehud; Segal, Daniel

doi:10.1016/j.ijbiomac.2020.12.106

Cited by 5 publications

(5 citation statements)

References 49 publications

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“…To test this, we selected two hexapeptides corresponding to the residues found in the trypsin fragments (peptide 13: 129-135, peptide 14: 121-126) with the highest AggreProt score, and two hexapeptides (peptide 16: 94-99, peptide 17: 150-155) which were not identified by the trypsin digestion and their AggreProt score is close to zero ( Figure 4a ). Additionally, we included one more hexapeptide with high AggreProt score (peptide 15: 41-46) which is annotated as non-amyloid in the AmyPro despite experimentally characterised to form amyloid fibrils 80 . The TEM analysis of the hexapeptides after 4 weeks of incubation reveals that the AggreProt predictions were correct ( Figure 4a ) and demonstrating that the annotations in both AmyPro and CPAD2.0 49 databases are indeed not accurate.…”

Section: Resultsmentioning

confidence: 99%

Prediction of Aggregation Prone Regions in Proteins Using Deep Neural Networks and Their Suppression by Computational Design

Cima,

Kunka,

Grakova

et al. 2024

Preprint

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Protein aggregation is a hallmark of multiple neurodegenerative diseases and a great hindrance in recombinant protein production, handling, and storage. Identification of aggregation prone residues or regions (APRs) in proteins and their suppression by mutations is a powerful and straightforward strategy for improving protein solubility and yield, which significantly increases their application potential. Towards this, we developed a deep neural network based predictor that generates residue level aggregation profile for one or several input protein sequences. The model was trained on a set of hexapeptides with experimentally characterised aggregation propensities and validated on two independent sets of data including hexapeptides and full-length proteins with annotated APRs. In both cases, the model matched, or outperformed the state-of-the-art algorithms. Its performance was further verified using a set of 34 hexapeptides identified in model haloalkane dehalogenase LinB and seven proteins from AmyPro database. The experimental data from Thioflavin T fluorescence and transmission electron microscopy matched the predictions in 79% of the cases, and revealed inaccuracies in the database annotations. Finally, the utility of the algorithm was demonstrated by identifying APRs in a model enzyme (LinB) and designing aggregation-suppressing mutations in the exposed regions. The designed variants showed reduced aggregation propensity, increased solubility and improved yield, with up to a 100% enhancement compared to the wild type for the best one.

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Section: Resultsmentioning

confidence: 99%

Prediction of Aggregation Prone Regions in Proteins Using Deep Neural Networks and Their Suppression by Computational Design

Cima,

Kunka,

Grakova

et al. 2024

Preprint

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show abstract

“…Recently, a similar study demonstrated that emodin, daunorubicin, and Adriamycin inhibit the aggregation of tau protein into paired helical flaments [53]. Likewise, carmine, an anthraquinone core attached to a glucose moiety, efectively reduced aggregation of the model 41 GCWMLY 46 peptide fragment (GDC6 peptide) in a dose-dependent manner [54]. Natural quinones remain an important source of novel anti-amyloid inhibitors.…”

Section: Discussionmentioning

confidence: 99%

Anti-Glycation, Anti-β-Amyloid Aggregation, and Antioxidant Effect of Cassia Seed-Derived Secondary Metabolites

Paudel,

Prajapati,

Lim

et al. 2023

Journal of Food Biochemistry

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Aggregation and non-enzymatic glycation of amyloidogenic peptides, amyloid-beta (Aβ), and insulin are key features of neuropathology. In this study, we evaluate the anti-glycation effect of Cassia seed-derived secondary metabolites on human insulin and bovine serum albumin, as well as their anti-Aβ aggregation and antioxidant effects using in vitro spectrofluorometric method, thioflavin T fluorescence, and peroxynitrite (ONOO‒) scavenging assay. Furthermore, molecular docking simulation was performed to investigate the binding characteristics of test compounds and Aβ42 peptide. Among 38 compounds, anthraquinones 9–12 and 14–18; naphthopyrones 24, 25, 27, and 33–36; and 38 from the naphthalenes and naphthalenic lactone groups showed moderate-to-good inhibition of AGE formation with IC50 values ranging from 7.52 ± 1.19 to 155.86 ± 0.79 µM. Likewise, compounds 5, 24, 15, 16, 27, and 20 showed good inhibition of D-ribose-mediated glycation of human insulin, with an IC50 value range of 46.37 ± 4.06 to 97.69 ± 7.88 µM. In the thioflavin-T assay, compounds 8 and 12 showed promising inhibition of Aβ aggregation, comparable to that of the reference compound morin. Molecular docking simulations confirmed that these active compounds have strong potential to interact with Aβ42 peptides and interrupt their self-assembly and conformational transformation, thereby inhibiting Aβ42 aggregation. In addition, compounds 5, 8, 10, 14, and 35 scavenged ONOO− at low concentrations. Overall, Cassia compounds’ anti-glycation, anti-Aβ aggregation, and antioxidant effects warrant further in vivo studies to evaluate their potential neuroprotective effects against comorbid AD and diabetes.

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“…Several studies by others have reported the existence of small molecules that bind to γ-crystallins, including lanosterol ( 59 ), cochineal carmine ( 60 ), ortho-Vanilin ( 61 ), quercetin ( 62 ), hesperetin ( 63 ), and sodium citrate ( 64 ). Moreover, small molecules with either demonstrated potential anticataract properties involving antiaggregation activity against crystallins include rosemarinic acid ( 65 ), morin ( 66 , 67 ), 25-hydrocholesterol ( 68 ), epigallocatechin gallate ( 69 ), and myoinositol ( 70 ).…”

Section: Discussionmentioning

confidence: 99%

α-Crystallin chaperone mimetic drugs inhibit lens γ-crystallin aggregation: Potential role for cataract prevention

Islam¹,

Michael²,

Frank³

et al. 2022

Journal of Biological Chemistry

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Inhibition of amyloid fibrillation of γD-crystallin model peptide by the cochineal Carmine

Cited by 5 publications

References 49 publications

Prediction of Aggregation Prone Regions in Proteins Using Deep Neural Networks and Their Suppression by Computational Design

Prediction of Aggregation Prone Regions in Proteins Using Deep Neural Networks and Their Suppression by Computational Design

Anti-Glycation, Anti-β-Amyloid Aggregation, and Antioxidant Effect of Cassia Seed-Derived Secondary Metabolites

α-Crystallin chaperone mimetic drugs inhibit lens γ-crystallin aggregation: Potential role for cataract prevention

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