Publications by authors named "Pradeep Hanumanthappa"

It is well established fact that peptides from various foods offer human health benefits displaying diverse functionalities. Millets considered as super foods is a major alternative in recent days for traditional diet being rich in proteins and fibre along with trace minerals and vitamins. In this connection, proteins from Buckwheat and Quinoa were digested by in vitro simulation digestion for the generation of peptides, analyzed by nLC-MS/MS and the functional annotations of the identified proteins/peptides were carried out.

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Background: Parkinson's disease ranks second, after Alzheimer's as the major neurodegenerative disorder, for which no cure or disease-modifying therapies exist. Ample evidence indicate that PD manifests as a result of impaired anti-oxidative machinery leading to neuronal death wherein Cullin-3 has ascended as a potential therapeutic target for diseases involving damaged anti-oxidative machinery.

Objective: The design of target specific inhibitors for the Cullin-3 protein might be a promising strategy to increase the Nrf2 levels and to decrease the possibility of "off-target" toxic properties.

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Background: L-asparaginase (L-ASN) is an anti-cancer enzyme therapeutic drug that exerts cytotoxicity via inhibition of protein synthesis through depletion of L-asparagine in the tumor microenvironment. The therapeutic performance of the native drug is partial due to the associated instability, reduced half-life and immunogenic complications.

Objective: In this study, we attempted the modification of recombinant L-asparaginase with PEG and an integrated computational strategy to probe the PEGylation in the protein to understand the biological stability/activity imparted by PEG.

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Parkinson's disease is the second most common neurodegenerative disorder, for which no cure or disease-modifying therapies exist. It is evident that mechanisms impairing mitochondrial dynamics will damage cell signaling pathways, leading to neuronal death that manifests as Parkinson's disease. Dynamin related protein1, a highly conserved profission protein that catalyzes the process of mitochondrial fission, is also associated with the excessive fragmentation of mitochondria, impaired mitochondrial dynamics and cell death.

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Glycogen synthase kinase 3β (GSK-3β) is implicated in diverse cellular processes such as cell signaling and survival. Accumulating lines of evidence indicate that increased GSK-3β activity contributes to neuronal death and pathogenesis of ischemic stroke. Considering predominant roles of GSK-3β in neuronal apoptosis, modulation of this protein kinase is a reliable strategy for ischemic neuroprotection.

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Mitochondria are the center of metabolism and the critical role of aberrant mitochondrial fission in the onset and progression of a wide range of human diseases such as neurodegenerative disorders, cardiovascular disease, ischemic stroke and diabetes, is slowly becoming recognized. Under physiological conditions, mitochondrial structure is predominantly regulated by cycles of fusion and fission, which is very crucial for the maintenance of cellular homeostasis. Dynamin-related protein 1 is a GTPase that catalyzes the process of mitochondrial fission and is also associated with the excessive fragmentation of mitochondria, impaired mitochondrial dynamics and cell death.

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Ischemic stroke is the second leading cause of death and disability worldwide and is associated with significant clinical and socioeconomic implications, emphasizing the need for effective therapies. Several neuroprotective strategies have failed in clinical trials because of poor knowledge of the molecular processes flanked with ischemic stroke. Therefore, uncovering the molecular processes involved in ischemic brain injury is of critical importance.

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In the present report, 3D-QSAR analysis was executed on the previously synthesized and evaluated derivatives of isoquinolin-1-ones and quinazolin-4-ones; potent inhibitors of tumor necrosis factor α (TNFα). Statistically significant 3D-QSAR models were generated using 42 molecules in the training set. The predictive ability of models was determined using a randomly chosen test set of 16 molecules, which gave excellent predictive correlation coefficients for 3-D models, suggesting good predictive index.

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