Polyether sulfone assisted shape construction of Calotropis gigantea fiber for preparing a sustainable and reusable oil sorbent

The suspensions of alcoholic extract of root bark of the plant Calotropis gigantea in 0.6% carboxy methyl cellulose (CMC) were evaluated for hepatoprotective activity in Wistar albino rats by inducing hepatic injury with D-galactosamine (400 mg/kg). Alcoholic extract of root bark of the plant Calotropis gigantea at an oral dose of 200 mg/kg and 400 mg/kg exhibited a significant (P<0.001, P<0.01 and P<0.05) protection effect by normalizing the levels of aspartate amino transferase (ASAT/ GOT), alanine amino transferase (ALAT/GPT), alkaline phosphatase (ALP), total bilirubin (TB), lactate dehydrogenase (LDH), which were significantly (P<0.001) increased in rats by treatment with 400 mg/kg i.p. of D-galactosamine. Silymarin (25 mg/kg), a known hepatoprotective drug used for comparison exhibited significant activity (P<0.001).

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Proteomic Analysis of Medicinal Plant Calotropis Gigantea by insilico Peptide Mass Fingerprinting

Current Computer - Aided Drug Design ◽

10.2174/1573409916666200219114531 ◽

2020 ◽

Vol 16 ◽

Author(s):

Saad Ur Rehman ◽

Muhammad Rizwan ◽

Sajid Khan ◽

Azhar Mehmood ◽

Anum Munir

Keyword(s):

Medicinal Plant ◽

Structure Prediction ◽

3D Structure ◽

Peptide Mass Fingerprinting ◽

Protein Docking ◽

Receptor Protein ◽

Human Leukemia ◽

Protein Database ◽

Calotropis Gigantea ◽

Peptide Mass

: Medicinal plants are the basic source of medicinal compounds traditionally used for the treatment of human diseases. Calotropis gigantea a medicinal plant belonging to the family of Apocynaceae in the plant kingdom and subfamily Asclepiadaceae usually bearing multiple medicinal properties to cure a variety of diseases. Background: The Peptide Mass Fingerprinting (PMF) identifies the proteins from a reference protein database by comparing the amino acid sequence that is previously stored in a database and identified. Method: The calculation of insilico peptide masses is done through the ExPASy PeptideMass and these masses are used to identify the peptides from MASCOT online server. Anticancer probability is calculated from the iACP server, docking of active peptides is done by CABS-dock the server. Objective: The purpose of the study is to identify the peptides having anti-cancerous properties by in-silico peptide mass fingerprinting. Results : The anti-cancerous peptides are identified with the MASCOT peptide mass fingerprinting server, the identified peptides are screened and only the anti-cancer are selected. De novo peptide structure prediction is used for 3D structure prediction by PEP-FOLD 3 server. The docking results confirm strong bonding with the interacting amino acids of the receptor protein of breast cancer BRCA1 which shows the best peptide binding to the Active chain, the human leukemia protein docking with peptides shows the accurate binding. Conclusion : These peptides are stable and functional and are the best way for the treatment of cancer and many other deadly diseases.

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