Proteomic Analysis of Medicinal Plant Calotropis Gigantea by In Silico Peptide Mass Fingerprinting

Author(s): Saad Ur Rehman*, Muhammad Rizwan, Sajid Khan, Azhar Mehmood, Anum Munir

Journal Name: Current Computer-Aided Drug Design

Volume 17 , Issue 2 , 2021

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Graphical Abstract:


Medicinal plants are the basic source of medicinal compounds traditionally used for the treatment of human diseases. Calotropis gigantea is 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 the database and identified.

Objective: The purpose of the study is to identify the peptides having anti-cancerous properties by in silico peptide mass fingerprinting.

Methods: The calculation of in silico peptide masses is done through the ExPASy PeptideMass and these masses are used to identify the peptides from the MASCOT online server. Anticancer probability is calculated by iACP server, docking of active peptides is done by CABS-dock the server.

Results: The anti-cancer 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.

Keywords: Peptide mass fingerprinting, calotropis gigantea, peptides, docking, anticancer, breast cancer, human leukemia, MASCOT.

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Article Details

Year: 2021
Published on: 18 May, 2021
Page: [254 - 265]
Pages: 12
DOI: 10.2174/1573409916666200219114531
Price: $65

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