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Proteomic Analysis of Medicinal Plant Calotropis Gigantea by In Silico Peptide Mass Fingerprinting
Current Computer-Aided Drug Design ( IF 1.5 ) Pub Date : 2021-03-31 , DOI: 10.2174/1573409916666200219114531
Saad Ur Rehman 1 , Muhammad Rizwan 1 , Sajid Khan 1 , Azhar Mehmood 1 , Anum Munir 1
Affiliation  

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.



中文翻译:

硅肽质量指纹图谱对药用植物大角豆的蛋白质组学分析

药用植物是传统上用于治疗人类疾病的药用化合物的基本来源。牛蒡属植物界夹竹桃科药用植物,通常具有多种药用功效,可治疗多种疾病。

背景:肽质量指纹图谱 (PMF) 通过比较先前存储在数据库中并已识别的氨基酸序列来识别参考蛋白质数据库中的蛋白质。

目的:本研究的目的是通过计算机肽质量指纹图谱鉴定具有抗癌特性的肽。

方法:计算机肽质量的计算是通过 ExPASy PeptideMass 完成的,这些质量用于识别来自 MASCOT 在线服务器的肽。抗癌概率由 iACP 服务器计算,活性肽的对接由 CABS 对接服务器完成。

结果:抗癌肽通过MASCOT肽质量指纹服务器进行鉴定,对鉴定出的肽进行筛选,仅筛选出抗癌肽。从头肽结构预测用于 PEP-FOLD 3 服务器的 3D 结构预测。对接结果证实与乳腺癌受体蛋白 BRCA1 的相互作用氨基酸强结合,显示与活性链的最佳肽结合,与肽对接的人白血病蛋白显示出准确的结合。

结论:这些肽是稳定和功能性的,是治疗癌症和许多其他致命疾病的最佳途径。

更新日期:2021-05-18
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