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Insilico Anticancer Peptide Prediction from Curcuma longa

Sunil Kumar Suryawanshi1, Usha Chouhan2, Jyoti Kant Choudhari2,*

1 Centre for Knowledge Management & e-Governance, Atal Bihari Vajpayee Institute of Good Governance & Policy Analysis, Bhopal, India
2 Department of Bioinformatics, Maulana Azad National Institute of Technology, Bhopal, India

* Corresponding Author: Jyoti Kant Choudhari. Email: email

Molecular & Cellular Biomechanics 2022, 19(4), 191-208. https://doi.org/10.32604/mcb.2022.023911

Abstract

Cancer is the second biggest cause of death globally, and the use of therapeutic peptides to specifically target and destroy cancer cells has gotten much interest. Cancer peptides or vaccinations are utilized to treat cancer nowadays, apart from chemotherapy, which has significant discomfort, side effects and costly. It is time demanding to identify and predict potential anticancer peptides using computational biology approaches. Thus, 3-D molecular modeling is being used to find possible ACP candidates. In this research, Curcuma longa has predicted peptide sequences were docked on breast cancer receptors and used a molecular docking technique to assess the anticipated peptides’ binding affinities to MHC molecules. A similar approach was utilized to simulate the interactions of the chosen peptide with the TCR. Additionally, the Pep10 LIRQHVASNIGIAKSKIREPIV was examined, and our findings indicated interaction with MHC classes I and II. However, the maximum binding energy was obtained with TCR at 695.61, giving strength through eight hydrogen bonds. Similarly, the Pep20, GAIIGNRKIKLQPHIIIRID, the projected, has the most significant overall binding energy with MHC classes I and II but a lower global E total value with TCR, namely −600.97 kj/Mol, and also four hydrogen bonds. This research could lead to the development of novel anticancer drugs based on the anticancer activity of the Curcuma longa medicinal plant.

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Cite This Article

Suryawanshi, S. K., Chouhan, U., Choudhari, J. K. (2022). Insilico Anticancer Peptide Prediction from Curcuma longa. Molecular & Cellular Biomechanics, 19(4), 191–208.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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