Promising Compounds for Treatment of Covid-19

Yasir Daanial Khan, Muhammad Sohaib Roomi

Abstract


The study spans over-identification of phytochemicals suited for the treatment of COVID-19. The study focuses on the chemical that has a tendency to bind with Human ACE2 protein and two of the main Sars-Cov-2 proteins which are the Spike protein and the RNA-directed RNA polymerase (RdRp) protein. After screening a large list of phytochemicals two of the compound i.e. Kansuinine B and Digitoxin were found to have promising traits for the treatment of COVID-19. Both the compounds have been in use for centuries. Digitoxin was extracted from Foxglove seeds in the 18th century for heart-related illnesses. Kansuinine B originates from a Chinese herb Euphorbia Kansui (E. Kansui) E. Kansui has been widely used in herbal medicine for a multitude of illnesses including lungs related diseases. Studies also show that it has the ability to suppress cytokine response through the expression of the SOCS3 gene. In-silico simulations show that both these compounds have a better affinity and binding properties with these three proteins as compared to many other drugs under trial for COVID-19 like Remdesivir, Ritonavir, Famotidine, Camostat Mesylate, and Hesperidin. A treatment based on the combination of both compounds can be very effective. Any self-medication of both the compounds is highly discouraged as misuse of both the compounds can be very harmful.

Full Text:

PDF

References


X. Huang, R. Pearce, and Y. Zhang, "Computational Design of Peptides to Block Binding of the SARS-CoV-2 Spike Protein to Human ACE2," bioRxiv, 2020.

S. Kim et al., "PubChem substance and compound databases," Nucleic acids research, vol. 44, no. D1, pp. D1202-D1213, 2016.

M. A. Akmal, W. Hussain, N. Rasool, Y. D. Khan, S. A. Khan, and K.-C. Chou, "Using Chou's 5-steps rule to predict O-linked serine glycosylation sites by blending position relative features and statistical moment," IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2020.

M. A. Akmal, N. Rasool, and Y. D. Khan, "Prediction of N-linked glycosylation sites using position relative features and statistical moments," PloS one, vol. 12, no. 8, 2017.

M. Awais, W. Hussain, Y. D. Khan, N. Rasool, S. A. Khan, and K.-C. Chou, "iPhosH-PseAAC: Identify phosphohistidine sites in proteins by blending statistical moments and position relative features according to the Chou's 5-step rule and general pseudo amino acid composition," IEEE/ACM transactions on computational biology and bioinformatics, 2019.

O. Barukab, Y. D. Khan, S. A. Khan, and K.-C. Chou, "iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou’s 5-steps Rule and Pseudo Components," Current Genomics, vol. 20, no. 4, pp. 306-320, 2019.

A. H. Butt, S. Alkhalaf, S. Iqbal, and Y. D. Khan, "EnhancerP-2L: A Gene regulatory site identification tool for DNA enhancer region using CREs motifs," bioRxiv, 2020.

A. H. Butt, S. A. Khan, H. Jamil, N. Rasool, and Y. D. Khan, "A prediction model for membrane proteins using moments based features," BioMed research international, vol. 2016, 2016.

A. H. Butt and Y. D. Khan, "Prediction of S-Sulfenylation Sites Using Statistical Moments Based Features via CHOU’S 5-Step Rule," International Journal of Peptide Research and Therapeutics, pp. 1-11, 2019.

A. H. Butt and Y. D. Khan, "CanLect-Pred: A Cancer Therapeutics Tool for Prediction of Target Cancerlectins Using Experiential Annotated Proteomic Sequences," IEEE Access, vol. 8, pp. 9520-9531, 2019.

A. H. Butt, N. Rasool, and Y. D. Khan, "A treatise to computational approaches towards prediction of membrane protein and its subtypes," The Journal of membrane biology, vol. 250, no. 1, pp. 55-76, 2017.

A. H. Butt, N. Rasool, and Y. D. Khan, "Predicting membrane proteins and their types by extracting various sequence features into Chou’s general PseAAC," Molecular biology reports, vol. 45, no. 6, pp. 2295-2306, 2018.

A. H. Butt, N. Rasool, and Y. D. Khan, "Prediction of antioxidant proteins by incorporating statistical moments based features into Chou's PseAAC," Journal of theoretical biology, vol. 473, pp. 1-8, 2019.

A. Ehsan, K. Mahmood, Y. D. Khan, S. A. Khan, and K.-C. Chou, "A novel modeling in mathematical biology for classification of signal peptides," Scientific reports, vol. 8, no. 1, pp. 1-16, 2018.

A. Ehsan, M. K. Mahmood, Y. D. Khan, O. M. Barukab, S. A. Khan, and K.-C. Chou, "iHyd-PseAAC (EPSV): identifying hydroxylation sites in proteins by extracting enhanced position and sequence variant feature via chou's 5-step rule and general pseudo amino acid composition," Current genomics, vol. 20, no. 2, pp. 124-133, 2019.

A. W. Ghauri, Y. D. Khan, N. Rasool, S. A. Khan, and K.-C. Chou, "pNitro-Tyr-PseAAC: predict nitrotyrosine sites in proteins by incorporating five features into Chou’s general PseAAC," Current pharmaceutical design, vol. 24, no. 34, pp. 4034-4043, 2018.

W. Hussain, Y. D. Khan, N. Rasool, S. A. Khan, and K.-C. Chou, "SPalmitoylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins," Analytical biochemistry, vol. 568, pp. 14-23, 2019.

W. Hussain, Y. D. Khan, N. Rasool, S. A. Khan, and K.-C. Chou, "SPrenylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins," Journal of theoretical biology, vol. 468, pp. 1-11, 2019.

S. Ilyas, W. Hussain, A. Ashraf, Y. D. Khan, S. A. Khan, and K.-C. Chou, "iMethylK-PseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAAC via Chou’s 5-steps Rule," Current Genomics, vol. 20, no. 4, pp. 275-292, 2019.

S. A. Khan, Y. D. Khan, S. Ahmad, and K. H. Allehaibi, "N-MyristoylG-PseAAC: sequence-based prediction of N-myristoyl glycine sites in proteins by integration of PseAAC and statistical moments," Letters in Organic Chemistry, vol. 16, no. 3, pp. 226-234, 2019.

Y. D. Khan, F. Ahmed, and S. A. Khan, "Situation recognition using image moments and recurrent neural networks," Neural Computing and Applications, vol. 24, no. 7-8, pp. 1519-1529, 2014.

Y. D. Khan, N. Amin, W. Hussain, N. Rasool, S. A. Khan, and K.-C. Chou, "iProtease-PseAAC (2L): A two-layer predictor for identifying proteases and their types using Chou's 5-step-rule and general PseAAC," Analytical biochemistry, vol. 588, p. 113477, 2020.

Y. D. Khan, A. Batool, N. Rasool, S. A. Khan, and K.-C. Chou, "Prediction of nitrosocysteine sites using position and composition variant features," Letters in Organic Chemistry, vol. 16, no. 4, pp. 283-293, 2019.

Y. D. Khan et al., "An efficient algorithm for recognition of human actions," The Scientific World Journal, vol. 2014, 2014.

Y. D. Khan, N. Rasool, W. Hussain, S. A. Khan, and K.-C. Chou, "iPhosT-PseAAC: Identify phosphothreonine sites by incorporating sequence statistical moments into PseAAC," Analytical biochemistry, vol. 550, pp. 109-116, 2018.

Y. D. Khan, N. Rasool, W. Hussain, S. A. Khan, and K.-C. Chou, "iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC," Molecular biology reports, vol. 45, no. 6, pp. 2501-2509, 2018.

S. J. Malebary, M. S. ur Rehman, and Y. D. Khan, "iCrotoK-PseAAC: Identify lysine crotonylation sites by blending position relative statistical features according to the Chou’s 5-step rule," PloS one, vol. 14, no. 11, 2019.

N. Rasool, W. Husssain, and Y. D. Khan, "Revelation of enzyme activity of mutant pyrazinamidases from Mycobacterium tuberculosis upon binding with various metals using quantum mechanical approach," Computational biology and chemistry, vol. 83, p. 107108, 2019.




DOI: http://dx.doi.org/10.21015/vtcs.v1i1.555

Refbacks

  • There are currently no refbacks.