Bafilomycin A1: A potential candidate drug for COVID-19 Treatment
Sharayu Salegaonkar1, Trapti Sharma1, Preeti P.1, Robin Sinha1, Aruna vigneshwari, Kamal Rawal#1
Amity Institute of Biotechnology, Amity University Uttar Pradesh, India.
#Corresponding Author
Email ID: kamalrawal@gmail.com
Centre for Computational Biology and Bioinformatics, AIB
Amity University, Noida.
Abstract
The crisis of the COVID-19 pandemic around the world has been devastating as many lives have been lost. There is an urgent need for the right therapeutic drug to control the disease. Drug development is a time-consuming process, hence the need to approach drug repurposing. Bafilomycin A1 is a drug that was used against many viruses hence used for the analysis using the developed pipeline. The drug which will be repurposed should be analyzed for its efficiency against the COVID-19. The CoV- DrugX Pipeline is developed for drug repurposing. The CoV-DrugX pipeline is available on (http://drugx.kamalrawal.in/drugx/) which integrates that should be considered for the repurposing of drugs against COVID-19. Bafilomycin A1 is a drug that was used against many viruses hence used for the analysis using the developed pipeline. The pipeline predicted and resulted in scores for the individual modules. The CoV-DrugX pipeline provides key parameters indicating the suitability of Bafilomycin A1 as a potential drug candidate for the treatment of COVID-19. The CoV-DrugX pipeline provides appropriate SI (sensitivity index) and PI (predictive index) scores which predicts that the drug Bafilomycin A1 has an efficiency of the drug repurposing.
Keywords: Drug repurposing, Bioinformatics, COVID-19, SAR-CoV-2, Bafilomycin A1, Artificial Intelligence
The coronavirus disease (COVID-19) pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) is currently a global public health concern [Akinlalu et al., 2021]. The current crisis report shows there have been more than 23 million cases and more than 840,000 deaths from COVID19 worldwide. No effective treatment or vaccine has been found.
Previously, we have developed several machine learning and bioinformatics platforms. These include text mining and network biology-based systems [Jagannadham et al., 2016], vaccine discovery systems [Rawal et al. 2021], next-generation sequencing analysis systems for cancer and other genomes [Preeti et al.,2021, Rawal et al 2011, Mandal et al 2006].
The Coronavirus belongs to the virus family Coronaviridae. SARS CoV-2 belongs to Beta-coronavirus. It has been reported that Alpha- and Beta-coronaviruses are predominant in bats and rodents whereas Delta- and Gamma-coronavirus gene sources are the avian species [Akinlalu et al., 2021]. Traditional approaches for finding new drugs against COVID-19 take a lot of time which is shortened by repurposing the available drugs [Sultana et al., 2020]. This also skips the long phase of drug discovery which is a clinical trial thereby reducing the cost [Pushpakom et al., 2018].
The compound Bafilomycin A1 (see Figure 1) belongs to the class of organic compounds known as macrolides and analogs. They are organic compounds containing a lactone ring of at least twelve members. Bafilomycin A1 also improved the existence and proliferation of Vero E6 cells after SARS-CoV-2 infection.
Moreover, in the hACE2 transgenic mice model of SARS-CoV-2 infection, Bafilomycin A1 reduced viral replication in lung tissues and alleviated viral pneumonia with reduced inflammatory exudation and infiltration in peribronchial and perivascular tissues. It also improves the structures of the alveolar septum and pulmonary alveoli [Shang et al., 2021]. Here we predict whether Bafilomycin A1 could be repurposed for COVID-19.
Infection of epithelial cells with viruses induces the production of several cytokines such as interleukin IL-1, IL-6, IL-8, tumor necrosis factor (TNF)-α, and granulocyte-macrophage colony-stimulating factor (see Figure 2) [Subauste et al., 1995]. These cytokines are known to mediate a wide variety of proinflammatory and immunoregulatory effects and may play an important role in the pathogenesis of viral infections [Terajima et al., 1997].
For a better understanding of the disease, it is necessary to identify the drug candidate for repurposing the drug against the disease. The selected drug must either inhibit/suppress the lifecycle of the COVID-19 or it should have the potential to counteract the effect caused by the coronavirus.
CoV-DrugX Pipeline (CoV-DrugX Pipeline | Home (kamalrawal.in)), a web-based tool that is developed on understanding the entire mechanism needed for drug candidate identification will determine whether the given drug can be repurposed for COVID-19. This does not need supercomputing systems or coding knowledge and gives efficient results with the SMILES of the drug alone.
CoV-DrugX Pipeline (http://drugx.kamalrawal.in/drugx/) takes the SMILES of the drug Bafilomycin A1 as input from DrugBank. This pipeline has 13 modules that analyze all the properties of drugs and predict whether the drug can be repurposed for COVID-19 or not.
These modules include: drug-circuit, drug-target, drug_dock_human, drug_dock_viral, drug_dock_KG, drug_phenotype, drug_AI_ranking, drug_condition, drug_side_effect, drug_symptom, drug_gene_expression, drug_dl_11 and drug_200. These different modules check the capability of the drug for essential properties. Hence according to these properties, the drug Bafilomycin A1 is considered for the repurposing of COVID-19, interacting with the COVID-19 target.
The pipeline can be proceeded by selecting one module at a time for analyzing particular properties of the drug. The other way the pipeline can proceed is by selecting all the modules at the same time. On submitting the pipeline to the server, the post submission page will appear which shows the job ID and other details of the job. Within a few minutes, the pipeline will give the results page displaying the score for each module on selecting all the modules.
The category of bafilomycin refers to a toxic macrolide antibiotic that is derivatives of Streptomyces griseus. All these compounds appear in the same fermentation and they have similar biological activity. Bafilomycins are specific inhibitors of vacuolar-type H+-ATPase (V-ATPase). The most commonly utilized bafilomycin is bafilomycin A1. This is a useful tool as it can prevent the re-acidification of synaptic vesicles once they have undergone exocytosis.
We extract drug data from the DrugBank using the keyword: Bafilomycin A1 (DB06733). The SMILES of the Bafilomycin A1 drug can be extracted from the DrugBank. The SMILES annotations are copied in the notepad and uploaded to the same text file in the CoV-DrugX Pipeline. The smile was obtained as -
(CO[C@H]1\C=C\C=C(C)\C[C@H](C)[C@H](O)[C@H](C)\C=C(/C)\C=C(OC)\C(=O)O[C@@H]1[C@@H](C)[C@@H](O)[C@H](C)[C@@]1(O)C[C@@H](O)[C@H](C)[C@H](O1)C(C)C).
We used smiles of Bafilomycin as an input to the Cov-DrugX pipeline. The Cov-DrugX uses 15 different modules (See Figure 3) for analysis.
The CoV-DrugX pipeline contains 3 docking-based modules which include a human target-based docking module, viral proteins target-based module, and protein kinases associated with the human protein’s module.
The module named ‘Drug_Dock_Viral’ was used to dock drug Bafilomycin A1 against 23 viral proteins. Here we obtained binding affinities from the docking experiments. The average binding affinity of Bafilomycin A1 against 23 different viral proteins was -5.2 kcal per mol (See Table 2 and Figure 5). We found Nsp14, Npro, Nsp3, ORF3A, S_trimer, and Nsp13 as top-ranking binding viral proteins. For instance, Nsp14 had shown the best binding affinity with Bafilomycin A1 (-6.5 Kcal/mol). On the other hand, Nsp1 was found to show the least binding affinity (-2.9 Kcal/mol) (See Table 2).
Similarly, Bafilomycin A1 when docked against human proteins such as ACE2 and TMPRSS2, the binding energy was -7.2 and -4.7 Kcal/mol respectively (See Table 3). The human gene docked with the COVID-19 targets, involved in the COVID-19 pathway, interacting with important targets like AAK1, GAK, JAK2, and ACE2 (See Table 4). According to these properties, we will get the cumulative PI score for analyzing the drug for repurposing.
The cumulative PI score can be considered for analyzing the drug for repurposing. These results can be downloadable in a comma-separated format file (.csv file format). Bafilomycin A1 is analyzed for its properties, docked with SARS-CoV-2 targets, human targets.
From the results, it is understood that the drug Bafilomycin A1 has no active drug targets in the COVID-19 pathway, no interactions with AAK1, GAK, JAK2, ACE2. Docking with knowledge graph targets, SARS-Cov-2, or human targets by SARS-Cov-2 has minimal to no result indicating a lack of viability in its usage as a repurpose drug for COVID-19. Based on the analysis there is a likely chance of abnormal gene expression but it has shown no side effects implicated with COVID-19. Since it doesn't have any possible COVID-19 targets, the pipeline predicts that it may not cure any of the symptoms associated with COVID-19.
Supplementary Information: Information available on a given website.
The results from the pipeline can be visualized in the pipeline-interface and can be downloadable. The result obtained for Bafilomycin A1 is listed in the given table.
Dr. Kamal Rawal acknowledges the support provided by SERB, Department of Science and Technology (Grant ID: CVD/2020/000842). The project involved the usage of computational infrastructure (server etc) provided by the Department of Biotechnology (DBT), Ministry of Science and Technology Government of India (Grant ID: BT/PRI7252/BID/7/708/2016) and Robert J. Kleberg Jr. and Helen C. Kleberg Foundation and Baylor College of Medicine, Houston, Texas, USA. We are also thankful to Amity University for the support provided during the conduct of this study.
2. Jagannadham, J., Jaiswal, H.K., Agrawal, S., Rawal, K., Comprehensive map of molecules implicated in obesity", PLoS ONE, vol. 11, no. 2: e0146759. doi: 10.1371/journal.pone. 014 6759, 2016.
3. Jagannadham, J., Jaiswal, H.K., Rawal, K., Deciphering relationships in disease networks using computational approaches: Fatty Liver, PCOD, Osteoarthritis, cholelithiasis & hyperlipidemia", International Journal of PharmTech Research, vol. 8, no. 1, pp. 127-134, 2015.
4. Jagannadham, J., Jaiswal, H.K., Agarwal, S., Rawal, K., Biomedical Text Mining of Obesity, Diabetes and hypertension genes. International Journal of Pharmaceutical Sciences Review and Research. vol 33(2), 182-186, 2015.
5. Rawal, K. and Ramaswamy, R., "Genome-wide analysis of mobile genetic elements insertion sites. Nucl. Acids Res., vol. 39, no. 16, pp. 6864-6878, Sep. 2011.
6. Mandal, P., Rawal, K., Ramaswamy, R., Bhattacharya, A. and Bhattacharya, S. "Identification of Insertion hot spots for non-LTR retrotransposons: Computational and Biochemical application to Entamoeba histolytica." Nucl. Acids Res., vol. 34, no. 20, pp. 5752-5763, 2006. (Lead author and equal contribution).
7. Rawal, K., Dorji, S. Kumar, A., Ganguly, A. Grewal, A.S. “Identification and characterization of MGEs and their insertion sites in the gorilla genome”. Mobile Genetic Elements, vol.3, no.4, pp. e25675, 2012.
8. Rawal, K., Priya, A., Malik, A., Bahl, R., Ramaswamy, R., “Distribution of MGEs and their insertion sites in the Macaca mulatta genome”. Mobile Genetic Elements, vol.2, no.3, pp. 133-141, 2012.
9. Sultana J, Crisafulli S, Gabbay F, Lynn E, Shakir S, Trifirò G. Challenges for drug repurposing in the COVID-19 pandemic era. Frontiers in pharmacology. 2020 Nov 6;11:1657.
10. Pushpakom S, Iorio F, Eyers PA, Escott KJ, Hopper S, Wells A, Doig A, Guilliams T, Latimer J, McNamee C, Norris A. Drug repurposing: progress, challenges, and recommendations. Nature reviews Drug discovery. 2019 Jan;18(1):41-58.
11. Shang, C., Zhuang, X., Zhang, H., Li, Y., Zhu, Y., Lu, J., ... & Li, X. (2021). Inhibitors of endosomal acidification suppress SARS-CoV-2 replication and relieve viral pneumonia in hACE2 transgenic mice. Virology Journal, 18(1), 1-9.
12. Subauste, M. C., Jacoby, D. B., Richards, S. M., & Proud, D. (1995). Infection of a human respiratory epithelial cell line with rhinovirus. Induction of cytokine release and modulation of susceptibility to infection by cytokine exposure. The Journal of clinical investigation, 96(1), 549-557.
13. Terajima, M., Yamaya, M., Sekizawa, K., Okinaga, S., Suzuki, T., Yamada, N., ... & Sasaki, H. (1997). Rhinovirus infection of primary cultures of human tracheal epithelium: role of ICAM-1 and IL-1β. American Journal of Physiology-Lung Cellular and Molecular Physiology, 273(4), L749-L759.
14. Dev, B.B., Malik A., Rawal, K., “Detecting motifs and patterns at mobile genetic element insertion site”. Bioinformation, vol. 8, pp.777-786, 2012.
15. Bakre, A.A., Rawal, K., Ramaswamy, R., Bhattacharya, A. and Bhattacharya, S., “The LINEs and SINEs of Entamoeba histolytica: Comparative analysis and genomic distribution.” Experimental Parasitology, vol. 110, no. 3, pp. 207-213, 2005.
16. Agrawal, S., Rawal, K., Sahu, A., Mahajan, S., Garg, P. and Bahl, R., "To find gene distributions in PubMed abstracts using Perl software", Journal of Pharmacy Research 2013.
17. Jaiswal, H.K., Rawal, K., Jaganadham, J., Agrawal, S., “Evaluation of inhibition activity of Tetrahydrolipstatin analogs on Diacylglycerol lipase alpha using In-silico techniques”. Journal of Pharmacy Research, vol.5, no.6, pp. 3473-3477, 2012.
18. Rustagi Y, Jaiswal HK, Rawal, K., Kundu GC, Rani V (2015). Comparative Characterization of Cardiac Development Specific microRNAs: Fetal Regulators for Future. PLoS ONE.10(10): e0139359.
19. Gupta, R., Soni, Patnaik, Sood, I., Singh, R., Rawal, K., Rani, V. High AU content: A Signature of Upregulated miRNA in Cardiac Diseases, Bioinformation, vol. 3, pp.132-135, 2010
20. Abbasi, B. A., Saraf, D., Sharma, T., Sinha, R., Singh, S., Gupta, P. Rawal, K. (2020, April 8). Identification of vaccine targets & design of vaccine against SARS-CoV-2 coronavirus using computational and deep learning-based approaches.
21. Kamal Rawal, v, Abbasi, B. A., et al (2020). Design of a multi-epitope Chagas disease vaccine by computational analysis of the Trypanosoma cruzi CL Brenner proteome. (Communicated)
22. Rawal k, Sinha R et al (2020) Vaxi – DL: A web-based Deep Learning (DL) server to identify Potential Vaccine Candidates, (Bioinformatics-communicated) COVID-19
23. Jethani B., Rawal, K. et al (2020). Clinical Characteristics and Remedy Profile of Patients with COVID-19: Retrospective Cohort Study, Accepted (In Press)
24. Rawal, K., Sinha R et al (2020) To Study the Effect of Unconventional Treatment Protocol on COVID-19 patients in Delhi using AI-based techniques (Lancet- communicated) Machine Learning.
25. Rawal, K., Khurana T, Sharma H (2019). An extensive survey of molecular docking tools and their applications using text mining and deep curation strategies. PeerJ Preprints7: e27538v1