Variolin B from sea sponge against lung cancer: a multitargeted molecular docking with fingerprinting and molecular dynamics simulation study

Abstract

Lung Cancer is the one that causes more fatalities in the world compared to other cancers, and its uniqueness is that it can be found in both males and females. However, recent data has shown that males are more affected due to lifestyle habits like smoking, tobacco consumption and inhaling polluted air. The World Health Organization has kept lung cancer on its priority list as it causes 1.8 million deaths worldwide each year, and the predictions show that the cases are going to increase year by year, and by 2050, there can be 3.8 million new cases and 3.2 million deaths, and the global health system is not prepared for it. Also, finding drug candidates that can help shrink cancerous cells and lead to their death is essential to reduce global mortality. The system needs drug compounds that can inhibit multiple paths together not to enter drug resistance quickly and to reduce costs. Our study identified a compound named Variolin B (DB08694) that belongs to the organic compounds class of pyrrolopyridines. The identified compound can inhibit multiple proteins, drastically reducing the global burden. Variolin B was identified as a potential candidate against lung cancer using the multisampling algorithm such as HTVS, SP, and XP, followed by MM\GBSA calculations showing the docking score of −9.245 Kcal/mol to −5.92 Kcal/mol. Also, we have validated it with ADMET predictions and molecular fingerprinting to analyse the interaction patterns. Further, the study was extended to molecular dynamics simulations for 100 ns to understand the complex stability and simulative interactions. The complex’s overall molecular dynamics simulation helped us understand that the identified candidate is stable with the lowest deviation and fluctuations.

Communicated by Ramaswamy H. Sarma

Keywords:

• Lung cancer
• multitargeted inhibitor
• molecular fingerprinting
• molecular dynamics simulation
• ADMET

Acknowledgement

The authors would like to acknowledge the Deanship of Scientific Research, Taif University, for Funding this work.

Disclosure statement

The authors declare no potential competing or conflict of interest.

Ethical responsibilities

Since this study is entirely in-silico, ethical obligations are not applicable because they do not directly involve humans or other organisms.

Consent for publication

All authors reviewed the results, approved the final version, and provided their consent to submit the manuscript to the journal.

Data and material availability

No supplementary materials are to be provided.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.