Toward the Identification of Natural Antiviral Drug Candidates against Merkel Cell Polyomavirus: Computational Drug Design Approaches

Amer H Asseri; Md Jahidul Alam; Faisal Alzahrani; Ahmed Khames; Mohammad Turhan Pathan; Mohammed A S Abourehab; Salman Hosawi; Rubaiat Ahmed; Sifat Ara Sultana; Nazia Fairooz Alam; Nafee-Ul Alam; Rahat Alam; Abdus Samad; Sushil Pokhrel; Jin Kyu Kim; Foysal Ahammad; Bonglee Kim; Shing Cheng Tan

doi:10.3390/ph15050501

Toward the Identification of Natural Antiviral Drug Candidates against Merkel Cell Polyomavirus: Computational Drug Design Approaches

Pharmaceuticals (Basel). 2022 Apr 20;15(5):501. doi: 10.3390/ph15050501.

Authors

Amer H Asseri^{1

2}, Md Jahidul Alam³, Faisal Alzahrani^{1

4}, Ahmed Khames⁵, Mohammad Turhan Pathan⁶, Mohammed A S Abourehab⁷, Salman Hosawi^{1

2}, Rubaiat Ahmed⁸, Sifat Ara Sultana⁹, Nazia Fairooz Alam⁸, Nafee-Ul Alam¹⁰, Rahat Alam^{11

12}, Abdus Samad^{11

12}, Sushil Pokhrel¹³, Jin Kyu Kim¹⁴, Foysal Ahammad^{12

15}, Bonglee Kim¹⁴, Shing Cheng Tan¹⁶

Affiliations

¹ Biochemistry Department, Faculty of Science, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia.
² Centre for Artificial Intelligence in Precision Medicines, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia.
³ Department of Applied Chemistry and Chemical Engineering, Noakhali Science and Technology University, Noakhali 3814, Bangladesh.
⁴ King Fahd Medical Research Center, Embryonic Stem Cells Unit, Department of Biochemistry, Faculty of Science, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia.
⁵ Department of Pharmaceutics and Industrial pharmacy, College of Pharmacy, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
⁶ Department of Biochemistry and Microbiology, North South University, Dhaka 1229, Bangladesh.
⁷ Department of Pharmaceutics, Faculty of Pharmacy, Umm Al-Qura University, Makkah 21955, Saudi Arabia.
⁸ Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka 1000, Bangladesh.
⁹ Department of Pharmacy, Faculty of Pharmacy, University of Dhaka, Dhaka 1000, Bangladesh.
¹⁰ Department of Biotechnology, College of Life Science and Medicine, Zhejiang Sci-Tech University, Hangzhou 310018, China.
¹¹ Department of Genetic Engineering and Biotechnology, Faculty of Biological Science and Technology, Jashore University of Science and Technology, Jashore 7408, Bangladesh.
¹² Laboratory of Computational Biology, Biological Solution Centre (BioSol Centre), Jashore 7408, Bangladesh.
¹³ Department of Biomedical Engineering, State University of New York (SUNY), Binghamton, NY 13902, USA.
¹⁴ College of Korean Medicine, Kyung Hee University, Kyungheedae-ro 26, Dongdaemun-gu, Seoul 05254, Korea.
¹⁵ Department of Biological Sciences, Faculty of Science, King Abdul-Aziz University (KAU), Jeddah 21589, Saudi Arabia.
¹⁶ UKM Medical Molecular Biology Institute, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia.

Abstract

Merkel cell carcinoma (MCC) is a rare form of aggressive skin cancer mainly caused by Merkel cell polyomavirus (MCPyV). Most MCC tumors express MCPyV large T (LT) antigens and play an important role in the growth-promoting activities of oncoproteins. Truncated LT promotes tumorigenicity as well as host cell proliferation by activating the viral replication machinery, and inhibition of this protein in humans drastically lowers cellular growth linked to the corresponding cancer. Our study was designed with the aim of identifying small molecular-like natural antiviral candidates that are able to inhibit the proliferation of malignant tumors, especially those that are aggressive, by blocking the activity of viral LT protein. To identify potential compounds against the target protein, a computational drug design including molecular docking, ADME (absorption, distribution, metabolism, and excretion), toxicity, molecular dynamics (MD) simulation, and molecular mechanics generalized Born surface area (MM-GBSA) approaches were applied in this study. Initially, a total of 2190 phytochemicals isolated from 104 medicinal plants were screened using the molecular docking simulation method, resulting in the identification of the top five compounds having the highest binding energy, ranging between -6.5 and -7.6 kcal/mol. The effectiveness and safety of the selected compounds were evaluated based on ADME and toxicity features. A 250 ns MD simulation confirmed the stability of the selected compounds bind to the active site (AS) of the target protein. Additionally, MM-GBSA analysis was used to determine the high values of binding free energy (ΔG bind) of the compounds binding to the target protein. The five compounds identified by computational approaches, Paulownin (CID: 3084131), Actaealactone (CID: 11537736), Epigallocatechin 3-O-cinnamate (CID: 21629801), Cirsilineol (CID: 162464), and Lycoricidine (CID: 73065), can be used in therapy as lead compounds to combat MCPyV-related cancer. However, further wet laboratory investigations are required to evaluate the activity of the drugs against the virus.

Keywords: ADMET; MD simulation; Merkel cell carcinomas; Merkel cell polyomavirus; drug design; molecular docking.

Grants and funding

FP-54-42/King Abdulaziz University