Computational drug screening against the SARS-CoV-2 Saudi Arabia isolates through a multiple-sequence alignment approach
COVID-19 is a rapidly emerging infectious disease caused by the SARS-CoV-2 virus currently spreading throughout the world. To date, there are no specific drugs formulated for it, and researchers around the globe are racing against the clock to investigate potential drug candidates. The repurposing o...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Published: |
Elsevier
2021
|
| _version_ | 1825937721539952640 |
|---|---|
| author | Mok, Pooi Ling Koh, Avin Ee Hwan Farhana, Aisha Alsrhani, Abdullah Alam, Mohammad Khursheed Subbiah, Suresh Kumar |
| author_facet | Mok, Pooi Ling Koh, Avin Ee Hwan Farhana, Aisha Alsrhani, Abdullah Alam, Mohammad Khursheed Subbiah, Suresh Kumar |
| author_sort | Mok, Pooi Ling |
| collection | UPM |
| description | COVID-19 is a rapidly emerging infectious disease caused by the SARS-CoV-2 virus currently spreading throughout the world. To date, there are no specific drugs formulated for it, and researchers around the globe are racing against the clock to investigate potential drug candidates. The repurposing of existing drugs in the market represents an effective and economical strategy commonly utilized in such investigations. In this study, we used a multiple-sequence alignment approach for preliminary screening of commercially-available drugs on SARS-CoV sequences from the Kingdom of Saudi Arabia (KSA) isolates. The viral genomic sequences from KSA isolates were obtained from GISAID, an open access repository housing a wide variety of epidemic and pandemic virus data. A phylogenetic analysis of the present 164 sequences from the KSA provinces was carried out using the MEGA X software, which displayed high similarity (around 98%). The sequence was then analyzed using the VIGOR4 genome annotator to construct its genomic structure. Screening of existing drugs was carried out by mining data based on viral gene expressions from the ZINC database. A total of 73 hits were generated. The viral target orthologs were mapped to the SARS-CoV-2 KSA isolate sequence by multiple sequence alignment using CLUSTAL OMEGA, and a list of 29 orthologs with purchasable drug information was generated. The results showed that the SARS CoV replicase polyprotein 1a had the highest sequence similarity at 79.91%. Through ZINC data mining, tanshinones were found to have high binding affinities to this target. These compounds could be ideal candidates for SARS-CoV-2. Other matches ranged between 27 and 52%. The results of this study would serve as a significant endeavor towards drug discovery that would increase our chances of finding an effective treatment or prevention against COVID19. |
| first_indexed | 2024-03-06T11:03:49Z |
| format | Article |
| id | upm.eprints-96492 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T11:03:49Z |
| publishDate | 2021 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | upm.eprints-964922023-01-11T08:49:19Z http://psasir.upm.edu.my/id/eprint/96492/ Computational drug screening against the SARS-CoV-2 Saudi Arabia isolates through a multiple-sequence alignment approach Mok, Pooi Ling Koh, Avin Ee Hwan Farhana, Aisha Alsrhani, Abdullah Alam, Mohammad Khursheed Subbiah, Suresh Kumar COVID-19 is a rapidly emerging infectious disease caused by the SARS-CoV-2 virus currently spreading throughout the world. To date, there are no specific drugs formulated for it, and researchers around the globe are racing against the clock to investigate potential drug candidates. The repurposing of existing drugs in the market represents an effective and economical strategy commonly utilized in such investigations. In this study, we used a multiple-sequence alignment approach for preliminary screening of commercially-available drugs on SARS-CoV sequences from the Kingdom of Saudi Arabia (KSA) isolates. The viral genomic sequences from KSA isolates were obtained from GISAID, an open access repository housing a wide variety of epidemic and pandemic virus data. A phylogenetic analysis of the present 164 sequences from the KSA provinces was carried out using the MEGA X software, which displayed high similarity (around 98%). The sequence was then analyzed using the VIGOR4 genome annotator to construct its genomic structure. Screening of existing drugs was carried out by mining data based on viral gene expressions from the ZINC database. A total of 73 hits were generated. The viral target orthologs were mapped to the SARS-CoV-2 KSA isolate sequence by multiple sequence alignment using CLUSTAL OMEGA, and a list of 29 orthologs with purchasable drug information was generated. The results showed that the SARS CoV replicase polyprotein 1a had the highest sequence similarity at 79.91%. Through ZINC data mining, tanshinones were found to have high binding affinities to this target. These compounds could be ideal candidates for SARS-CoV-2. Other matches ranged between 27 and 52%. The results of this study would serve as a significant endeavor towards drug discovery that would increase our chances of finding an effective treatment or prevention against COVID19. Elsevier 2021 Article PeerReviewed Mok, Pooi Ling and Koh, Avin Ee Hwan and Farhana, Aisha and Alsrhani, Abdullah and Alam, Mohammad Khursheed and Subbiah, Suresh Kumar (2021) Computational drug screening against the SARS-CoV-2 Saudi Arabia isolates through a multiple-sequence alignment approach. Saudi Journal of Biological Sciences, 28 (4). 2502 - 2509. ISSN 1319-562X https://www.sciencedirect.com/science/article/pii/S1319562X21000528 10.1016/j.sjbs.2021.01.051 |
| spellingShingle | Mok, Pooi Ling Koh, Avin Ee Hwan Farhana, Aisha Alsrhani, Abdullah Alam, Mohammad Khursheed Subbiah, Suresh Kumar Computational drug screening against the SARS-CoV-2 Saudi Arabia isolates through a multiple-sequence alignment approach |
| title | Computational drug screening against the SARS-CoV-2 Saudi Arabia isolates through a multiple-sequence alignment approach |
| title_full | Computational drug screening against the SARS-CoV-2 Saudi Arabia isolates through a multiple-sequence alignment approach |
| title_fullStr | Computational drug screening against the SARS-CoV-2 Saudi Arabia isolates through a multiple-sequence alignment approach |
| title_full_unstemmed | Computational drug screening against the SARS-CoV-2 Saudi Arabia isolates through a multiple-sequence alignment approach |
| title_short | Computational drug screening against the SARS-CoV-2 Saudi Arabia isolates through a multiple-sequence alignment approach |
| title_sort | computational drug screening against the sars cov 2 saudi arabia isolates through a multiple sequence alignment approach |
| work_keys_str_mv | AT mokpooiling computationaldrugscreeningagainstthesarscov2saudiarabiaisolatesthroughamultiplesequencealignmentapproach AT kohavineehwan computationaldrugscreeningagainstthesarscov2saudiarabiaisolatesthroughamultiplesequencealignmentapproach AT farhanaaisha computationaldrugscreeningagainstthesarscov2saudiarabiaisolatesthroughamultiplesequencealignmentapproach AT alsrhaniabdullah computationaldrugscreeningagainstthesarscov2saudiarabiaisolatesthroughamultiplesequencealignmentapproach AT alammohammadkhursheed computationaldrugscreeningagainstthesarscov2saudiarabiaisolatesthroughamultiplesequencealignmentapproach AT subbiahsureshkumar computationaldrugscreeningagainstthesarscov2saudiarabiaisolatesthroughamultiplesequencealignmentapproach |