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Comparative mutational analysis of SARS-CoV-2 isolates from Pakistan and structural-functional implications using computational modelling and simulation approaches.
Shah, Abdullah; Rehmat, Saira; Aslam, Iqra; Suleman, Muhmmad; Batool, Farah; Aziz, Abdul; Rashid, Farooq; Nawaz, Muhmmad Asif; Ali, Syed Shujait; Junaid, Muhammad; Khan, Abbas; Wei, Dong-Qing.
  • Shah A; Department of Biotechnology, Shaheed Benazir Bhutto University Sheringal, Dir (U), Pakistan.
  • Rehmat S; Sharif Medical and Dental College, Lahore, Pakistan. Electronic address: Tamimrai18@gmail.com.
  • Aslam I; Nawaz Shareef Medical College, Gujrat, Pakistan. Electronic address: Habibrai36@gmail.com.
  • Suleman M; Centre for Biotechnology and Microbiology, University of Swat, Khyber Pakhtunkhwa, Pakistan.
  • Batool F; Institute of Pharmacy and Allied Health Sciences, Lahore College for Women University, Jail Road, Lahore, Pakistan. Electronic address: fblcwu@gmail.com.
  • Aziz A; Molecular Biology Research Center, School of Life Sciences, Central South University, Changsha, China.
  • Rashid F; Dermatology Hospital, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, China.
  • Midrarullah; Department of Biotechnology, Shaheed Benazir Bhutto University Sheringal, Dir (U), Pakistan.
  • Nawaz MA; Department of Biotechnology, Shaheed Benazir Bhutto University Sheringal, Dir (U), Pakistan.
  • Ali SS; Centre for Biotechnology and Microbiology, University of Swat, Khyber Pakhtunkhwa, Pakistan.
  • Junaid M; Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China.
  • Khan A; Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China. Electronic address: abbaskhan@sjtu.edu.cn.
  • Wei DQ; Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China; State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint Laborato
Comput Biol Med ; 141: 105170, 2022 02.
Article in English | MEDLINE | ID: covidwho-1588030
ABSTRACT
SARS-CoV-2, an RNA virus, has been prone to high mutations since its first emergence in Wuhan, China, and throughout its spread. Its genome has been sequenced continuously by many countries, including Pakistan, but the results vary. Understanding its genomic patterns and connecting them with phenotypic features will help in devising therapeutic strategies. Thus, in this study, we explored the mutation landscape of 250 Pakistani isolates of SARS-CoV-2 genomes to check the genome diversity and examine the impact of these mutations on protein stability and viral pathogenesis in comparison with a reference sequence (Wuhan NC 045512.2). Our results revealed that structural proteins mainly exhibit more mutations than others in the Pakistani isolates; in particular, the nucleocapsid protein is highly mutated. In comparison, the spike protein is the most mutated protein globally. Furthermore, nsp12 was found to be the most mutated NSP in the Pakistani isolates and worldwide. Regarding accessory proteins, ORF3A is the most mutated in the Pakistani isolates, whereas ORF8 is highly mutated in world isolates. These mutations decrease the structural stability of their proteins and alter different biological pathways. Molecular docking, the dissociation constant (KD), and MM/GBSA analysis showed that mutations in the S protein alter its binding with ACE2. The spike protein mutations D614G-S943T-V622F (-75.17 kcal/mol), D614G-Q677H (-75.78 kcal/mol), and N74K-D614G (-73.84 kcal/mol) exhibit stronger binding energy than the wild type (-66.34 kcal/mol), thus increasing infectivity. Furthermore, the simulation results strongly corroborated the predicted protein servers. Our analysis findings also showed that E, M, ORF6, ORF7A, ORF7B, and ORF10 are the most stable coding genes; they may be suitable targets for vaccine and drug development.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Prognostic study Topics: Vaccines Limits: Humans Country/Region as subject: Asia Language: English Journal: Comput Biol Med Year: 2022 Document Type: Article Affiliation country: J.compbiomed.2021.105170

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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Prognostic study Topics: Vaccines Limits: Humans Country/Region as subject: Asia Language: English Journal: Comput Biol Med Year: 2022 Document Type: Article Affiliation country: J.compbiomed.2021.105170