Impact of clade specific mutations on structural fidelity of SARS-CoV-2 proteins
Basu, S.; Mukhopadhyay, S.; Das, R.; Mukhopadhyay, S.; Singh, P. K.; Ganguli, S.
Show abstract
The SARS-CoV-2 is a positive stranded RNA virus with a genome size of ~29.9 kilobase pairs which spans 29 open reading frames. Studies have revealed that the genome encodes about 16 non-structural proteins (nsp), four structural proteins, and six or seven accessory proteins. Based on prevalent knowledge on SARS-CoV and other coronaviruses, functions have been assigned for majority of the proteins. While, researchers across the globe are engrossed in identifying a potential pharmacological intervention to control the viral outbreak, none of the work has come up with new antiviral drugs or vaccines yet. One possible approach that has shown some positive results is by treating infected patients with the plasma collected from convalescent COVID-19 patients. Several vaccines around the world have entered their final trial phase in humans and we expect that these will in time be available for application to worldwide population to combat the disease. In this work we analyse the effect of prevalent mutations in the major pathogenesis related proteins of SARS-COV2 and attempt to pinpoint the effects of those mutations on the structural stability of the proteins. Our observations and analysis direct us to identify that all the major mutations have a negative impact in context of stability of the viral proteins under study and the mutant proteins suffer both structural and functional alterations as a result of the mutations. Our binary scoring scheme identifies L84S mutation in ORF8 as the most disruptive of the mutations under study. We believe that, the virus is under the influence of an evolutionary phenomenon similar to Mullers ratchet where the continuous accumulation of these mutations is making the virus less virulent which may also explain the reduction in fatality rates worldwide.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Insights into the microevolution of SARS-ACE2 Interactions: In-silico analysis of glycosylation and SNP pattern 97%
- Contriving a chimeric polyvalent vaccine to prevent infections caused by Herpes Simplex Virus (Type-1 and Type-2): an exploratory immunoinformatic approach 97%
- Molecular Basis for Reduced Cleavage Activity and Drug Resistance in D30N HIV-1 protease 96%
Similar papers in this journal
- An in-silico study of the mutation-associated effects on the spike protein of SARS-CoV-2, Omicron variant 97%
- SARS-CoV-2: Proof of recombination between strains and emergence of possibly more virulent ones 97%
- Comprehensive Genome Based Analysis of Vibrio parahaemolyticus for Identifying Novel Drug and Vaccine Molecules: Subtractive Proteomics and Vaccinomics Approach 96%
Similar papers in this journal
- Insights into the mutation T1117I in the spike and the lineage B.1.1.389 of SARS-CoV-2 circulating in Costa Rica 96%
- Whole Genome Comparison of Pakistani Corona Virus with Chinese and US Strains along with its Predictive Severity of COVID-19 96%
- Analysis of single nucleotide polymorphisms between 2019-nCoV genomes and its impact on codon usage 96%
Similar papers in this journal
- Characterizations of SARS-CoV-2 mutational profile, spike protein stability and viral transmission 97%
- Genome based Evolutionary study of SARS-CoV-2 towards the Prediction of Epitope Based Chimeric Vaccine 97%
- Understanding the B and T cells epitopes of spike protein of severe respiratory syndrome coronavirus-2: A computational way to predict the immunogens 96%
Similar papers in this journal
- Identification of novel mutations in RNA-dependent RNA polymerases of SARS-CoV-2 and their implications on its protein structure 97%
- An Issue of Concern: Unique Truncated ORF8 Protein Variants of SARS-CoV-2 97%
- Prediction of antiviral drugs against African Swine Fever Viruses based on protein-protein interaction analysis 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.