Mallick, D. | bioRxiv Science

bioRxiv2021

D155Y Substitution of SARS-CoV-2 ORF3a Weakens Binding with Caveolin-1: An in silico Study

The clinical manifestation of the recent pandemic COVID-19, caused by the novel SARS-CoV-2 virus, varies from mild to severe respiratory illness. Although environmental, demographic and co-morbidity factors have an impact on the severity of the disease, contribution of the mutations in each of the viral genes towards the degree of severity needs a deeper understanding for designing a better therapeutic approach against COVID-19. Open Reading Frame-3a (ORF3a) protein has been found to be mutated at several positions. In this work, we have studied the effect of one of the most frequently occurring mutants, D155Y of ORF3a protein, found in Indian COVID-19 patients. Using computational simulations we demonstrated that the substitution at 155th changed the amino acids involved in salt bridge formation, hydrogen-bond occupancy, interactome clusters, and the stability of the protein compared with the other substitutions found in Indian patients. Protein-protein docking using HADDOCK analysis revealed that substitution D155Y weakened the binding affinity of ORF3a with caveolin-1 compared with the other substitutions, suggesting its importance in the overall stability of ORF3a-caveolin-1 complex, which may modulate the virulence property of SARS-CoV-2.

bioinformatics↗

bioRxiv2020

A Numerical Representation and Classification of Codons to Investigate Codon Alternation Patterns during Genetic Mutations on Disease Pathogenesis

Alteration of amino acids is possible due to mutation in codons that could have potential reasons to occur disease. Single nucleotide substitutions (SNS) in genetic codon thus have prime importance for their ability to occur mutations that may be deleterious indeed. Effective mutation analysis can help to predict the fate of the diseased individual which can be validated later by in-vitro experiments. Hence in this present study, we try to investigate the codon alteration patterns and their impact during mutation for the genes known to be responsible for a particular disease. We use a numerical representation of four nucleotides based on the number of hydrogen bonds in their chemical structures and make a classification of 64 codons as well as corresponding 20 amino acids into three different classes (Strong, Weak and Transitional). The entire analysis has been carried out based on these classifications. For our current study, we consider two neurodegenerative diseases, Parkinsons disease, and Glaucoma. Several evidences claim similarities between both the diseases but proper pathogenetic factors are still unknown. The analysis reveals that the strong class of codons is highly mutated followed by the weak and transitional class. We observe that most of the mutations occur in the first or second positions in the codon rather than the third and mutations that occurred at the second place of codons are majorly deleterious. In most cases, the change in the determinative degree of codon due to mutation is directly proportional to the physical density property. Furthermore, we derive a determinative degree of five wild-type amino acid sequences, which can help biologists to understand the evolutionary relationship among them based on amino acid occurrence frequencies in proteins. In this regard we proposed an alignment-free method SSADDA (Sequence Similarity Analysis using Determinative Degree of Amino acid). Thus, our scheme gives a more microscopic and alternative representation of the existing codon table that helps in deciphering interesting codon alteration patterns during mutations in disease pathogenesis.

bioinformatics↗