Distance matrices for nitrogenous bases and amino acids of SARS-CoV-2 via structural metric

J Bioinform Comput Biol. 2021 Aug;19(4):2150011. doi: 10.1142/S0219720021500116. Epub 2021 Apr 30.

Abstract

COVID-19 pandemic has caused a global health crisis. Developing vaccines would need a good knowledge of genetic properties of SARS-CoV-2. The most fundamental approach is to look into the structures of its RNA, in particular, the nucleotides and amino acids. This motivates our research on this topic. We study the occurrence structures of nitrogenous bases and amino acids. To this aim, we devise a structural metric which could measure the structure differences for bases or amino acids. By analyzing various SARS-CoV-2 samples, we calculate the distance matrices for nitrogenous bases and amino acids. Based on the distance matrices, we find the average distance matrices for them, respectively. Then we identify the relations of all the minimal distances between bases and amino acids. The results also show that different substructures would yield much more diversified distances between amino acids. In the end, we also conduct the comparison of our structural metric with other frequently used metrics, in particular, Hausdorff metrics.

Keywords: Hausdorff metrics.; SARS-CoV-2; amino acids; nitrogenous bases; structural metric.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Amino Acids / chemistry*
  • Amino Acids / genetics
  • Computational Biology
  • Coronavirus Nucleocapsid Proteins / chemistry
  • Phosphoproteins / chemistry
  • SARS-CoV-2 / chemistry*
  • Spike Glycoprotein, Coronavirus / chemistry
  • Viral Matrix Proteins / chemistry
  • Viral Proteins / chemistry

Substances

  • Amino Acids
  • Coronavirus Nucleocapsid Proteins
  • Phosphoproteins
  • Spike Glycoprotein, Coronavirus
  • Viral Matrix Proteins
  • Viral Proteins
  • membrane protein, SARS-CoV-2
  • nucleocapsid phosphoprotein, SARS-CoV-2
  • spike protein, SARS-CoV-2