SARS-CoV-2 virus, the causative agent of COVID-19 pandemic, has a genomic organization consisting of 16 nonstructural proteins (nsps), 4 structural proteins, and 9 accessory proteins. Relative of SARS-CoV-2, SARS-CoV, has genomic organization, which is very similar. In this article, the function and structure of the proteins of SARS-CoV-2 and SARS-CoV are described in great detail. The nsps are expressed as a single or two polyproteins, which are then cleaved into individual proteins using two proteases of the virus, a chymotrypsin-like protease and a papain-like protease. The released proteins serve as centers of virus replication and transcription. Some of these nsps modulate the host’s translation and immune systems, while others help the virus evade the host immune system. Some of the nsps help form replication-transcription complex at double-membrane vesicles. Others, including one RNA-dependent RNA polymerase and one exonuclease, help in the polymerization of newly synthesized RNA of the virus and help minimize the mutation rate by proofreading. After synthesis of the viral RNA, it gets capped. The capping consists of adding GMP and a methylation mark, called cap 0 and additionally adding a methyl group to the terminal ribose called cap1. Capping is accomplished with the help of a helicase, which also helps remove a phosphate, two methyltransferases, and a scaffolding factor. Among the structural proteins, S protein forms the receptor of the virus, which latches on the angiotensin-converting enzyme 2 receptor of the host and N protein binds and protects the genomic RNA of the virus. The accessory proteins found in these viruses are small proteins with immune modulatory roles. Besides functions of these proteins, solved X-ray and cryogenic electron microscopy structures related to the function of the proteins along with comparisons to other coronavirus homologs have been described in the article. Finally, the rate of mutation of SARS-CoV-2 residues of the proteome during the 2020 pandemic has been described. Some proteins are mutated more often than other proteins, but the significance of these mutation rates is not fully understood.

Background L-asparaginase II (asnB), a periplasmic protein commercially extracted from E coli and Erwinia, is often used to treat acute lymphoblastic leukemia. L-asparaginase is an enzyme that converts L-asparagine to aspartic acid and ammonia. Cancer cells are dependent on asparagine from other sources for growth, and when these cells are deprived of asparagine by the action of the enzyme, the cancer cells selectively die. Objective Questions remain as to whether asnB from E coli and Erwinia is the best asparaginase as they have many side effects. asnBs with the lowest Michaelis constant (Km; most potent) and lowest immunogenicity are considered the most optimal enzymes. In this paper, we have attempted the development of a method to screen for optimal enzymes that are better than commercially available enzymes. Methods In this paper, the asnB sequence of E coli was used to search for homologous proteins in different bacterial and archaeal phyla, and a maximum likelihood phylogenetic tree was constructed. The sequences that are most distant from E coli and Erwinia were considered the best candidates in terms of immunogenicity and were chosen for further processing. The structures of these proteins were built by homology modeling, and asparagine was docked with these proteins to calculate the binding energy. Results asnBs from Streptomyces griseus, Streptomyces venezuelae, and Streptomyces collinus were found to have the highest binding energy (–5.3 kcal/mol, –5.2 kcal/mol, and –5.3 kcal/mol, respectively; higher than the E coli and Erwinia asnBs) and were predicted to have the lowest Kms, as we found that there is an inverse relationship between binding energy and Km. Besides predicting the most optimal asparaginase, this technique can also be used to predict the most optimal enzymes where the substrate is known and the structure of one of the homologs is solved. Conclusions We have devised an in silico method to predict the enzyme kinetics from a sequence of an enzyme along with being able to screen for optimal alternative asnBs against acute lymphoblastic leukemia.

BACKGROUND L-asparaginase II (asnB), a periplasmic protein commercially extracted from <i>E coli</i> and <i>Erwinia</i>, is often used to treat acute lymphoblastic leukemia. L-asparaginase is an enzyme that converts L-asparagine to aspartic acid and ammonia. Cancer cells are dependent on asparagine from other sources for growth, and when these cells are deprived of asparagine by the action of the enzyme, the cancer cells selectively die. OBJECTIVE Questions remain as to whether asnB from <i>E coli</i> and <i>Erwinia</i> is the best asparaginase as they have many side effects. asnBs with the lowest Michaelis constant (Km; most potent) and lowest immunogenicity are considered the most optimal enzymes. In this paper, we have attempted the development of a method to screen for optimal enzymes that are better than commercially available enzymes. METHODS In this paper, the asnB sequence of <i>E coli</i> was used to search for homologous proteins in different bacterial and archaeal phyla, and a maximum likelihood phylogenetic tree was constructed. The sequences that are most distant from <i>E coli</i> and <i>Erwinia</i> were considered the best candidates in terms of immunogenicity and were chosen for further processing. The structures of these proteins were built by homology modeling, and asparagine was docked with these proteins to calculate the binding energy. RESULTS asnBs from <i>Streptomyces griseus</i>, <i>Streptomyces venezuelae</i>, and <i>Streptomyces collinus</i> were found to have the highest binding energy (–5.3 kcal/mol, –5.2 kcal/mol, and –5.3 kcal/mol, respectively; higher than the <i>E coli</i> and <i>Erwinia</i> asnBs) and were predicted to have the lowest Kms, as we found that there is an inverse relationship between binding energy and Km. Besides predicting the most optimal asparaginase, this technique can also be used to predict the most optimal enzymes where the substrate is known and the structure of one of the homologs is solved. CONCLUSIONS We have devised an in silico method to predict the enzyme kinetics from a sequence of an enzyme along with being able to screen for optimal alternative asnBs against acute lymphoblastic leukemia.

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L-Asparaginase II (asnB), a periplasmic protein, commercially extracted from E. coli and Erwinia, is often used to treat Acute Lymphoblastic Leukemia. L-Asparaginase is an enzyme that converts L-asparagine to aspartic acid and ammonia. Cancer cells are dependent on asparagine from other sources for growth and when these cells are deprived of asparagine by the action of the enzyme the cancer cells selectively die. Questions remain as to whether asnB from E. coli and Erwinia is the best asparaginase as they have many side-effects. asnB with the lowest Michaelis constant (Km) (most potent), and with the lowest immunogenicity is considered the most optimal enzyme. In this paper asnB sequence of E. coli was used to search for homologous proteins in different bacterial and archaeal phyla and a maximum likelihood phylogenetic tree was constructed. The sequences that are most distant from E. coli and Erwinia were considered best candidates in terms of immunogenicity and were chosen for further processing. The structures of these proteins were built by homology modeling and asparagine was docked with these proteins to calculate the binding energy. asnBs from Streptomyces griseus, Streptomyces venezuelae and Streptomyces collinus were found to have the highest binding energy i.e. -5.3 kcal/mol, -5.2 kcal/mol, and -5.3 kcal/mol respectively (Higher than the E.coli and Erwinia asnBs) and were predicted to have the lowest Kms as we found that there is an inverse relationship between binding energy and Km. Besides predicting the most optimal asparaginase, this technique can also be used to predict the most optimal enzymes where the substrate is known and the structure of one of the homologs is solved.