Selection of the Optimal L-asparaginase II Against Acute Lymphoblastic Leukemia: An In Silico Approach

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.

Selection of the Optimal L-asparaginase II Against Acute Lymphoblastic Leukemia: An In Silico Approach (Preprint)

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.

In-silico development of a method for the selection of optimal enzymes using L-asparaginase II against Acute Lymphoblastic Leukemia as an example

ABSTRACTL-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.

Highly efficient DSB-free base editing for streptomycetes with CRISPR-BEST

Streptomycetes serve as major producers of various pharmacologically and industrially important natural products. Although CRISPR-Cas9 systems have been developed for more robust genetic manipulations, concerns of genome instability caused by the DNA double-strand breaks (DSBs) and the toxicity of Cas9 remain. To overcome these limitations, here we report development of the DSB-free, single-nucleotide–resolution genome editing system CRISPR-BEST (CRISPR-Base Editing SysTem), which comprises a cytidine (CRISPR-cBEST) and an adenosine (CRISPR-aBEST) deaminase-based base editor. Specifically targeted by an sgRNA, CRISPR-cBEST can efficiently convert a C:G base pair to a T:A base pair and CRISPR-aBEST can convert an A:T base pair to a G:C base pair within a window of approximately 7 and 6 nucleotides, respectively. CRISPR-BEST was validated and successfully used in different Streptomyces species. Particularly in nonmodel actinomycete Streptomyces collinus Tü365, CRISPR-cBEST efficiently inactivated the 2 copies of kirN gene that are in the duplicated kirromycin biosynthetic pathways simultaneously by STOP codon introduction. Generating such a knockout mutant repeatedly failed using the conventional DSB-based CRISPR-Cas9. An unbiased, genome-wide off-target evaluation indicates the high fidelity and applicability of CRISPR-BEST. Furthermore, the system supports multiplexed editing with a single plasmid by providing a Csy4-based sgRNA processing machinery. To simplify the protospacer identification process, we also updated the CRISPy-web (https://crispy.secondarymetabolites.org), and now it allows designing sgRNAs specifically for CRISPR-BEST applications.