ArticleJMIRx med2021
Selection of the Optimal L-asparaginase II Against Acute Lymphoblastic Leukemia: An In Silico Approach.
Article in JMIRx med, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- L‒asparaginase activity in some endophytic fungi: Glutaminase‒free and low urease co‒activities.PloS one · 2026Article
- L-asparaginase from the mangrove endophyteToxicology research · 2025Article
- Desirable L-asparaginases for treating cancer and current research trends.Frontiers in microbiology · 2024Review
- Engineering and Expression Strategies for Optimization of L-Asparaginase Development and Production.International journal of molecular sciences · 2023Review
- Uncovering differences in the composition and function of phage communities and phage-bacterium interactions in raw soy sauce.Frontiers in microbiology · 2023Article
- Massive annotation of bacterial L-asparaginases reveals their puzzling distribution and frequent gene transfer events.Scientific reports · 2022Article
- Article
- Characterization and Evaluation of the Anti-proliferative Activity and Hypersensitivity of L-Asparaginase fromAvicenna journal of medical biotechnologyArticle
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundL-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.
objectiveQuestions 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.
methodsIn 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.
resultsasnBs 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.
conclusionsWe 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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