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Published on: September 18, 2013
Selection of the Optimal L-asparaginase II Against Acute Lymphoblastic Leukemia: An In Silico Approach
Adesh Baral1, Ritesh Gorkhali1, Amit Basnet1
1Department of Biotechnology, Kathmandu University, Dhulikhel, Nepal.
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.
