STRUCTURAL DETERMINATION OF L-ASPARAGINASE II OF STREPTOMYCES ALBIDOFLAVUS AND INTERACTION ANALYSIS WITH L-ASPARAGINE AND CEFOTAXIME

Authors

  • ACHYUTUNI VENKATA NAGA TEJASWINI School of Biotechnology, Institute of Science and Technology, Jawaharlal Nehru Technological University, Kakinada 533003, Andhra Pradesh, India
  • MALOTHU RAMESH School of Biotechnology, Institute of Science and Technology, Jawaharlal Nehru Technological University, Kakinada 533003, Andhra Pradesh, India

DOI:

https://doi.org/10.22159/ijap.2021v13i4.41303

Keywords:

L-Asparaginase, Streptomyces, Homology modelling, GalaxyTBM, I-TASSER, L-Asparagine, Cefotaxime

Abstract

Objective: L-Asparaginase enzyme possesses a crucial role in the treatment of various hematologic malignancies. The current study focuses on homology modeling and interaction analysis of L-Asparaginase proteins belonging to Streptomyces albidoflavus (S. albidoflavus) with the essential ligand L-Asparagine and subsequent analysis with essential β-lactam antibiotic Cefotaxime.

Methods: The process of understanding Asparaginase interactions primarily involved structure determination of WP_096097608, WP_095730301, which is achieved by GalaxyTBM, I-TASSER and SWISS-MODEL. Further, the S. albidoflavus Asparaginase proteins are subjected to GalaxySite and Autodock Vina of PyRx analysis.

Results: The GalaxyTBM predicted structures of both the proteins are found promising on various validation studies. The two Asparaginase proteins exhibited high binding affinities of-6.8 and-6.5 kcal/mol with Cefotaxime and-5.1 and-4.9 kcal/mol towards Asparagine. The protein WP_096097608 residues forming hydrogen bonds with L-Asparagine are also analysed to involve in interaction with Cefotaxime on individual docking analysis.

Conclusion: The current findings details the two S. albidoflavus Asparaginase proteins affinity towards L-Asparagine, hence can be assessed further for immunogenicity studies. In addition to the above findings, an attempt is made to find the L-Asparaginase binding possibilities with non-metals that identified an essential β-lactam antibiotic Cefotaxime to be an effective inhibitor. This study helps in understanding the interactions of L-Asparaginase with Cefotaxime, as intake of antibiotics between the phases of chemotherapy is observed to treat various infections and also as an antibiotic to microbes that utilize Asparaginase as a vital enzyme.

Downloads

Download data is not yet available.

References

Ehsanipour EA, Sheng X, Behan JW, Wang X, Butturini A, Avramis VI, et al. Adipocytes cause leukemia cell resistance to L-asparaginase via release of glutamine. Cancer Res 2013;73:2998–6.

Barry E, DeAngelo DJ, Neuberg D, Stevenson K, Loh ML, Asselin BL, et al. Favorable outcome for adolescents with acute lymphoblastic leukemia treated on dana-farber cancer institute acute lymphoblastic leukemia consortium protocols. J Clin Oncol 2007;25:813–9.

Rowe JM, Buck G, Burnett AK, Chopra R, Wiernik PH, Richards SM, et al. Induction therapy for adults with acute lymphoblastic leukemia: results of more than 1500 patients from the international ALL trial: MRC UKALL XII/ECOG E2993. Blood 2005;106:3760–7.

Moghrabi A, Levy DE, Asselin B, Barr R, Clavell L, Hurwitz C, et al. Results of the dana-farber cancer institute all consortium protocol 95-01 for children with acute lymphoblastic leukemia. Blood 2007;109:896–904.

Katz AJ, Chia VM, Schoonen WM, Kelsh MA. Acute lymphoblastic leukemia: an assessment of international incidence, survival, and disease burden. Cancer Causes Control 2015;26:1627–42.

Mitchell L, Hoogendoorn H, Giles AR, Vegh P, Andrew M. Increased endogenous thrombin generation in children with acute lymphoblastic leukemia: risk of thrombotic complications in L’Asparaginase-induced antithrombin III deficiency. Blood 1994;83:386–91.

Gao Y, Shang Q, Li W, Guo W, Stojadinovic A, Mannion C, et al. Antibiotics for cancer treatment: a double-edged sword. J Cancer 2020;11:5135–49.

Reuter G. The lactobacillus and bifidobacterium microflora of the human intestine: composition and succession. Curr Issues Intest Microbiol 2001;2:43–53.

Balakrishnan NA, Raj JS, Kandakatla NA. In silico studies on new indazole derivatives as gsk-3 β inhibitors. Int J Pharm Pharm Sci 2015;7:295-9.

Feig M. Computational protein structure refinement: almost there, yet still so far to go. Wiley Interdiscip Rev Comput Mol Sci 2017;7:1307.

Shuid AN, Kempster R, Mcguffin LJ. ReFOLD: a server for the refinement of 3D protein models guided by accurate quality estimates. Nucleic Acids Res 2017;45:422–8.

Kinoshita K, Nakamura H. Protein informatics towards function identification. Curr Opin Struct Biol 2003;13:396–400.

Agarwala R, Barrett T, Beck J, Benson DA, Bollin C, Bolton E, et al. Database resources of the national center for biotechnology information. Nucleic Acids Res 2018;46:8–13.

Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol 1990;215:403-10.

UniProt Consortium. The universal protein resource (UniProt). Nucleic Acids Res 2007;36:D190-5.

Thompson JD, Higgins DG, Gibson TJ. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 1994;22:4673-80.

Saitou N, Nei M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Bio Evol 1987;4:406-25.

Felsenstein J. Confidence limits on phylogenies: an approach using the bootstrap. Evolution 1985;39:783-91.

Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol 2018;35:1547-9.

Gasteiger E, Gattiker A, Hoogland C, Ivanyi I, Appel RD, Bairoch A. ExPASy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res 2003;31:3784-8.

Geourjon C, Deleage G. SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Bioinformatics 1995;11:681–4.

Yang J, Zhang Y. I-TASSER server: new development for protein structure and function predictions. Nucleic Acids Res 2015;43:174–81.

Lu S, Wang J, Chitsaz F, Derbyshire MK, Geer RC, Gonzales NR, et al. CDD/SPARCLE: the conserved domain database in 2020. Nucleic Acids Res 2020;48:265–8.

Mistry J, Chuguransky S, Williams L, Qureshi M, Salazar GA, Sonnhammer ELL, et al. Pfam: the protein families database in 2021. Nucleic Acids Res 2021;49:412–9.

Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 2018;46:296–303.

Ko J, Park H, Seok C. GalaxyTBM: template-based modeling by building a reliable core and refining unreliable local regions. BMC Bioinformatics 2012;13:1-8.

Heo L, Park H, Seok C. GalaxyRefine: protein structure refinement driven by side-chain repacking. Nucleic Acids Res 2013;41:384-8.

Laskowski RA, MacArthur MW, Moss DS, Thornton JM. PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 1993;26:283–91.

Wiederstein M, Sippl MJ. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 2007;35(Suppl 2):407-10.

Colovos C, Yeates TO. Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 1993;2:1511–9.

Heo L, Shin WH, Lee MS, Seok C. GalaxySite: ligand-binding-site prediction by using molecular docking. Nucleic Acids Res 2014;42:210-4.

Trott O, Olson AJ. AutoDock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 2010;31:455-61.

Dallakyan S, Olson AJ. Small-molecule library screening by docking with PyRx. Methods Mol Biol 2015;1263:243–50.

Soding J. Protein homology detection by HMM-HMM comparison. Bioinformatics 2005;21:951–60.

Shin WH, Heo L, Lee J, Ko J, Seok C, Lee J. LigDockCSA: protein-ligand docking using conformational space annealing. J Comput Chem 2011;32:3226–32.

BIOVIA, Dassault Systemes. Discovery studio modeling environment v2021. San Diego: Dassault Systemes; 2016.

Manochitra K, Parija SC. In silico prediction and modeling of the entamoeba histolytica proteins: serine-rich entamoeba histolytica protein and 29 kDa cysteine-rich protease. Peer J 2017;5:3160.

Appaiah P, Vasu P. In silico designing of protein rich in large neutral amino acids using bovine αs1 casein for treatment of phenylketonuria. J Proteomics Bioinform 2016;9:287-97.

Bagag A, Jault JM, Sidahmed Adrar N, Refregiers M, Giuliani A, Le Naour F. Characterization of hydrophobic peptides in the presence of detergent by photoionization mass spectrometry. PloS One 2013;8:79033.

Jones DT. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 1999;292:195–202.

Ramachandran S, Kota P, Ding F, Dokholyan NV. Automated minimization of steric clashes in protein structures. Proteins Struct Funct Bioinforma 2011;79:261–70.

Wallace AC, Laskowski RA, Thornton JM. Ligplot: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng Des Sel 1995;8:127–34.

Jenkinson SG. The use of cefotaxime in the treatment of gram-positive pneumonias. Infection 1985;13:14-7.

Rolston K, Bolivar R, Fainstein V, Jones P, Elting linda, Bodey GP. Cefotaxime: single agent therapy for infections in cancer patients with adequate granulocyte counts. J Antimicrob Chemother 1985;15:91–6.

Lefevre G, Ianotto JC, Tempescul A, Lemoine P, Guillerm G, Berthou C. Infection by H1N1 flu virus revealing T-cell acute lymphoid leukaemia: about two cases. Ann Hematol 2011;90:1111–2.

Maiche AG, Teerenhovi L. Empiric treatment of serious infections in patients with cancer: randomised comparison of two combinations. Infection 1991;19:326-9.

Leroux S, Roue JM, Gouyon JB, Biran V, Zheng H, Zhao W, et al. A population and developmental pharmacokinetic analysis to evaluate and optimize cefotaxime dosing regimen in neonates and young infants. Antimicrob Agents Chemother 2016;60:6626-34.

Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res 2009;37 Suppl 2:623.

O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open babel: an open chemical toolbox. J Cheminform 2011;3:1-4.

Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, et al. UCSF chimera-a visualization system for exploratory research and analysis. J Comput Chem 2004;25:1605–12.

Vimal A, Pal D, Tripathi T, Kumar A. Eucalyptol, sabinene and cinnamaldehyde: potent inhibitors of salmonella target protein l-asparaginase. 3 Biotech 2017;7:1-4.

Galande S, Khaursade P, Reddy Shetty P, PB Kavi K, Shetty Prakasham R, Kishor PBK. In silico development of efficient L-asparaginase enzyme for acute lymphoblastic leukaemia therapy. Int J Pharm Sci Res 2018;9:4177–86.

Gonzalez Torres I, Perez Rueda E, Evangelista Martinez Z, Zarate Romero A, Moreno Enriquez A, Huerta Saquero A. Identification of L-asparaginases from Streptomyces strains with competitive activity and immunogenic profiles: a bioinformatic approach. Peer J 2020;8:10276.

Published

07-07-2021

How to Cite

TEJASWINI, A. V. N., & RAMESH, M. (2021). STRUCTURAL DETERMINATION OF L-ASPARAGINASE II OF STREPTOMYCES ALBIDOFLAVUS AND INTERACTION ANALYSIS WITH L-ASPARAGINE AND CEFOTAXIME. International Journal of Applied Pharmaceutics, 13(4), 160–167. https://doi.org/10.22159/ijap.2021v13i4.41303

Issue

Section

Original Article(s)