Abstract
The present research uses integrative computational analysis to assess Streptomyces koyangensis L-asparaginase as a therapeutic against acute lymphoblastic leukemia (ALL), overcoming immunogenicity and cross-reactivity issues with E. coli and Erwinia carotovora enzymes. We characterized enzyme-oncoprotein interactions using six in silico methods: homology modeling (SWISS-MODEL, AlphaFold2), molecular docking (ClusPro, HADDOCK, AutoDock Vina), 100 ns molecular dynamics (MD) (GROMACS), and pharmacophore modeling (LigandScout). Exceptional stability of the S. koyangensis-BCL-2 complex was revealed: binding energy − 13.8 kcal/mol; RMSD < 2.5 Å; Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) -68.4 ± 5.2 kcal/mol, forming a 2,145 Ų interface with 80 interacting residues. Pharmacophore modeling identified eight features targeting Asp42, Glu78, and Arg156 for rational engineering. This suggests a potential dual mechanism involving asparagine depletion and predicted BCL-2 binding interactions that may enhance leukemic apoptosis, pending experimental validation. Comparative analysis confirmed S. koyangensis demonstrated statistically significant superior binding affinity compared to alternatives (P < 0.01), offering a computational framework for identifying potential anti-cancer biotherapeutic candidates requiring experimental validation.
Data availability
We provide open-source R and Python code to fully reproduce the analyses presented in this study. All scripts and templates are available at: https://github.com/S7674/Streptomyces_koyangensis_L-Asparaginase_repro-pack.git.
Code availability
We provide open-source R and Python code to fully reproduce the analyses presented in this study. All scripts and templates are available at: https://github.com/S7674/Streptomyces_koyangensis_L-Asparaginase_repro-pack.git.
References
Montefiori, L. E. et al. Enhancer hijacking drives oncogenic BCL11B expression in lineage-ambiguous stem cell leukemia. Cancer Discov. 11, 2846–2867 (2021).
Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209–249 (2021).
Ferlay, J. et al. Global cancer observatory: cancer today. Int. Agency Res. Cancer (IARC), Lyon (2020).
Yu, W. J., Sun, H., Zhang, Y. & Sun, J. The characterization of L-asparaginase with low L-glutaminase activity from marine bacteria. Horiz. Enzymol. 16, 2 (2024).
Juluri, K. R. et al. Asparaginase in the treatment of acute lymphoblastic leukemia. Front. Oncol. 12, 915888 (2022).
Lubkowski, J., Dauter, M., Aghaiypour, K., Wlodawer, A. & Dauter, Z. Atomic resolution structure of Erwinia chrysanthemi L-asparaginase. Acta Crystallogr. D Biol. Crystallogr. 59, 84–92 (2003).
Michalska, K. & Jaskolski, M. Structural aspects of L-asparaginases, their friends and relations. Acta Biochim. Pol. 53, 627–640 (2006).
Kiriyama, Y. et al. Biochemical characterization of U937 cells resistant to L-asparaginase: the role of asparagine synthetase. Leukemia 3, 294–297 (1989).
Goody, H. E. & Ellem, K. A. O. Nutritional effects on precursor uptake and compartmentalization of intracellular pools in relation to RNA synthesis. Biochim. Biophys. Acta 383, 30–39 (1975).
Ueno, T. et al. Cell cycle arrest and apoptosis of leukemia cells induced by L-asparaginase. Leukemia 11, 1858–1861 (1997).
Gallagher, M. P., Marshall, R. D. & Wilson, R. Asparaginase as a drug for treatment of acute lymphoblastic leukaemia. Essays Biochem. 24, 1–40 (1989).
Andrulis, I. L., Argonza, R. & Cairney, A. E. Molecular and genetic characterization of human cell lines resistant to L-asparaginase and albizziin. Somat. Cell Mol. Genet. 16, 59–65 (1990).
Chereda, B. & Melo, J. V. Natural course and biology of CML. Ann. Hematol. 94, 107–121 (2015).
Singhal, S. A case of chronic myeloid leukemia with Philadelphia chromosome, BCR-ABL transcript and complex translocation involving four different chromosomes. Int. J. Clin. Diagn. Pathol. 3, 428–431 (2020).
Chen, P. et al. Cell-active, reversible, and irreversible covalent inhibitors that selectively target the catalytic lysine of BCR-ABL kinase. Angew. Chem. Int. Ed. 61, e202203878 (2022).
Adams, J. M. & Cory, S. The BCL-2 arbiters of apoptosis and their growing role as cancer targets. Cell Death Differ. 25, 27–36 (2018).
Salah, H-T. et al. Potential biomarkers for treatment response to the BCL-2 selective inhibitor venetoclax. Cancers 13, 2974 (2021).
Perini, G. F. et al. BCL-2 as therapeutic target for hematological malignancies. J. Hematol. Oncol. 11, 65 (2018).
Eisenberg, D., Marcotte, E. M., Xenarios, I. & Yeates, T. O. Protein functions in the post-genomic era. Nature 405, 823–826 (2000).
Sun, J., Zhu, K., Zheng, W. J. & Xu, H. A comparative study of disease genes and drug targets in the human protein interactome. BMC Bioinformatics 16(Suppl 1), S1 (2015).
Arkin, M. R. & Wells, J. A. Small-molecule inhibitors of protein–protein interactions: Progressing towards the dream. Nat. Rev. Drug Discov. 3, 301–317 (2004).
Avramis, V. I. & Tiwari, P. N. Asparaginase (native ASNase or pegylated ASNase) in the treatment of acute lymphoblastic leukemia. Int. J. Nanomed. 1, 241–254 (2006).
NCBI Resource Coordinators. NCBI Protein Database (Non-redundant, nr). National Center for Biotechnology Information, Bethesda, MD, USA. (2025).
Gasteiger, E. et al. ExPASy: The proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res. 31, 3784–3788 (2003).
Dimitrov, I., Bangov, D. R., Flower, I. & Doytchinova, I. AllerTOP v.2 — A server for in silico prediction of allergens. J. Mol. Model. 202278. (2014).
Geourjon, C. & Deleage, G. SOPMA: Significant improvements in protein secondary structure prediction. Bioinformatics 11, 681–684 (1995).
Kozakov, D. et al. The ClusPro web server for protein–protein docking.. Nat. Protoc. 12, 255–278 (2017).
van Zundert, G. C. P. et al. The HADDOCK2.2 web server: User-friendly integrative modeling of biomolecular complexes.. J. Mol. Biol. 428, 720–725 (2016).
Trott, O. & Olson, A. J. AutoDock Vina: Improving the speed and accuracy of docking. J. Comput. Chem. 31, 455–461 (2010).
Xue, L. C., Rodrigues, J. P., Kastritis, P. L., Bonvin, A. M. & Vangone, A. PRODIGY: Predicting binding affinity of protein–protein complexes.. Bioinformatics 32, 3676–3678 (2016).
Abraham, M. J. et al. GROMACS: high performance molecular simulations through multi-level parallelism. SoftwareX 1–2, 19–25 (2015).
Kumari, R., Kumar, R. & Lynn, A. g_mmpbsa — A GROMACS tool for high-throughput MM-PBSA calculations. J. Chem. Inf. Model. 54, 1951–1962 (2014).
Krissinel, E. & Henrick, K. Inference of macromolecular assemblies from crystalline state. J. Mol. Biol. 372, 774–797 (2007).
Wolber, G. & Langer, T. LigandScout: 3-D pharmacophores and virtual screening filters. J. Chem. Inf. Model. 45, 160–169 (2005).
Manochitra, K. & Parija, S. C. In silico prediction and modeling of Entamoeba histolytica proteins. PeerJ 5, e3160 (2017).
Appaiah, P. & Vasu, P. In silico designing of protein rich in large neutral amino acids using bovine αs1 casein. J. Proteomics Bioinform. 9, 287–297 (2016).
Bagag, A. et al. Characterization of hydrophobic peptides in the presence of detergent. PLoS One. 8, e79033 (2013).
Brumano, L. P. et al. Development of L-asparaginase biobetters: current research status. Front. Bioeng. Biotechnol. 6, 212 (2019).
Wei, H. et al. Structures of p53/BCL-2 complex suggest mechanism for antagonizing BCL-2. Nat. Commun. 14, 4300 (2023).
Avramis, V. I. & Tiwari, P. N. Asparaginase in the treatment of acute lymphoblastic leukemia. Int. J. Nanomed. 1, 241–254 (2006).
Patel, C. N. et al. Pharmacophore-based virtual screening of catechol-o-methyltransferase (COMT) inhibitors to combat Alzheimer’s disease. J. Biomol. Struct. Dyn. 36(15), 3938–3957. https://doi.org/10.1080/07391102.2017.1392897 (2018).
Baccarani, M., Rosti, G. & Soverini, S. Concepts of resistance and persistence in CML. Leukemia 33, 2358–2364 (2019).
Patel, H. C. et al. In silico and in vitro evaluation of newly synthesized pyrazolo-pyridine fused tetrazolo-pyrimidines derivatives as potential anticancer and antimicrobial agents. J. Biomol. Struct. Dynamics. 43 (7), 3467–3490. https://doi.org/10.1080/07391102.2024.2308768 (2025).
Funding
The proposed study did not receive any funding.
Author information
Authors and Affiliations
Contributions
Sunil Tulshiram Hajare, Gayatri Solanki and Chirag Prajapati: Supervision, Conceptualization. Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Rekha Gadhvi: Writing – review & editing, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Sunil Tulshiram Hajare, Mukesh Chandra Sharma and Laxmikant Kamble: Writing – review & editing, Validation,, Project administration, Methodology.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Consent for publication
Not applicable.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Solanki, G., Prajapati, C., Gadhvi, R. et al. Streptomyces koyangensis L-asparaginase: computational prediction of dual-mechanism BCL-2 interaction in acute lymphoblastic leukemia. Sci Rep (2026). https://doi.org/10.1038/s41598-026-42798-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-42798-0
