Int J App Pharm, Vol 15, Issue 1, 2023, 98-103Original Article

MODELING, BINDING SITE, AND IMMUNOGENICITY ANALYSIS OF GENES ENCODING L-ASPARAGINASE FROM ARTHROSPIRA PLATENSIS NIES 39

ASEP A. PRIHANTO1,2*, HAPPY NURSYAM1,2, RAHMI NURDIANI1,2, HIDAYATUN MUYASYAROH2, ROYANI L. HAYATI3, ANIS MIFTACURROCHMAH2

1Department of Fishery Product Technology, Faculty of Fisheries and Marine Science, Brawijaya University, Jl. Veteran Malang, East Java 65145, Indonesia, 2Bioseafood Research Unit, Faculty of Fisheries and Marine Science, Brawijaya University, Jl. Veteran Malang, East Java 65145, Indonesia, 3Post Graduate Program, Brawijaya University, Jl. MT Haryono Malang, East Java 65145, Indonesia
Email: asep_awa@ub.ac.id

Received: 23 Aug 2022, Revised and Accepted: 30 Nov 2022

ABSTRACT

Objective: This work aimed to study the modeling, binding site, and immunogenicity analysis of genes encoding L-asparaginase from Arthrospira platensis NIES 39.

Methods: Physicochemical characteristic of the gene was analyzed using ProtParam. I-TASSER, PROCHECK, ProSA, and ProQ were used to build the L-asparaginase model. The enzyme's binding site was achieved based on the SiteMap and COACH analysis. Immunogenicity analysis was based on MHC II binding epitopes on the immune epitope database with further epitope prediction, such as NN-align, SMM aligns, Combinatorial library, and Net MHCIIpan.

Results: The result showed that the protein had an aliphatic index of 94.46. It was dominated by strand, helix, and coil groups. The best template for building the model was the malonate-bound human L-asparaginase protein. The amino acid at 173,191,193, 201, 204, 205, 223, and 225 positions served as binding sites. The best substrate for A. platensis NIES 39 asparaginase was L-asparagine. There is no substantial evidence that the protein is highly allergenic.

Conclusion: In conclusion, this is the first report on the character of ASNase from microalgae A. platensis where the enzyme has the potential to be applied for health applications because of its low allergenicity.

Keywords: Arthrospira platensis, Immune response, L-asparaginase, Modeling

© 2023 The Authors. Published by Innovare Academic Sciences Pvt Ltd. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/)
DOI: https://dx.doi.org/10.22159/ijap.2023v15i1.46177. Journal homepage: https://innovareacademics.in/journals/index.php/ijap

INTRODUCTION

L-asparaginase (L-asparagine amidohydrolase, E. C. 3.5.1.1) is an essential enzyme in health and food safety issues. L-asparaginase is widely known as an anticancer agent. Acute lymphoblastic leukemia is the most reported type of cancer that is successfully treated with L-asparaginase. Furthermore, the enzyme was also developed for a leukemia biosensor [1]. L-asparaginase has also been used to reduce acrylamide in several foods [2-4].

For health and food applications, the commercially available L-asparaginase is limited. Furthermore, the application of L-asparaginase has several drawbacks. The character of L-asparaginase, without a doubt, will affect the successful application of the enzyme. The main handicaps for applying L-ASNase are low affinity toward L-ASN, reactivity to L-glutamine, substrate (L-glutamine), low half-life in the blood system, and allergenic reaction [5]. For this reason, many scientists are in a race to explore any possible source of this enzyme. Structural characterization of L-ASNase from terrestrial sources was extensively investigated. Unfortunately, the limited source was further explored. Therefore, aquatic microorganisms are a new potential source that needs to be explored.

Scientists have attempted to overcome these problems by using different approaches, such as exploring the marine environment [5] and modifying the enzyme characters through mutagenesis [6, 7]. Protein engineering usually starts by studying the protein character. When the rigorous character and protein model were absent, an examination was necessary using the bioinformatic approach.

Microbial sources and enzyme properties will affect the safety of therapeutic agents. This alternative enzyme therapy is still minimal due to the availability of isolate sources included in the safe species. Arthrospira platensis NIIES-39 was investigated for modeling, binding site, and immunogenicity. Arthrospira platensis or Spirulina platensis is a microalga long known and consumed for supplement purposes. It is generally recognized as a safe microalga in an aquatic ecosystem. Therefore, this microalga and its metabolites are safe for human application [8].

This study investigates the gene that encoded L-ASNase type II from the blue-green algae, Arthrospira platensis. ASNase type II has several benefits over other ASNases. ASNase type II has a higher affinity for L-asparagine. Therefore, it can potentially be an antileukemic agent [9]. Our preliminary investigation revealed that A. platensis could produce L-ASNase [10]. In this study, we further investigated in silico protein model, binding site, and immunogenicity profiles of L-ASNase type II genes in A. platensis to meet health application requirements.

MATERIALS AND METHODS

Data retrieval

The L-asparaginase protein from Arthrospira platensis used for this docking was obtained from the Uniport database (D5A0L1). The SWISS-Model (https://swissmodel.expasy.org/) was used for modeling the L-asparaginase protein. 4pvp.1. A protein Isoaspartyl peptidase/l-asparaginase was used as a template with a sequence identity of 31.47% with a similarity of 0.35. While the ligands used are L-asparagine (Pubchem ID 6267), D-asparagine (439600), D-glutamine (145815), and L-glutamine (5951).

Gene confirmation

The initial gene was retrieved from the Kyoto Encyclopedia of Genes and Genomes) (https://www.genome.jp/kegg/).

Physicochemical characterization of proteins

Physicochemical characteristics of the protein (amino acid, isoelectric point, extension coefficient, stability of protein, aliphatic index, and Grand average hydropathicity) were analyzed by Protparam (https://web.expasy.org/protparam/).

Homology modeling of proteins

The homology modeling of the L-asparaginase gene was performed using I-TASSER. The stereochemical quality and validation were performed using PROCHECK (validation of stereochemistry), ProSA (detection of native structure compatibility), ProQ (quality analysis of the 3-D model of protein), and Profile 3-D analysis. The structural domain of the modeled protein was analyzed by the DIAL server (http://caps.ncbs.res.in/DIAL/DIALserver.html).

Binding site prediction and docking analysis

The potential binding site of L-asparaginase was predicted using the SiteMap application ver 3.8 (https://www.schrodinger.com/sitemap) [11]. Basic parameters were applied for building the binding sites. Further molecular docking analyzes were applied for L-glutamine, L-asparagine, D-glutamine, and D-asparagine. Finally, the specific docking method with a grid box corroborated the COACH for the protein docking prediction.

T-cell and Allergenicity prediction

The immune epitope database (IEDB) (http://tools.iedb.org/mhcii/) was applied to predict the MHC II binding epitopes of L-ASNase [12]. Another method to support the binding epitope predictions, NN-align [13], SMM align [14], Combinatorial library [15], and Net MHCIIpan [16], was applied. Alleles HLA-DRB1 * 01:01, HLA-DRB1 * 03:01, HLA-DRB1 * 04:01, HLA-DRB1 * 07:01, HLA-DRB1 * 011:01, HLA-DRB1 * 13:01, and HLA-DRB1*15:01 were used for epitope prediction. The relative frequency was used to identify the epitope density with the formula of 𝑓𝑖=𝑛𝑖/𝑁=𝑛𝑖/∑𝑗𝑛𝑗fi=ni/N=ni/∑jnj, where ni is the number of predicted immunogenic epitopes, and 𝑁 is the total number of immunogenic and non-immunogenic epitopes [17]. B-cell epitope predictor, BepiPred-2.0, was selected to predict the B-cell epitope [18]. AlgPred 2.0 (https://webs.iiitd.edu.in/raghava/algpred2/batch.html) and AllerCatPro (https://allercatpro.bii.a-star.edu.sg) were applied for the prediction of epitope allergenicity [19, 20].

RESULTS

Physicochemical and amino acid characteristics

The absence of 3D structure data for the L-asparaginase enzyme from A. platensis and the possibility that this enzyme could be used in food and attachment has stimulated us to reconstruct and analyze the potential of this enzyme. In A. platensis, gene asparaginase is located in the order 761202 to 762146 in the genome map. The number of nucleotides is 945, with 314 amino acids. The enzyme has a molecular weight of 33.31 kDa with a theoretical isoelectric point of 4.99. It has an aliphatic index of 94.46. The grand average of hydropathicity is −0.094. The instability index is 35.60. Hence, this protein is classified as stable. In the mammalian system, the estimated half-life is 30 h.

Arthrospira platensis L-asparaginase has good potential because algae have long been used as dietary supplements and are known to be functional foods [21, 22]. In this study, we predicted 3D structures using a SWISS-Model. SWISS models can automatically identify the best templates for building the 3D structure of the protein L-asparaginase from A. platensis utilizing the template of malonate-bound human L-protein asparaginase.

Based on the secondary predictive map of L-asparaginase translated from the entry gene of NIES39_A07830, three major groups, strands, helix, and coil, dominate the enzyme (fig. 1).

Fig. 1: Secondary structure map for L-ASNase from A. platensis

This study modeled the enzyme L-asparaginase from A. platensis using a finalized template from up to 50 templates. The quality alignment to build and predict the model is depicted in fig. 2. The best template is obtained from 4pvp.1. A Isoaspartyl peptidase/l-asparaginase template with a sequence identity of 31.47 % and a similarity of 0.35.

These values indicate that 4pvp.1. A protein is well enough to be used as a template to build a 3D structure model for the D5A0L1 protein.

Fig. 3 shows the result of the 3D protein analysis of D5A0L1 using SWISS-MODEL. Based on the outcome, it is confirmed that the model is dominantly α-helix and β-sheet. Also, Ramachandran plots to investigate psi and phi torsion angles were conducted. As a result, the models have a favored Ramachandran of 92.18% (fig. 4.). Ramachandran outliner accounted for 2.89%, including B140 PRO, A14 GLY, A221 ALA, A170 ASP, B116 SER, A266 GLN, A116 SER, A162 ALA, A118 ASP, A155 GLN, A161 MET, B159 ARG, A158 ASN, B166 ALA, B177 THR, B175 ARG, A175 ARG.

Fig. 2: Sequence alignment between A. platensis D5A0L1 protein and human 4pvp.1. A Isoaspartyl peptidase/l-asparaginase as a template

Fig. 3: Predicted 3D structure of A. platensis NIES 39 L-asparaginase

Fig. 4: Ramachandran plot of D5A0L1 model

Validation analysis using PROSA showed that the Z-score was −6.57 (fig. 5A). This value is still in the range of native conformations. Overall, this value is in the range of negative energy, except for several amino acids (fig. 5B). Between the N-and C-terminal domains, the balance number of residues is colored blue and red (fig. 5C). Energy distribution is mostly in apposition of the zero baselines. ProQ analysis indicated that the model is excellent, with LGscore of 6.006.

Fig. 5: Result of the PROSA web analysis. A. plot of Z-score, B. residue scores of a native protein structure, and C. visualization of molecules

The binding site and molecular docking analysis

According to the COACH analysis, the amino acids at 173,191,193, 201, 204, 205, 223, 225 are the active side of proteins with a confidence score (C score) of 0.74. The binding site is obtained from malonate-bound human L-asparaginase protein's protein template crystal structure (4GDT).

The binding site is then used to analyze the docking capabilities of L-asparagine, D-asparagine, L-glutamine, and D-glutamine. The docking conducted in this research is a specific docking with the grid box following the predictions. Docking results are depicted in fig. 6.

The analysis of amino acid bonds in the binding pocket produces different bonds. The bond between the enzyme active site and the L-asparaginase substrate indicates the Vander wall bond and hydrogen bond (fig. 7A). While in the D-asparagine substrate, although the bonds and interactions are similar, there are some inappropriate donor acceptors (fig. 7B). In the L-glutamine and D-glutamine substrates, there are hydrogen and carbon-hydrogen bonds, as well as the interaction of van der Waals with several unfavorable donors (fig. 7C, and 7D)

Fig. 6: Docking L-asparaginase with L-asparagine (Red), D-glutamine (Orange), L-glutamine (Yellow), D-asparagine (Green)

Fig. 7: Binding pocked L-asparaginase with amino acids. L-asparagine (A), D-asparagine (B), L-glutamine (C), D-glutamine (D)

Immunogenicity analysis

Fig. 8: Relative frequency of predicted immunogenic T-cell epitopes in A. platensis-ANSase for three alleles (HLA-DRB1*01:01, HLA-DRB1*04:01, HLA-DRB1*015:01)

DISCUSSION

Arthrospira platensis L-asparaginase has good potential because algae have long been used as dietary supplements and are known to be functional foods [21-23]. In this study, we analyzed the physicochemical of the protein and predicted 3D structures using a SWISS-Model. SWISS models can automatically identify the best templates for building the 3D structure of the protein L-asparaginase from A. platensis utilizing the template of malonate-bound human L-protein asparaginase. The molecular size of A. platensis asparaginase (33.9 kDa) is slightly smaller than the well-known asparaginase type II from E. coli (34.9 kDa) and E. carotovora (35.3 kDa) [24]. It is much smaller when compared to hexamer L-asparaginase from Thermus thermophilus (200 kDa) [25]. Several L-asparaginases with similar molecular weights are L-asparaginase from Bacillus flexus strain SS (33 kDa) [26] and Yersinia Pseudotuberculosis (33.9 kDa) [27].

Lubkowski et al. [28] described the double displacement reaction mechanism in E. coli ASNase II. A conserved threonine residue is considered to provide the nucleophile hydroxy-group that attacks the asparagine amide bond during the reaction. All homotetrameric L-ASNse from mesophilic bacteria follow a similar catalysis mechanism involving two nucleophilic substitutions followed by forming a covalent and non-covalent intermediate with different substrates, as well as tetrahedral intermediates during the catalytic process [29].

The identified signatures PS00144, Asparaginase/glutaminase active site signature 1 have active site consensus patterns [LIVM]-x-{L}-T-G-G-T-[IV]-[AGS] [GIV] [30]. Analysis with PROSITE program revealed that B. subtilis ETMC-2 L-ASNase had a lipoprotein signal peptide sequence of twenty-two amino acids in the beginning (N′ MKKQ–R—MLVLFTALLFVFTGCSL; residues 1–22) and asparaginase/glutaminase domains (residues 51–375) which are noncytoplasmic. N′ terminal signal sequence (20 amino acids) in B. subtilis L-ASNase was reported earlier. Removing a signal peptide may be the most important part of the enzyme's active site [31]. The Thr61, Tyr75, Thr89, Ser108, Thr121, Thr141, Asp142, and Lys214 are important amino acids that may play an important role in the active site of the enzyme. Enzyme catalysis sites include the Gly16, Asp171, Ala173, dan Arg175 residues with ASN ligand. Well-known residues from Bacillus substilis ASNase are Thr89, Thr121, and Asp122 [32]. Meanwhile, for Escherichia coli ASNase are Thr15, Tyr29, Ser62, Glu63, Thr95, Asp 96, Ala120, Lys168 [33].

The average value of the relative frequency of the A. platensis ANSase was 0.04. This value is lower than the E. coli ASNase, with a value of 0.08. Identifying the immunogen epitope is crucial in predicting the peptide responsible for the allergy reaction. HLA-DRB1 is an allele that has high-affinity binding to ASNase epitopes [34] with a relative frequency of T-cell epitopes with eight alleles. Two alleles, namely HLA-DRB1*01:01 and HLA-DRB1*07:01, have been recognized for their link to hypersensitivity reactions. HLA-DRB1*01:01 has an association with cow milk allergen and an immune response related to antiretroviral agents, nevirapine, and cockroach [35]. Meanwhile, HLA-DRB1*07:01 correlates with the immune response related to L-ASNase therapy [36].

Based on the analysis using AlgPred 2.0 and AllerCatPro, the protein D5A0L1 is considered non-allergenic. These results were corroborated by the analysis using AlgPred based on epitope IgE mapping, SVM method based on amino acid composition, and a hybrid approach (SVMc+IgE epitope+ARPs BLAST+MAST), which produced a consistent conclusion that the protein D5A0L1 is a non-allergic protein.

CONCLUSION

This outlook is the first of a microalgae L-ASNase. Human 4pvp.1. A Isoaspartyl peptidase/l-asparaginase is a good template for enzyme modeling. The enzyme active site relied on amino acids at 173,191,193, 201, 204, 205, 223, 225 positions. Arthrospira platensis ASNase type II has the potential as an alternative therapeutic agent. This is because this enzyme had lower allergenic levels than commercial ASNases.

FUNDING

Nil

AUTHORS CONTRIBUTIONS

All the authors have contributed equally.

CONFLICT OF INTERESTS

Declared none

REFERENCES

  1. Nunes JCF, Cristovao RO, Santos Ebinuma VC, Faria JL, Silva CG, Neves MC. L-asparaginase-based biosensors. Encyclopedia. 2021 Aug 20;1(3):848-58. doi: 10.3390/encyclopedia1030065.

  2. Onishi Y, Prihanto AA, Yano S, Takagi K, Umekawa M, Wakayama M. Effective treatment for suppression of acrylamide formation in fried potato chips using L-asparaginase from Bacillus subtilis. 3 Biotech. 2015 Feb 4;5(5):783-9. doi: 10.1007/s13205-015-0278-5, PMID 28324531.

  3. Xu F, Oruna Concha MJ, Elmore JS. The use of asparaginase to reduce acrylamide levels in cooked food. Food Chem. 2016 Nov 1;210:163-71. doi: 10.1016/j.foodchem.2016.04.105, PMID 27211635.

  4. Baskar G, Aiswarya R. Overview on mitigation of acrylamide in starchy fried and baked foods. J Sci Food Agric. 2018 May 11;98(12):4385-94. doi: 10.1002/jsfa.9013, PMID 29572830.

  5. Prihanto AA, Wakayama M. Marine microorganism: an underexplored source of l-asparaginase. Adv Food Nutr Res. 2016 Aug 26;79:1-25. doi: 10.1016/bs.afnr.2016.07.005, PMID 27770857.

  6. Kotzia GA, Labrou NE. Engineering thermal stability of l-asparaginase by in vitro directed evolution. FEBS Journal. 2009 Feb 25;276(6):1750-61. doi: 10.1111/j.1742-4658.2009.06910.x, PMID 19220855.

  7. Verma S, Mehta RK, Maiti P, Rohm KH, Sonawane A. Improvement of stability and enzymatic activity by site-directed mutagenesis of E. coli asparaginase II. Biochim Biophys Acta. 2014 Apr 8;1844(7):1219-30. doi: 10.1016/j.bbapap.2014.03.013, PMID 24721562.

  8. Ampofo J, Abbey L. Microalgae: Bioactive composition, health benefits, safety and prospects as potential high-value ingredients for the functional food industry. Foods. 2022 Jun 14;11(12):1744-64. doi: 10.3390/foods11121744, PMID 35741941.

  9. Sharafi Z, Barati M, Khoshayand MR, Adrangi S. Screening for type II L-asparaginases: lessons from the genus Halomonas. Iran J Pharm Res. 2017;16(4):1565-73. PMID 29552065.

  10. Prihanto AA, Wakayama M. Combination of environmental stress and localization of L-asparaginase in Arthrospira platensis for production improvement. 3 Biotech. 2014 Apr 13;4(6):647-53. doi: 10.1007/s13205-014-0215-z, PMID 28324309.

  11. Halgren TA. Identifying and characterizing binding sites and assessing druggability. J Chem Inf Model. 2009 Jan 20;49(2):377-89. doi: 10.1021/ci800324m, PMID 19434839.

  12. Wang P, Sidney J, Dow C, Mothe B, Sette A, Peters B. A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach. PLOS Comput Biol. 2008;4(4):e1000048. doi: 10.1371/journal.pcbi.1000048, PMID 18389056.

  13. Nielsen M, Lund O. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. BMC Bioinf. 2009 Sep 18;10:296. doi: 10.1186/1471-2105-10-296, PMID 19765293.

  14. Nielsen M, Lundegaard C, Lund O. Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinf. 2007 Jul 4;8(238):238. doi: 10.1186/1471-2105-8-238, PMID 17608956.

  15. Sidney J, Assarsson E, Moore C, Ngo S, Pinilla C, Sette A. Quantitative peptide binding motifs for 19 human and mouse MHC class I molecules derived using positional scanning combinatorial peptide libraries. Immunome Res. 2008 Jan 25;4(1):1-14. doi: 10.1186/1745-7580-4-2.

  16. Andreatta M, Karosiene E, Rasmussen M, Stryhn A, Buus S, Nielsen M. Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification. Immunogenetics. 2015 Sep 29;67(11-12):641-50. doi: 10.1007/s00251-015-0873-y, PMID 26416257.

  17. de Belen RAJ, Bednarz T, Sowmya A, Del Favero D. Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019. Transl Psychiatry. 2020 Sep 30;10(1):333. doi: 10.1038/s41398-020-01015-w, PMID 32999273.

  18. Jespersen MC, Peters B, Nielsen M, Marcatili P. MarcatiliBepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res. 2017 Jul 3;45(W1):W24-9. doi: 10.1093/nar/gkx346, PMID 28472356.

  19. Dimitrov I, Bangov I, Flower DR, Doytchinova I. AllerTOP v.2–a server for in silico prediction of allergens. J Mol Model. 2014 May 31;20(6):2278. doi: 10.1007/s00894-014-2278-5, PMID 24878803.

  20. Maurer Stroh S, Krutz NL, Kern PS, Gunalan V, Nguyen MN, Limviphuvadh V. AllerCatPro-prediction of protein allergenicity potential from the protein sequence. Bioinformatics. 2019 Sep 1;35(17):3020-7. doi: 10.1093/bioinformatics/btz029, PMID 30657872.

  21. Azabji Kenfack M, Dikosso SE, Loni EG, Onana EA, Sobngwi E, Gbaguidi E. Potential of Spirulina platensis as a nutritional supplement in malnourished HIV-Infected Adults in sub-Saharan Africa: A Randomised, single-blind study. Nutr Metab Insights. 2011;4:29-37. doi: 10.4137/NMI.S5862, PMID 23946659.

  22. Gutierrez Salmean G, Fabila Castillo L, Chamorro Cevallos G. Nutritional and toxicological aspects of Spirulina (Arthrospira). Nutr Hosp. 2015 Jul 1;32(1):34-40. doi: 10.3305/nh.2015.32.1.9001, PMID 26262693.

  23. Kulkarni S, Chavan D. Nutritional and therapeutic evaluation of Spirulina platensis. Asian J Pharm Clin Res. 2020 Jul;13(7):86-90. doi: 10.22159/ajpcr.2020.v13i7.34123.

  24. Schalk AM, Nguyen HA, Rigouin C, Lavie A. Identification and structural analysis of an L-asparaginase enzyme from guinea pig with putative tumor cell killing properties. J Biol Chem. 2014 Nov 28;289(48):33175-86. doi: 10.1074/jbc.M114.609552. PMID 25320094.

  25. Pritsa AA, Kyriakidis DA. L-asparaginase of thermus thermophilus: purification, properties and identification of essential amino acids for its catalytic activity. Mol Cell Biochem. 2001;216(1-2):93-101. doi: 10.1023/a:1011066129771. PMID 11216870.

  26. Chand S, Mihooliya KN, Sahoo DK, Prasad JP, Sharma G. L-asparaginase from Bacillus flexus strain SS: isolation, screening, production process optimization, purification, and anticancer activity. Appl Biochem Microbiol. 2022;58(4):416-27. doi: 10.1134/S0003683822040032.

  27. Sidoruk KV, Pokrovsky VS, Borisova AA, Omeljanuk NM, Aleksandrova SS, Pokrovskaya MV. Creation of a producent, optimization of expression, and purification of recombinant Yersinia pseudotuberculosis L-asparaginase. Bull Exp Biol Med. 2011;152(2):219-23. doi: 10.1007/s10517-011-1493-7. PMID 22808465.

  28. Lubkowski J, Vanegas J, Chan WK, Lorenzi PL, Weinstein JN, Sukharev S. Mechanism of catalysis by l-asparaginase. Biochemistry. 2020 May 26;59(20):1927-45. doi: 10.1021/acs.biochem.0c00116. PMID 32364696.

  29. Lubkowski J, Wlodawer A. Geometric considerations support the double-displacement catalytic mechanism of l-asparaginase. Protein Sci. 2019 Oct;28(10):1850-64. doi: 10.1002/pro.3709. PMID 31423681, PMC6740147.

  30. https://prosite.expasy.org/PS00144. [Last accessed on 17 Dec 2022]

  31. Feng Y, Liu S, Jiao Y, Gao H, Wang M, Du G. Enhanced extracellular production of L-asparaginase from bacillus subtilis 168 by B. subtilis WB600 through a combined strategy. Appl Microbiol Biotechnol. 2017;101(4):1509-20. doi: 10.1007/s00253-016-7816-x, PMID 27796436.

  32. Agrawal S, Jana UK, Kango N. Heterologous expression and molecular modelling of L-asparaginase from Bacillus subtilis ETMC-2. Int J Biol Macromol. 2021;192:28-37. doi: 10.1016/j.ijbiomac.2021.09.186, PMID 34610352.

  33. Li X, Zhang X, Xu S, Xu M, Yang T, Wang L. Insight into the thermostability of thermophilic L-asparaginase and non-thermophilic L-asparaginase II through bioinformatics and structural analysis. Appl Microbiol Biotechnol. 2019;103(17):7055-70. doi: 10.1007/s00253-019-09967-w, PMID 31273395.

  34. Wetzler M. Asparaginase allergies: it’s all in the genes. Blood. 2014 Aug 21;124(8):1206-7. doi: 10.1182/blood-2014-07-585919, PMID 25147374.

  35. Link J, Lundkvist Ryner M, Fink K, Hermanrud C, Lima I, Brynedal B. Human leukocyte antigen genes and interferon beta preparations influence risk of developing neutralizing anti-drug antibodies in multiple sclerosis. Plos One. 2014 Mar 7;9(3):e90479. doi: 10.1371/journal.pone.0090479. PMID 24608124.

  36. Kutszegi N, Yang X, Gezsi A, Schermann G, Erdelyi DJ, Semsei AF. HLA-DRB1*07:01-HLA-DQA1*02:01-HLA-DQB1*02:02 haplotype is associated with a high risk of asparaginase hypersensitivity in acute lymphoblastic leukemia. Haematologica. 2017 Sep;102(9):1578-86. doi: 10.3324/haematol.2017.168211, PMID 28596278.