Beta Fulltext view is in preview — article structure may vary. Browse all articles
Contents
Bioinformatics & Proteomics Open Access Journal Research Article 1 min read

Screening for Alternative Sources of L-Asparaginase Used in Acute Lymphoblastic Leukaemia (All) Treatment: An In Silico Approach

Joy ZF, Purkaystha A, Das NK, Al-Hakim, Chakrabarty S and Hasan M*
* Corresponding author
ISSN: 2642-6129  10.23880/bpoj-16000128  Received: March 27, 2019  Published: April 12, 2019
  views
 5 figures
 2 tables
PDF
Keywords
L-asparaginase Acute Lymphoblastic Leukemia (ALL) In Silico Analysis Plylogeny
Abstract

Acute Lymphoblastic Leukemia (ALL) is the most prevalent acute leukemia in children and it also represents a devastating disease when it occurs in adults. Within the United States, the incidence of ALL is estimated at 1.6 per 100 000 population and an estimated 6590 new cases were diagnosed in 2016 alone. The enzyme L-asparaginase (L-Asp) is being used for treatment of childhood acute lymphoblastic leukemia (ALL) for many years because of its unique pharmacological features and historically improved treatment outcomes. As L‑asparaginase demonstrates relative substrate specificity and at the same time affects the glutamine metabolism, these may intensify adverse effects including hepatotoxicity, hemostatic disorders and hyperglycemia. That’s why alternative L-asparaginase sources are crying needed to tackle the present drawbacks of commercially available L-asparaginase (For example, PEG-asparaginase from Erwiniachry santhemi). The present study planned to suggest an alternative source of L-asparaginase for ALL treatment by in silico analysis, mostly for child patient. The study included phylogenetic tree construction, physiochemical properties analysis, the secondary structure screening and three-dimensional structure prediction of proposed Lasparaginase. After phylogeny analysis and in slico screening of physiochemical and secondary properties, homology modeling of L-asparaginase Shigella boydii (WP_000513786.1) was uggested as the best alternative option for ALL treatment rather than commercially available L-asparaginase sources.

Figures

Figure 1: Phylogenetic Tree of L-asparaginase Generated by MEGA 4.2.
Click to enlarge
Figure 1: Phylogenetic Tree of L-asparaginase Generated by MEGA 4.2.
Figure 2: Homology modeling of alternative L- asparaginase. (2A) Best three-dimensional model of Shigella boydiiand (2B) Best three-dimensional model of Shigella flexneri k-315 Model Validation However, in case of Shigella boydii (WP_000513786.1), RAMPAGE, ERRAT, PROCHECK found better result. ERRAT validated models by statistical relation of non- bonded interactions among different atom types based on characteristic atomic interaction20. It assesses overall quality of a model at 0.01 and 0.05 level of significance and presents result as overall quality factor. Standard high resolution structures generally produces values around 95% or higher. Low resolution structures produced values around 91%. Figure 3 illustrate ERRAT score of predicted models of Shigella boydii, which scored overall quality factor more than 90%. Again, the best models form Shigella boydii, which was selected on the basis of RAMPAGE scored more than 84% (Figure 4). This range suggests quality models were predicted by using I- Tasser. PROCHECK tests stereochemical quality of protein structure by evaluating residue-by-residue geometry and overall structural geometry. The best model suggested that more than 77% amino acid residues were in most favored region for these models (Figure 5).
Click to enlarge
Figure 2: Homology modeling of alternative L- asparaginase. (2A) Best three-dimensional model of Shigella boydiiand (2B) Best three-dimensional model of Shigella flexneri k-315 Model Validation However, in case of Shigella boydii (WP_000513786.1), RAMPAGE, ERRAT, PROCHECK found better result. ERRAT validated models by statistical relation of non- bonded interactions among different atom types based on characteristic atomic interaction20. It assesses overall quality of a model at 0.01 and 0.05 level of significance and presents result as overall quality factor. Standard high resolution structures generally produces values around 95% or higher. Low resolution structures produced values around 91%. Figure 3 illustrate ERRAT score of predicted models of Shigella boydii, which scored overall quality factor more than 90%. Again, the best models form Shigella boydii, which was selected on the basis of RAMPAGE scored more than 84% (Figure 4). This range suggests quality models were predicted by using I- Tasser. PROCHECK tests stereochemical quality of protein structure by evaluating residue-by-residue geometry and overall structural geometry. The best model suggested that more than 77% amino acid residues were in most favored region for these models (Figure 5).
Figure 3: ERRAT result of best models for L- asparaginase of Shigella Boydii.
Click to enlarge
Figure 3: ERRAT result of best models for L- asparaginase of Shigella Boydii.
Figure 4: RAMPAGE output of best models for L- asparaginase of Shigella boydii.
Click to enlarge
Figure 4: RAMPAGE output of best models for L- asparaginase of Shigella boydii.
Figure 5: PROCHECK analysis result for best models for L-asparaginase of Shigella boydii.
Click to enlarge
Figure 5: PROCHECK analysis result for best models for L-asparaginase of Shigella boydii.

Tables

Accession numberMolecular weightTheoretical pIGRAVYInstability indexAliphatic index
WP_040002975.132695.95.040.01928.9289.58
WP_029685647.1368594.89-0.17836.880
WP_051619038.141337.74.98-0.07128.6283.93
WP_024107744.1337875.32-0.07528.6285.56
WP_012882884.1383135.11-0.1128.1185.29
WP_019938543.134005.55.450.06620.4488.2
WP_025517848.139017.15.38-0.02926.6887.72
WP_051484355.142336.75.08-0.05229.0985.36
KGM80445.146629.94.920.04129.4689.75
WP_000513786.151074.84.92-0.0327.4785.61
WP_039059459.155521.64.91-0.0926.381.74
EIQ25923.133326.94.880.11233.7890.69
WP_000513771.133287.94.930.12532.8691
EST84617.133004.54.830.09433.9390.6
NP_001077395.132054.55.84-0.1316.6884.87

Table 1: Prot Param analysis of selected amino acid from Phylogenetic Tree.

Extranded
ProteinAlpha helix310 helixPi helixBeta bridgeBeta turnBend regionRandom coil
strand
WP_040002975.140.58%0.00%0.00%0.00%19.49%10.86%0.00%29.07%
WP_029685647.135.94%0.00%0.00%0.00%20.62%13.12%0.00%30.31%
WP_051619038.138.24%0.00%0.00%0.00%21.94%14.73%0.00%25.08%
WP_024107744.137.89%0.00%0.00%0.00%19.88%11.80%0.00%30.43%
WP_012882884.138.94%0.00%0.00%0.00%19.31%11.84%0.00%29.91%
WP_019938543.137.80%0.00%0.00%0.00%16.46%11.28%0.00%34.45%
WP_025517848.140.00%0.00%0.00%0.00%18.46%12.92%0.00%28.62%
WP_051484355.139.74%0.00%0.00%0.00%15.38%12.50%0.00%32.37%
KGM80445.139.25%0.00%0.00%0.00%18.38%12.77%0.00%29.60%
WP_000513786.137.07%0.00%0.00%0.00%19.94%12.46%0.00%30.53%
WP_039059459.138.01%0.00%0.00%0.00%18.38%13.08%0.00%30.53%
EIQ25923.138.94%0.00%0.00%0.00%18.38%13.08%0.00%29.60%
WP_000513771.138.63%0.00%0.00%0.00%18.38%12.15%0.00%30.84%
EST84617.139.75%0.00%0.00%0.00%17.67%12.93%0.00%29.65%
NP_001077395.128.90%0.00%0.00%0.00%26.30%11.04%0.00%33.77%

Table 2: Secondary Structure Analysis of Closely Related L-asparaginase.

Cite this article

BibTeX
APA
RIS
@article{joy2019,
  title   = {Screening for Alternative Sources of L-Asparaginase Used in Acute Lymphoblastic Leukaemia (All) Treatment: An In Silico Approach},
  author  = {Joy ZF, Purkaystha A, Das NK, Al-Hakim, Chakrabarty S and
Hasan M},
  journal = {Bioinformatics & Proteomics Open Access Journal},
  year    = {2019},
  volume  = {3},
  number  = {1},
  doi     = {10.23880/bpoj-16000128}
}
Joy ZF, Purkaystha A, Das NK, Al-Hakim, Chakrabarty S and
Hasan M (2019). Screening for Alternative Sources of L-Asparaginase Used in Acute Lymphoblastic Leukaemia (All) Treatment: An In Silico Approach. Bioinformatics & Proteomics Open Access Journal, 3(1). https://doi.org/10.23880/bpoj-16000128
TY  - JOUR
TI  - Screening for Alternative Sources of L-Asparaginase Used in Acute Lymphoblastic Leukaemia (All) Treatment: An In Silico Approach
AU  - Joy ZF, Purkaystha A, Das NK, Al-Hakim, Chakrabarty S and
Hasan M
JO  - Bioinformatics & Proteomics Open Access Journal
PY  - 2019
VL  - 3
IS  - 1
DO  - 10.23880/bpoj-16000128
ER  -