Applying Bioinformatic Tools for Modeling and Modifying Type II E. coli l-Asparginase to Present a Better Therapeutic Agent/Drug for Acute Lymphoblastic Leukemia

Mahboobi, Mahdieh; Sedighian, Hamid; Hedayati, Mojtaba; Bambai, Bijan; Soofian, Saeed Esmaeil; Amani, Jafar

doi:10.5812/ijcm.5785

Cited by 20 publications

(11 citation statements)

References 35 publications

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“…3D models of chimeric protein have been created by i-Tasser, uploaded to the Swiss-PdbViewer server to describe the tertiary structural illustrations (32).…”

Section: Tertiary Structural Prediction For the Chimeric Proteinmentioning

confidence: 99%

In Silico Evaluation of Two Targeted Chimeric Proteins Based on Bacterial Toxins for Breast Cancer Therapy

et al. 2019

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Background: Despite major advances in cancer research, breast cancer still remains the most common cancer in women. Breast cancer is a heterogeneous disease, including at least 5 subtypes. Overexpression of human epidermal growth factor receptor 2 (HER2) in the patients denotes poor prognosis leading to a reduced survival rate compared to other subtypes of breast cancer. Therefore, HER2 can be a potential therapeutic target. To enhance the potency of HER2 blockers, the conjugation of specific cytotoxic agents with these types of anticancer agents may be successful. Application of antibody-based agents are important emerging anticancer therapies. One novel approach to increase the potency of monoclonal antibodies (mAbs) is combining them with toxic molecules. Objectives: Pseudomonas exotoxin A (PE), ricin toxin (RT), and others in very minor quantities can be more potent and biologically active for this purpose. Methods: In this study, we used trastuzumab as a ligand for HER2 receptor along with Pseudomonas exotoxin A (PE38) and A subunit of Shiga toxin 2a (Stx2a). This fusion protein selectively bound to the HER2 receptor. Upon uptake by the target cells, apoptosis and cell eradication was observed. An in silico method was used before the in vitro study to illustrate the properties and construction of the protein. Physicochemical properties, structure, stability, and ligand-receptor interaction of this chimeric protein were predicted by means of computational and bioinformatics tools and servers. Results: The results of this study showed that codon adaptation index of s1 and p2 fusion gene has improved to 0.98 and .99, respectively. The mfold result has revealed that s1 and p2 mRNA were stable sufficient for efficient translation in the new host. Based on Ramachandran plot, s1 and p2 were categorized as constant fusion protein. Conclusions: Finally, based on docking software analysis, the binding ability of Herceptin was robust enough to its receptor, so these constructs could be assigned as a new antitumor candidate in cancer therapy. The results suggested that s1 and p2 were stable fusion proteins with accurate affinity to the overexpressed receptors making them potential candidates for inducing apoptosis in breast cancer cells.

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“…3D models of chimeric protein have been created by i-Tasser, uploaded to the Swiss-PdbViewer server to describe the tertiary structural illustrations (32).…”

Section: Tertiary Structural Prediction For the Chimeric Proteinmentioning

confidence: 99%

In Silico Evaluation of Two Targeted Chimeric Proteins Based on Bacterial Toxins for Breast Cancer Therapy

et al. 2019

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“…The identification of immunogenic epitopes in the protein structure by computational tools has been a strategy used to achieve a reduction in the immunogenicity of several proteins [78][79][80]. It is also the case of ASNase from E. coli, where the use of bioinformatics techniques has allowed engineering the enzyme with the aim of obtaining an improved therapeutic agent [58,81,82].…”

Section: Resultsmentioning

confidence: 99%

Immunogenicity assessment of fungal l-asparaginases: an in silico approach

et al. 2020

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Acute lymphoblastic leukemia is the most common cancer among children worldwide, characterized by an overproduction of undifferentiated lymphoblasts in the bone marrow. The enzyme l-asparaginase isolated from Escherichia coli and Dickeya chrysanthemi is a key factor in multiple therapies against this disease. Regardless of their effectiveness, these formulations present well-known adverse effects, highlighting the immunogenicity and allergenicity they cause. Some strategies have been adopted in this regard, such as PEGylation and modification by bioengineering as well as the search for new non-bacterial microorganisms producing the enzyme. Fungi have been shown to be asparaginase producers with high antitumor activity; however, little is known about the immunological features of fungal asparaginase. In this work, we developed the first immunoinformatics study focused on revealing the antigenic determinants that contribute to the immunogenicity of nine asparaginases of filamentous fungi experimentally tested, and compared them with the enzyme from E. coli. We were able to predict that the fungal asparaginases evaluated have a high degree of immunogenicity, which is an important result to consider if the aim is to produce clinical l-asparaginase from a fungal source. Likewise, for the first time, a bioinformatics-based approach was used to predict the immunogenic and allergenic epitopes present in fungal asparaginases.

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“…This kind of findings has advantages in the field of medicine. Mahboobi et al, in 2017 showed interest in the application of bioinformatics tools to study bacteria such as E. coli model. He reported that E. coli is a good source for the production of amylase enzyme.…”

Section: Algpred Methodsmentioning

confidence: 99%

Prediction of the allergic response of extracellular amylase producing bacteria through in-silico method

Sharma

Kaushik

Tomar

2019

ijrps

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Allergies intolerance is a common problem worldwide. The major difficulties are related to the correct diagnosis of causes which is associated with amino acid sequences present in the epitope region of allergen. So there is a need to find out the factors causing allergies and allergens themselves. In the present study a bioinformatics tool is used to predict amino acid sequence and mast cell association with different integrated approaches. Internet databases for amylase producing bacteria were used in In-silico method to check the allergy for microorganism producing the extracellular enzyme. Amylase is an extracellular enzyme isolated from soil bacteria Salmonella species and Proteus vulgaris. It is very important in the pharmaceutical industry to check the allergenicity of any drug, protein or enzyme that be used in the treatment of diseases or food industries for various purpose. The aim of the present study is isolation and characterisation of extracellular enzyme produced from soil bacteria and to analyzed allergic response through AlgPred tool of bioinformatics. From results, it was concluded that the protein sequence of amylase did not contain any epitope, no hits for mast and blast which proved that it was not an allergen. So, bacterial isolates from the industrial soil are a good alternative source of enzyme production and may be used as an industrial level. Thus, from the results, it may be concluded that microbes from soil sample can be a good source of industrially important enzymes without any allergy.

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Applying Bioinformatic Tools for Modeling and Modifying Type II E. coli l-Asparginase to Present a Better Therapeutic Agent/Drug for Acute Lymphoblastic Leukemia

Cited by 20 publications

References 35 publications

In Silico Evaluation of Two Targeted Chimeric Proteins Based on Bacterial Toxins for Breast Cancer Therapy

In Silico Evaluation of Two Targeted Chimeric Proteins Based on Bacterial Toxins for Breast Cancer Therapy

Immunogenicity assessment of fungal l-asparaginases: an in silico approach

Prediction of the allergic response of extracellular amylase producing bacteria through in-silico method

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