اخبار و اعلانات

 آمار

تعداد نشریات24
تعداد شماره‌ها903
تعداد مقالات8,081
تعداد مشاهده مقاله15,203,329
تعداد دریافت فایل اصل مقاله12,847,695
Tafvizi, Negar, Behbahani, Mandana, Mohabatkar, Hassan. (1403). In-silico comparison of fungal and bacterial asparaginase enzymes. , 13(4), 183-191. doi: 10.22099/mbrc.2024.50123.1981
Negar Tafvizi; Mandana Behbahani; Hassan Mohabatkar. "In-silico comparison of fungal and bacterial asparaginase enzymes". , 13, 4, 1403, 183-191. doi: 10.22099/mbrc.2024.50123.1981
Tafvizi, Negar, Behbahani, Mandana, Mohabatkar, Hassan. (1403). 'In-silico comparison of fungal and bacterial asparaginase enzymes', , 13(4), pp. 183-191. doi: 10.22099/mbrc.2024.50123.1981
Tafvizi, Negar, Behbahani, Mandana, Mohabatkar, Hassan. In-silico comparison of fungal and bacterial asparaginase enzymes. , 1403; 13(4): 183-191. doi: 10.22099/mbrc.2024.50123.1981

In-silico comparison of fungal and bacterial asparaginase enzymes

Molecular Biology Research Communications
دوره 13، شماره 4، 2024، صفحه 183-191    اصل مقاله (743.35 K)
نوع مقاله: Original article
شناسه دیجیتال (DOI): 10.22099/mbrc.2024.50123.1981
نویسندگان
Negar Tafvizi؛ Mandana Behbahani؛ Hassan Mohabatkar*
Department of Biotechnology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran
چکیده
L-asparaginase is a commercial enzyme with a wide variety of applications. Asparaginase is known as an anti-cancer agent that is effective for the treatment of certain lymphomas and leukemias by growth inhibition of human cancer cells. Additionally, asparaginase is used in the food industry in a pretreatment process to decrease the accumulation of carcinogenic acrylamide. In this paper, different aspects of bacterial and fungal asparaginases such as mass, hydrophobicity and hydrophilicity of pseudo amino acid composition (PseAAC), physicochem-ical properties, and structural motifs were studied, and ROC curve statistical analysis was used for the comparison. The results showed that none of the physicochemical properties of fungal and bacterial asparaginase could not be differed, except molecular weight and sequence length. MEME Suite analysis demonstrated that there was a motif that was specific for bacterial asparaginases. However, analysis based on the concept of PseACC indicated a differentiation line between fungal and bacterial asparaginases. In conclusion, although there was not any specific demonstration to separate the bacterial and fungal asparaginases in the case of physicochemical properties, PseAAC analysis can be an appropriate and usable method to differentiate between them.
کلیدواژه‌ها
L-asparaginase؛ Pseudo amino acid composition؛ Physicochemical properties
مراجع
  1. Darvishi F, Faraji N, Shamsi F. Production and structural modeling of a novel asparaginase in Yarrowia lipolytica. Int J Biol Macromol 2019;125:955-961.
  2. Batool T, Makky EA, Jalal M, Yusoff MM. A comprehensive review on L-asparaginase and its applications. Appl Biochem Biotechnol 2016;178:900-923.
  3. Ohnuma T, Bergel F, Bray RC. Enzymes in cancer: asparaginase from chicken liver. Biochem J 1967;103:238-245.
  4. Campell HA, Mashburn LT, Boyse EA, Old LJ. Two L-asparaginases from Escherichia coli B. Their separation, purification, and antitumor activity. Biochemistry 1967;6:721-730.
  5. Souza PM, de Freitas MM, Cardoso SL, Pessoa A, Guerra ENS, Magalhaes PO. Optimization and purification of L-asparaginase from fungi: A systematic review. Crit Rev Oncol Hematol 2017;120:194-202.‏
  6. Dhanam JG. Kannan S. L-asparaginase-Types, perspectives and applications. Advanced BioTech 2013;13:01-05.
  7. Cachumba JJM, Fernandes Antunes FA, Dias Peres GF, Brumano LP, Dos Santos JC, Da Silva SS. Current applications and different approaches for microbial L-asparaginase production. Braz J Microbiol 2016;47:77-85.
  8. Shi R, Liu Y, Mu Q, Jiang Z, Yang S. Biochemical characterization of a novel L-asparaginase from Paenibacillus barengoltzii being suitable for acrylamide reduction in potato chips and mooncakes. Int J Biol Macromol 2017; 96:93-99.‏
  9. Duval M, Suciu S, Ferster A, Rialland X, Nelken B, Lutz P, Benoit Y, Robert A, Manel AM, Vilmer E, Otten J, Philippe N. Comparison of Escherichia coli–asparaginase with Erwinia-asparaginase in the treatment of childhood lymphoid malignancies: results of a randomized European Organisation for Research and Treatment of Cancer-Children's Leukemia Group phase 3 trial. Blood 2002;99:2734-2739.‏
  10. Mousavi SE, Mohabatkar H, Behbahani M. Comparative in silico analysis of fungal and bacterial alkaline serine proteases: Insights into structure, function, and evolution. Iran J Sci 2024;48:1-8.‏
  11. Dwivedi VD, Mishra SK. In silico analysis of L-asparaginase from different source organisms. Interdiscip Sci 2014;6:93-99.
  12. Elsaba YM, Salama WH, Soliman ERS. In silico and biochemical analysis on a newly isolated Trichoderma asperellum l-asparaginase. Biocatal Agricul Biotech 2022;40: 102309.
  13. Zadeh Hosseingholi E, Neghabi N, Molavi G, Gheibi Hayat SM, Shahriarpour H. In silico identification and characterization of antineoplastic asparaginase enzyme from endophytic bacteria. IUBMB Life 2020;72:991-1000.
  14. Chou KC, Shen HB. Recent advances in developing web-servers for predicting protein attributes. Nat Sci 2009;1:63-92.
  15. Chou KC. Prediction of protein cellular attributes using pseudo‐amino acid composition.  Proteins 2001;43:246-255.
  16. Su W, Qian X, Yang K, Ding H, Huang C, Zhang Z. Recognition of outer membrane proteins using multiple feature fusion. Front Genet 2023;14:1211020.‏
  17. Samman N, Mohabatkar H, Rabiei P. Using several pseudo amino acid composition types and different machine learning algorithms to classify and predict archaeal phospholipases. Mol Biol Res Commun 2023;12:117-126.
  18. Huang A, Lu F, Liu F. Discrimination of psychrophilic enzymes using machine learning algorithms with amino acid composition descriptor. Front Microbiol 2023;14:1130594.
  19. Shen HB, Chou KC. PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition. Anal Biochem 2008;373:386-388.
  20. Manavalan B, Basith S, Shin TH, Wei L, Lee G. mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation. Bioinformatics 2019;35:2757-2765.
  21. Sen TZ, Jernigan RL, Garnier J, Kloczkowski A. GOR V server for protein secondary structure prediction. Bioinformatics 2005;21:2787-2788.‏
  22. Alballa M, Butler G. Integrative approach for detecting membrane proteins. BMC Bioinformatics 2020;21:575.‏
  23. Boulesteix AL, Janitza S, Kruppa J, König IR. Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. Wiley Interdiscip Rev Data Min Knowl Discov 2012;2:493-507.
  24. Sajana OK, Sajesh TA. Robust quadratic discriminant analysis using Sn covariance.  Commun Stat-Simul C 2023;52:735-744.‏
  25. Rost B. Protein secondary structure prediction continues to rise. J Struct Biol 2001;134: 204-218.‏
  26. Garnier J, Gibrat JF, Robson B. GOR method for predicting protein secondary structure from amino acid sequence. Methods Enzymol 1996;266:540-553.‏
  27. Bailey TL, Boden M, Buske FA, Frith M, Grant CE, Clementi L, Ren J, Li WW, Noble WS. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res 2009; 37(suppl_2):W202-W208.
  28. Mulder N, Apweiler R. Interpro and interproscan, in Comparative genomics. Springer 2007;396:59-70.
  29. Hajian-Tilaki K. Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian J Intern Med 2013;4:627-635.
  30. Ikai A. Thermostability and aliphatic index of globular proteins. J Biochem 1980;88:1895-1898.
  31. Lebreton A, Moreau V, Lapalud P, Cayzac C, André S, Nguyen C, Schved JF, Lavigne G, Granier C. Discontinuous epitopes on the C2 domain of coagulation Factor VIII mapped by computer‐designed synthetic peptides. Br J Haematol 2011;155:487-497.
  32. Behbahani M, Mohabatkar H, Nosrati M. Discrimination of HIV-1 and HIV-2 reverse transcriptase proteins using Chou’s PseAAC. Iran J Sci Technol, Trans A: Science 2017;42: 1805-1811.
  33. Behbahani M, Mohabatkar H, Nosrati M. Analysis and comparison of lignin peroxidases between fungi and bacteria using three different modes of Chou’s general pseudo amino acid composition. J Theor Biol 2016;411:1-5.
  34. Gupta S, Kapoor P, Chaudhary K, Gautam A, Kumar R, Open Source Drug Discovery Consortium, Raghava GPS. In silico approach for predicting toxicity of peptides and proteins. PlOS One 2013;8:e73957.‏
  35. Pan Y, Wang S, Zhang Q, Lu Q, Su D, Zuo Y, Yang L. Analysis and prediction of animal toxins by various Chou's pseudo components and reduced amino acid compositions. J Theor Biol 2019;462:221-229.
  36. Amoozadeh M, Behbahani M, Mohabatkar H, Keyhanfar M. Analysis and comparison of alkaline and acid phosphatases of Gram-negative bacteria by bioinformatic and colorimetric methods. J Biotech 2020;308:56-62.
آمار
تعداد مشاهده مقاله: 1,152
تعداد دریافت فایل اصل مقاله: 1,414


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب