In Silico Modeling and Validation of L-Arginase, an Anti-Cancer Enzyme using Computational Tools

Download Full-Text PDF Cite this Publication

Text Only Version

In Silico Modeling and Validation of L-Arginase, an Anti-Cancer Enzyme using Computational Tools

In Silico Modeling and Validation of L-Arginase, an Anti-Cancer Enzyme using Computational Tools

Praveen Reddy. P

Department of Microbiology,

Vivekananda Degree and PG College, Karimnagar-505001, Telangana, India

Abstract – The microbial L-Arginase is a potent drug which can be used for the treatment of various forms of cancer and other severe complications. In the present work L-Arginase sequence of a bacterium was obtained from UniProt server and it is analyzed in SWISS MODEL to develop its 3-D model. The L- Arginase enzyme model was verified and validated in ProSA web server. Further the efficiency of L-Arginase can be enhanced by using sophisticated computational tools and such L-Arginase with enhanced activity can be produced by microbes in the laboratory.

Key words L-Arginase; drug; cancer; UniProt; SWISS MODEL; ProSA

  1. INTRODUCTION

    The L-Arginase enzyme produced by microbes can be used as a multiple drug. It is used as a potent drug to treat different cancers. It is also used to cure rheumatoid arthritis, allergy associated asthma and neurological complications. In humans L-Arginase occurs in two forms viz., L-Arginase-I and L-Arginase-II. In vitro studies had proved L-Arginase as a genuine therapeutic agent to cure different forms of cancer. The L-Arginine is an important amino acid required for living cells. It is necessary for the synthesis of nucleic acids and proteins. Healthy cells are capable of synthesizing L- Arginine whereas cancer cells lack the ability to synthesize L-Arginine. The cancer cells depend upon dietary L- Arginine which circulates in blood after digestion and absorption of food. If L-Arginase is administered into a cancer patient it degrades L-Arginine present in blood into L-ornithine and urea. Thus, L-Arginine is not available to cancer cells and eventually they die. The normal cells are unaffected as they can synthesize L-Arginine. Many microbes have been reported to produce L-Arginase by researchers. The microbial L-Arginase can be easily produced by microbes and is a stable compound [1, 2].

    The researchers from different areas of sciences have integrated their ideas to develop the bioinformatics tools. The bioinformatics tools are extensively used to design and improve the biomolecules. The tools are available online and can be freely used. Various online servers can be employed for In Silico modeling and improvement of protein models [3].

    In the present paper the sequence of L-Arginase enzyme was downloaded from UniProt server and used in SWISS

    MODEL to build three-dimensional model of L-Arginase. Then the model quality of L-Arginase structure was validated in ProSA server.

  2. MATERIALS AND METHODS

    1. Retrieving the sequence of L-Arginase enzyme

      The sequence of L-Arginase enzyme was obtained from UniProt server. The UniProt server is a source of protein data collected from different protein databases [4].

    2. Construction of three-dimensional model of L-Arginase enzyme

      The L-Arginase enzyme sequence was used to build the corresponding 3-D model of L-Arginase in SWISS MODEL. The SWISS MODEL is an online web server in which protein models can be developed based on their amino acid sequences [5].

    3. Determination of validity of L-Arginase enzyme model structure

    The quality of model of L-Arginase was checked in ProSA web sever. The PDB file of L-Arginase was submitted to ProSA in which protein models are verified based on structural details generated by X-ray and NMR spectroscopy [6].

  3. RESULTS AND DISCUSSION

    1. Sequence of L-Arginase enzyme obtained from Uniprot server

      The sequence of L-Arginase (L-Arginase-I) enzyme of Flammeovirgaceae bacterium 311 in FASTA format was derived from UniProt server. The following is the sequence of L-Arginase.

      MERIKFIEVASELGAGTRGASLGIGALKAASLAKGSDF FKKYPCLAVPTLNEQLFEDILHPYAKRIPQVRQILEHT AGAVKNTLAEGCFPVVLAGDHSTAAGTIAGIKMQMP HKRIGVIWIDAHADLHTPYTTPSGNVHGMPLSVVTGI DNKECQVNYPQTETVEEWNKCKNIGVEGAKIDPSDIV FIGMRSFEEPEKAIINRHGIRNFSVEEVRTKGVAAVVG EIMEVLNNCDAIYISFDVDSLDPEISTGTGTPVPQGLTA EEGRALNHSLIMQPKVVCWEMVEINPTLDNLNKMAV TAFEIMEHAINARASNGKLVDVAL

      The UniProt server allows the users to obtain the desired protein sequences freely [7].

    2. Generation of three-dimensional model of L-Arginase in SWISS MODEL

      The FASTA format sequence of L-Arginase was entered in SWISS MODEL to develop its structure by automated mode. The sequence of L-Arginase (query sequence) was matched with similar protein sequences available in SWISS MODEL. The protein sequence which was exhibiting maximum similarity to the L-Arginase sequence was regarded as

      template protein [8]. The protein, 4ie1.1.A showed highest similarity to L-Arginase. The alignment of sequence of protein, 4ie1.1.A and L-Arginase sequence is shown in figure-1. The protein 4ie1.1.A was used as template model (Figure-2) to develop L-Arginase model (Figure-3).

      The L-Arginase enzyme exists as trimer made up of three identical monomer units [9]. The three monomer units of L- Arginase were matched with one of the three identical units of template, 4ie1.1.A.

      Figure-1: Alignment of template, 4ie1.1.A sequence with the sequences of L-Arginase enzyme (trimer)

      Figure-2: Template (4ie1.1.A) model Figure-3: L-Arginase enzyme model (trimer)

    3. Validation of L-Arginase enzyme model in ProSA

    The 3-D model of L-Arginase was verified and validated in ProSA. The PDB format of L-Arginase was submitted to ProSA server. The ProSA server generates an overall quality score denoted as Z-score for a submitted protein model. The Z-score (black dot) of the polypeptide chains of L-Arginase enzyme calculated based on X-ray (light blue) and NMR spectrum (dark blue) analysis are depicted in Figure-4. The

    1. score (-8.31) calculated in ProSA web for L-Arginase enzyme model was not deviating significantly from similar pre-determined native protein models which inferred the reliability of L-Arginase model [10-12].

      Figure-4: Z-score of L-Arginase model generated in ProSA web

  4. CONCLUSION

The bioinformatics tools were extensively used to construct and validate the 3-D model of bacterial L-Arginase enzyme which is an important multi therapeutic drug. The activity of L-Arginase model can be improved by modifying amino acids at certain sites using advanced tools of computational biology. Such improved L-Arginase can be produced commercially on large scale by making required modifications in L-Arginase gene of microbes.

REFERENCES

    1. G. Kaur, N. Verma and D.N. Wheatley, Production and over- expression arginase with enhanced enzyme activity by an efficient recombinant Escherichia coli system, Romanian Biotechnological Letters, vol. 14, pp. 4333-4341, 2009.

    2. P.D. Nadaf, A.G. Kulkarni and A.B. Vedamurthy, Isolation, Screening and Characterization of L-Arginase Producing Soil Bacteria, International Journal of Pharmaceutical Sciences and Research, vol. 10, pp. 3440-3444, July 2019.

    3. D. Xu, Y. Xu and E.C. Uberbacher, Computational Tools for Protein Modeling, Current Protein and Peptide Science, vol. pp. 1- 21, 2000.

    4. R. Apweiler, A. Bairoch, C.H. Wu, W.C. Barker, B. Boeckmann, S. Ferro, E. Gasteiger, H. Huang, R. Lopez, M. Magrane, M.J. Martin,

      D.A. Natale, C. ODonovan, N. Redaschi and L.S.L. Yeh, UniProt: the Universal Protein knowledgebase, Nucleic Acids Research, vol. 32, pp. D115-D19, 2004.

    5. T. Schwede, J. Kopp, N. Guex and M.C. Peitsch, SWISS- MODEL: an automated protein homology-modeling server, Nucleic Acids Research, vol. 31, pp. 3381-3385, 2003.

    6. M. Wiederstein and M.J. Sippl, ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins, Nucleic Acids Research, vol. 35, pp. W407-W410, 2007.

    7. A. Bairoch, R. Apweiler, C.H, Wu, W.C. Barker, B. Boeckmann, S. Ferro, E. Gasteiger, H. Huang, R. Lopez, M. Magrane, M.J. Martin,

      D.A. Natale, C. ODonovan, N. Redaschi and L.S. Yeh, The Universal Protein Resource (UniProt), Nucleic Acids Research, vol. 33, pp. D153-D159, 2005.

    8. A. Waterhouse, M. Bertoni, S. Bienert, G. Studer, G. Tauriello, R. Gumienny, F.T. Heer, T.A.P. de Beer, C. Rempfer, L. Bordoli, R. Lepore and T. Scwede, SWISS-MODEL: homology modeling of protein structures and complexes, Nucleic Acids Research, vol. 46, pp. W296-W303, 2018.

    9. D.P. Dowling, L.D. Costanzo, H.A. Gennadios and D.W. Christianson, Evolution of the arginase fold and functional diversity, Cell Mol Life Sci, vol. 65, pp. 2039-2055, 2008.

    10. D. Droppa-Almeida, E. Franceschi and F.F. Padilha, Immuno- Informatic Analysis and Design of Peptide Vaccine From Multi- epitopes Against Corynebacterium pseudotuberculosis, Bioinformatics and Biology Insights, vol. 12, pp. 1-9, 2018.

    11. H. Rasouli and B. Fazeli-Nasab, Structural Validation and Homology Modeling of Lea 2 Protein in Bread Wheat, American- Eurasian J. Agric. & Environ. Sci, vol. 10, pp. 1044-1048, 2014.

    12. A. Elengoe, M.A. Naser and S. Hamdan, Modeling and Docking Studies on Novel Mutants (K71L and T204V) of the ATPase Domain of Human Heat Shock 70 kDa Protein 1, Int. J. Mol. Sci., vol. 15, pp. 6797-6814, 2014.

Leave a Reply

Your email address will not be published. Required fields are marked *