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THE PHYLOGENETIC RELATIONSHIPS OF LIPASE ENZYMES ACCORDING TO THEIR THERMAL STABILITY BY A COMPUTATIONAL APPROACH

Year 2018, Volume: 1 Issue: 1, 1 - 7, 30.06.2018

Çağın Kandemir Çavaş Türkan Arıt

Abstract

Molecular
Phylogenetic Analysis studies conducted to reveal the evolutionary
relationships of biological sequences, use two basic methodologies. In the
study of distance-based methodologies, phylogenetic trees were obtained by
clustering Lipase enzymes according to their thermal stability. The biological
sequences to be used in the study were obtained from the NCBI Genbank database.
Methods were coded in the Java programming language and distance matrices were
obtained.  Phylogenetic trees were
constructed using the R language. As a result, Lipase enzymes were effectively
clustered using a distance-based method without alignment, according to their
thermal stability.

Keywords

Molecular Phylogeny distance based methodology enzyme clustering

References

  • 1. Lesk AM. Introduction to bioinformatics. 2nd edition. New York: Oxford University Press; 2005.
  • 2. Nasibov E and Kandemir-Cavas C. OWA-based linkage method in hierarchical clustering: Application on phylogenetic trees. Expert Sys. Appl., 2011;38:12684-12690.
  • 3. NCBI Resource Coordinators. Database resources of the national center for biotechnology information. Nucleic Acids Research, 2016;4(44 Database issue):D7-D19.
  • 4. Apostolico A, Guerra C, Landau GM, and Pizzi C. Sequence similarity measures based on bounded hamming distance. Theor. Comput. Sci., 2016;638:76-90.
  • 5. Mount DM. Bioinformatics: Sequence and Genome Analysis. 2nd edition. New York: Cold Spring Harbor Laboratory Press; 2004.
  • 6. Ng PC and Henikoff S. Predicting deleterious amino acid substitutions, Genome Res., 2001;11(5):863-74.
  • 7. Albayrak A and Sezerman OU. Lempel-Ziv Complexity scores for clustering mesophilic and thermophilic Lipases. The International Enzyme Engineering Symposium 2008, 2008, Kuşadası, Aydın.
  • 8. Krane DE and Raymer ML. Fundamental concepts of bioinformatics. San Francisco, USA: Pearson Education; 2008.
  • 9. Clustal Omega [Document on the Internet]. 2017 [cited 2017 April]. Available from: http://www.ebi.ac.uk/Tools/msa/clustalo/
  • 10. Amiri S and Dinov D. Comparison of genomic data via statistical distribution. J. Theor. Biol., 2016;407:318-327.
  • 11. Mitra S and Acharya T. Data mining: multimedia, soft computing and bioinformatics. NJ: Wiley; 2003.
  • 12. Melsted P and Pritchard JK. Efficient counting of k-mers in DNA sequences using a bloom filter. BMC Bioinformatics, 2011;12:333.
  • 13. Hashim EKM and Abdullah R. Rare k-mer DNA: Identification of sequence motifs and prediction of CpG island and promoter. J. Theor. Biol., 2015;387:88-100.
  • 14. Fiannaca A, La Rosa M, Rizzo R, and Urso A. A k-mer-based barcode DNA classification methodology based on spectral representation and a neural gas network. Artif. Intell. Med., 2015;64:173-184.
  • 15. Wen J, Chan RH, Yau SC, He RL, and Yau SS. K-mer natural vector and its application to the phylogenetic analysis of genetic sequences, Gene, 2014;546(1):25-34.
  • 16. Al-Anzi FS and AbuZeina D. Toward an enhanced Arabic text classification using cosine similarity and Latent Semantic Indexing. Journal of King Saud University – Int. J. Comput. Inf. Sci., 2014;29:189-195.
  • 17. Royter M, Schmidt M, Elend C, Höbenreich H, Schäfer T, Bornscheuer UT, and Antranikian G. Thermostable lipases from the extreme thermophilic anaerobic bacteria Thermoanaerobacter thermohydrosulfuricus SOL1 and Caldanaerobacter subterraneus subsp. Tengcongensis, Extremophiles, 2009;13:769–83.
  • 18. Anaerobic Digestion-Mesophilic Vs. Thermophilic [Document on the Internet]. 2017 [Cited 2017 April]. Available from: https://www.theecoambassador.com/Anaerobic-Digestion-Temperature.html
  • 19. Arpigny JL and Jaeger KE. Bacterial lipolytic enzymes: classification and properties, Biochem. J., 1999;343:177-183.
  • 20. Weyenberg G and Yoshida R. Phylogenetic Tree Distances, Ref. Module Life Sci. Encyc. Evol. Biol., 2016;285-290.

Year 2018, Volume: 1 Issue: 1, 1 - 7, 30.06.2018

Çağın Kandemir Çavaş Türkan Arıt

Abstract

References

  • 1. Lesk AM. Introduction to bioinformatics. 2nd edition. New York: Oxford University Press; 2005.
  • 2. Nasibov E and Kandemir-Cavas C. OWA-based linkage method in hierarchical clustering: Application on phylogenetic trees. Expert Sys. Appl., 2011;38:12684-12690.
  • 3. NCBI Resource Coordinators. Database resources of the national center for biotechnology information. Nucleic Acids Research, 2016;4(44 Database issue):D7-D19.
  • 4. Apostolico A, Guerra C, Landau GM, and Pizzi C. Sequence similarity measures based on bounded hamming distance. Theor. Comput. Sci., 2016;638:76-90.
  • 5. Mount DM. Bioinformatics: Sequence and Genome Analysis. 2nd edition. New York: Cold Spring Harbor Laboratory Press; 2004.
  • 6. Ng PC and Henikoff S. Predicting deleterious amino acid substitutions, Genome Res., 2001;11(5):863-74.
  • 7. Albayrak A and Sezerman OU. Lempel-Ziv Complexity scores for clustering mesophilic and thermophilic Lipases. The International Enzyme Engineering Symposium 2008, 2008, Kuşadası, Aydın.
  • 8. Krane DE and Raymer ML. Fundamental concepts of bioinformatics. San Francisco, USA: Pearson Education; 2008.
  • 9. Clustal Omega [Document on the Internet]. 2017 [cited 2017 April]. Available from: http://www.ebi.ac.uk/Tools/msa/clustalo/
  • 10. Amiri S and Dinov D. Comparison of genomic data via statistical distribution. J. Theor. Biol., 2016;407:318-327.
  • 11. Mitra S and Acharya T. Data mining: multimedia, soft computing and bioinformatics. NJ: Wiley; 2003.
  • 12. Melsted P and Pritchard JK. Efficient counting of k-mers in DNA sequences using a bloom filter. BMC Bioinformatics, 2011;12:333.
  • 13. Hashim EKM and Abdullah R. Rare k-mer DNA: Identification of sequence motifs and prediction of CpG island and promoter. J. Theor. Biol., 2015;387:88-100.
  • 14. Fiannaca A, La Rosa M, Rizzo R, and Urso A. A k-mer-based barcode DNA classification methodology based on spectral representation and a neural gas network. Artif. Intell. Med., 2015;64:173-184.
  • 15. Wen J, Chan RH, Yau SC, He RL, and Yau SS. K-mer natural vector and its application to the phylogenetic analysis of genetic sequences, Gene, 2014;546(1):25-34.
  • 16. Al-Anzi FS and AbuZeina D. Toward an enhanced Arabic text classification using cosine similarity and Latent Semantic Indexing. Journal of King Saud University – Int. J. Comput. Inf. Sci., 2014;29:189-195.
  • 17. Royter M, Schmidt M, Elend C, Höbenreich H, Schäfer T, Bornscheuer UT, and Antranikian G. Thermostable lipases from the extreme thermophilic anaerobic bacteria Thermoanaerobacter thermohydrosulfuricus SOL1 and Caldanaerobacter subterraneus subsp. Tengcongensis, Extremophiles, 2009;13:769–83.
  • 18. Anaerobic Digestion-Mesophilic Vs. Thermophilic [Document on the Internet]. 2017 [Cited 2017 April]. Available from: https://www.theecoambassador.com/Anaerobic-Digestion-Temperature.html
  • 19. Arpigny JL and Jaeger KE. Bacterial lipolytic enzymes: classification and properties, Biochem. J., 1999;343:177-183.
  • 20. Weyenberg G and Yoshida R. Phylogenetic Tree Distances, Ref. Module Life Sci. Encyc. Evol. Biol., 2016;285-290.
There are 20 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Çağın Kandemir Çavaş

Türkan Arıt This is me

Publication Date June 30, 2018
Submission Date April 25, 2018
Acceptance Date May 22, 2018
Published in Issue Year 2018 Volume: 1 Issue: 1

Cite

APA Kandemir Çavaş, Ç., & Arıt, T. (2018). THE PHYLOGENETIC RELATIONSHIPS OF LIPASE ENZYMES ACCORDING TO THEIR THERMAL STABILITY BY A COMPUTATIONAL APPROACH. Usak University Journal of Engineering Sciences, 1(1), 1-7.
AMA Kandemir Çavaş Ç, Arıt T. THE PHYLOGENETIC RELATIONSHIPS OF LIPASE ENZYMES ACCORDING TO THEIR THERMAL STABILITY BY A COMPUTATIONAL APPROACH. UUJES. June 2018;1(1):1-7.
Chicago Kandemir Çavaş, Çağın, and Türkan Arıt. “THE PHYLOGENETIC RELATIONSHIPS OF LIPASE ENZYMES ACCORDING TO THEIR THERMAL STABILITY BY A COMPUTATIONAL APPROACH”. Usak University Journal of Engineering Sciences 1, no. 1 (June 2018): 1-7.
EndNote Kandemir Çavaş Ç, Arıt T (June 1, 2018) THE PHYLOGENETIC RELATIONSHIPS OF LIPASE ENZYMES ACCORDING TO THEIR THERMAL STABILITY BY A COMPUTATIONAL APPROACH. Usak University Journal of Engineering Sciences 1 1 1–7.
IEEE Ç. Kandemir Çavaş and T. Arıt, “THE PHYLOGENETIC RELATIONSHIPS OF LIPASE ENZYMES ACCORDING TO THEIR THERMAL STABILITY BY A COMPUTATIONAL APPROACH”, UUJES, vol. 1, no. 1, pp. 1–7, 2018.
ISNAD Kandemir Çavaş, Çağın - Arıt, Türkan. “THE PHYLOGENETIC RELATIONSHIPS OF LIPASE ENZYMES ACCORDING TO THEIR THERMAL STABILITY BY A COMPUTATIONAL APPROACH”. Usak University Journal of Engineering Sciences 1/1 (June 2018), 1-7.
JAMA Kandemir Çavaş Ç, Arıt T. THE PHYLOGENETIC RELATIONSHIPS OF LIPASE ENZYMES ACCORDING TO THEIR THERMAL STABILITY BY A COMPUTATIONAL APPROACH. UUJES. 2018;1:1–7.
MLA Kandemir Çavaş, Çağın and Türkan Arıt. “THE PHYLOGENETIC RELATIONSHIPS OF LIPASE ENZYMES ACCORDING TO THEIR THERMAL STABILITY BY A COMPUTATIONAL APPROACH”. Usak University Journal of Engineering Sciences, vol. 1, no. 1, 2018, pp. 1-7.
Vancouver Kandemir Çavaş Ç, Arıt T. THE PHYLOGENETIC RELATIONSHIPS OF LIPASE ENZYMES ACCORDING TO THEIR THERMAL STABILITY BY A COMPUTATIONAL APPROACH. UUJES. 2018;1(1):1-7.

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