Journal of Molecular Modeling

, 25:317 | Cite as

Molecular simulation of PcCel45A protein expressed from Aspergillus nidulans to understand its structure, dynamics, and thermostability

  • Mehmet Altay UnalEmail author
  • Bahadir Boyacioglu
  • Huseyin Unver
  • Ayhan Elmali
Original Paper


PACS and mathematical subject classification numbers as needed. Molecular dynamic simulation is a very usable tool to understand various factors, including structure temperature dependence, dynamics, and stability for protein structure. The three main components, namely endoglucanase, exoglucanase, and β-glucosidase, effectively convert lignocellulosic biomass into fermentable sugar. Cellulose is the major component of plant cell walls and is the most abundant organic compound on the earth. Somewhat organisms can use cellulose as a food source, possessing cellulases (cellobiohydrolases and endoglucanases) that can catalyze the hydrolysis of the β-(1,4) glycosidic bonds. In this work, we investigated conformational and structural properties of PcCel45A protein by changing at temperatures with 300 K, 333 K, and 352 K. We found that the ASN92 residue was the major contributor to the stability of structure; some other residues correlated significantly with thermal stability. We also compared the theoretical results of the current study with the experimental ones published in previous studies.


Molecular dynamics PCcel45A 



  1. 1.
    Rosegrant MW, Zhu T, Msangi S, Sulser T (2008) Global scenarios for biofuels: impacts and implications. Rev Agri Econ 30:495–505CrossRefGoogle Scholar
  2. 2.
    EUR-Lex - 32009L0028 - EN - EUR-Lex. Accessed 24 Jul 2019
  3. 3.
    Global biofuel production 2018. In: Statista. Accessed 24 Jul 2019
  4. 4.
    Biodiesel production United States 2018. In: Statista.
  5. 5.
    Srivastava N, Srivastava M, Mishra PK, et al. (2015) Application of cellulases in biofuels industries: an overview. J Biof nd Bioen 1:55. CrossRefGoogle Scholar
  6. 6.
    Igarashi K, Ishida T, Hori C, Samejima M (2008) Characterization of an endoglucanase belonging to a new subfamily of glycoside hydrolase family 45 of the basidiomycete phanerochaete chrysosporium. Appl Environ Microbiol 74:5628–5634. CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Kricka W, Fitzpatrick J, Bond U (2015) Challenges for the production of bioethanol from biomass using recombinant yeasts. In: Advances in applied microbiology. Elsevier, pp 89–125Google Scholar
  8. 8.
    Eriksson K-EL, Blanchette RA, Ander P (1990) Biodegradation of cellulose. In: Microbial and enzymatic degradation of wood and wood components. Springer, BerlinCrossRefGoogle Scholar
  9. 9.
    Clarke AJ (1997) Biodegradation of cellulose: enzymology and biotechnology. Technomic Pub Co, LancasterGoogle Scholar
  10. 10.
    Rojas OJ, Jeong C, Turon X (2007) American chemical measurement of cellulase activity with piezoelectric resonators. In: Argyropoulos DS (ed) Materials, chemicals, and energy from forest biomass. American Chemical Society, Washington, DC, pp 478–494Google Scholar
  11. 11.
    Bhat MK, Bhat S (1997) Cellulose degrading enzymes and their potential industrial applications. Biotechnol Adv 15:583–620. CrossRefPubMedGoogle Scholar
  12. 12.
    Shewmaker CK, Stalker DM (1992) Modifying starch biosynthesis with transgenes in potatoes. Plant Physiol 100:1083–1086. CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Müller-Röber B, Sonnewald U, Willmitzer L (1992) Inhibition of the ADP-glucose pyrophosphorylase in transgenic potatoes leads to sugar-storing tubers and influences tuber formation and expression of tuber storage protein genes. EMBO J 11:1229–1238CrossRefGoogle Scholar
  14. 14.
    Oakes JV, Shewmaker CK, Stalker DM (1991) Production of cyclodextrins, a novel carbohydrate, in the tubers of transgenic potato plants. Biotechnology (NY) 9:982–986CrossRefGoogle Scholar
  15. 15.
    Vieille C, Zeikus GJ (2001) Hyperthermophilic enzymes: sources, uses, and molecular mechanisms for thermostability. Microbiol Mol Biol Rev 65:1–43. CrossRefGoogle Scholar
  16. 16.
    Sauer DB, Karpowich NK, Song JM, Wang D -N (2015) Rapid bioinformatic identification of thermostabilizing mutations. Biophys J 109:1420–1428CrossRefGoogle Scholar
  17. 17.
    Pucci F, Rooman M (2017) Physical and molecular bases of protein thermal stability and cold adaptation. Curr Opin Struct Biol 42:117–128CrossRefGoogle Scholar
  18. 18.
    Yennamalli RM, Rader AJ, Kenny AJ, et al. (2013) Endoglucanases: insights into thermostability for biofuel applications. Biotechnol Biofuels 6:136CrossRefGoogle Scholar
  19. 19.
    Karlsson J, Siika-aho M, Tenkanen M, Tjerneld F (2002) Enzymatic properties of the low molecular mass endoglucanases Cel12A (EG III) and Cel45A (EG V) of Trichoderma reesei. J Biotechnol 99:63–78CrossRefGoogle Scholar
  20. 20.
    Karlsson J, Siika-aho M, Tenkanen M, Tjerneld F (2002) Enzymatic properties of the low molecular mass endoglucanases Cel12A (EG III) and Cel45A (EG V) of Trichoderma reesei. J Biotechnol 99:63–78. CrossRefGoogle Scholar
  21. 21.
    Godoy AS, Ramia MP, Camilo CM, Polikarpov I X-ray structure of PcCel45A expressed in Aspergillus nidullans. To be published.
  22. 22.
    Jorgensen WL, Maxwell DS, Tirado-Rives J (1996) Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J Am Chem Soc 118:11225–11236. CrossRefGoogle Scholar
  23. 23.
    Kaminski GA, Friesner RA, Tirado-Rives J, Jorgensen WL (2001) Evaluation and reparametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides. J Phys Chem B 105:6474–6487. CrossRefGoogle Scholar
  24. 24.
    Abraham MJ, Murtola T, Schulz R, et al. (2015) GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2:19–25. CrossRefGoogle Scholar
  25. 25.
    Páll S, Abraham MJ, Kutzner C, et al. (2015) Tackling exascale software challenges in molecular dynamics simulations with GROMACS. In: Markidis S, Laure E (eds) Solving software challenges for exascale. Springer International Publishing, ChamGoogle Scholar
  26. 26.
    Pronk S, Páll S, Schulz R, et al. (2013) GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29:845–854. CrossRefGoogle Scholar
  27. 27.
    Hess B, Kutzner C, van der Spoel D, Lindahl E (2008) GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 4:435–447. CrossRefGoogle Scholar
  28. 28.
    Van Der Spoel D, Lindahl E, Hess B, et al. (2005) GROMACS: fast, flexible, and free. J Comput Chem 26:1701–1718. CrossRefPubMedGoogle Scholar
  29. 29.
    Lindahl E, Hess B, van der Spoel D (2001) GROMACS 3.0: a package for molecular simulation and trajectory analysis. J Mol Model 7:306–317CrossRefGoogle Scholar
  30. 30.
    Berendsen HJC, van der Spoel D, van Drunen R (1995) GROMACS: a message-passing parallel molecular dynamics implementation. Comput Phys Commun 91:43–56. CrossRefGoogle Scholar
  31. 31.
    Jorgensen WL, Chandrasekhar J, Madura JD, et al. (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926–935. CrossRefGoogle Scholar
  32. 32.
    Hess B, Bekker H, Berendsen HJC, Fraaije JGEM (1997) LINCS: a linear constraint solver for molecular simulations. J Comput Chem 18:1463–1472.<1463::AID-JCC4>3.0.CO;2-H CrossRefGoogle Scholar
  33. 33.
    Bussi G, Donadio D, Parrinello M (2007) Canonical sampling through velocity rescaling. J Chem Phys 126:014101. CrossRefGoogle Scholar
  34. 34.
    Darden T, York D, Pedersen L (1993) Particle mesh Ewald: an N log(N ) method for Ewald sums in large systems. J Chem Phys 98:10089–10092. CrossRefGoogle Scholar
  35. 35.
    Pettersen EF, Goddard TD, Huang CC, et al. (2004) UCSF Chimera? A visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612. CrossRefGoogle Scholar
  36. 36.
    Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14:33–38. CrossRefGoogle Scholar
  37. 37.
    Peng X-N, Wang J, Zhang W (2017) Molecular dynamics simulation analysis of the effect of T790M mutation on epidermal growth factor receptor protein architecture in non-small cell lung carcinoma. Oncol Lett 14:2249–2253. CrossRefGoogle Scholar
  38. 38.
    Nishiyama K (2008) Thermal behavior of luciferase on nanofabricated hydrophilic Si surface. Biomacromolecules 9:1081–1083. CrossRefPubMedGoogle Scholar
  39. 39.
    Martinez R, Schwaneberg U, Roccatano D (2011) Temperature effects on structure and dynamics of the psychrophilic protease subtilisin S41 and its thermostable mutants in solution. Protein Eng Des Selection 24:533–544CrossRefGoogle Scholar
  40. 40.
    Nakamura A, Ishida T, Kusaka K et al (2015) Newton’s cradle proton relay with amide–imidic acid tautomerization in inverting cellulase visualized by neutron crystallography. Sci Adv 1:e1500263. CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Godoy AS, Pereira CS, Ramia MP, et al. (2018) Structure, computational and biochemical analysis of PcCel45A endoglucanase from Phanerochaete chrysosporium and catalytic mechanisms of GH45 subfamily C members. Sci Rep 8:3678CrossRefGoogle Scholar
  42. 42.
    Szijártó N, Siika-aho M, Tenkanen M, et al. (2008) Hydrolysis of amorphous and crystalline cellulose by heterologously produced cellulases of Melanocarpus albomyces. J Biotechnol 136:140–147. CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Stem Cell InstituteAnkara UniversityCevizlidere-AnkaraTurkey
  2. 2.Vocational School of Health ServicesAnkara UniversityKecioren-AnkaraTurkey
  3. 3.Faculty of Sciences, Department of PhysicsAnkara UniversityBesevler AnkaraTurkey
  4. 4.Department of Physics Engineering, Faculty of EngineeringAnkara UniversityBesevler AnkaraTurkey

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