Skip to main content
Log in

Comparative modelling studies of fruit bromelain using molecular dynamics simulation

  • Original Paper
  • Published:
Journal of Molecular Modeling Aims and scope Submit manuscript

Abstract

Fruit bromelain is a cysteine protease accumulated in pineapple fruits. This proteolytic enzyme has received high demand for industrial and therapeutic applications. In this study, fruit bromelain sequences QIM61759, QIM61760 and QIM61761 were retrieved from the National Center for Biotechnology Information (NCBI) Genbank Database. The tertiary structure of fruit bromelain QIM61759, QIM61760 and QIM61761 was generated by using MODELLER. The result revealed that the local stereochemical quality of the generated models was improved by using multiple templates during modelling process. Moreover, by comparing with the available papain model, structural analysis provides an insight on how pro-peptide functions as a scaffold in fruit bromelain folding and contributing to inactivation of mature protein. The structural analysis also disclosed the similarities and differences between these models. Lastly, thermal stability of fruit bromelain was studied. Molecular dynamics simulation of fruit bromelain structures at several selected temperatures demonstrated how fruit bromelain responds to elevation of temperature.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Abbreviations

Dope:

Discrete optimized protein energy

GROMACS:

GROningen MAchine for Chemical Simulations

LINCS:

Linear Constraint Solver

MD:

Molecular dynamics

NCBI:

National Center for Biotechnology Information

NPT:

Constant number of particles, pressure and temperature

NVT:

Constant number of particles, volume and temperature

PDB ID:

Protein Data Bank identification

PME:

Particle mesh Ewald method

Rg:

Radius of gyration

RMSD:

Root-mean-square deviations.

RMSF:

Root-mean-square fluctuations

SASA:

Solvent accessible surface area

References

  1. Jisha VN, Smitha RB, Pradeep S et al (2013) Versatility of microbial proteases. Adv Enzym Res 1(3):39–51. https://doi.org/10.4236/aer.2013.13005

    Article  CAS  Google Scholar 

  2. Mahajan RT, Badgujar SB (2010) Biological aspects of proteolytic enzymes : a review. J Pharm Res 3(9):2048–2068

    Google Scholar 

  3. Chew LY, Toh GT, Ismail A (2018) Application of proteases for the production of bioactive peptides. In: Kuddus M (ed) Enzymes in food biotechnology. Elsevier, London, pp 247–261

    Google Scholar 

  4. Kwon CW, Park KM, Kang BC et al (2015) Cysteine protease profiles of the medicinal plant Calotropis procera R. Br. revealed by de Novo transcriptome analysis. PLoS One 10(3):1–15. https://doi.org/10.1371/journal.pone.0119328

    Article  CAS  Google Scholar 

  5. Lin E, Burns DJW, Gardner RC (1993) Fruit developmental regulation of the kiwifruit actinidin promoter is conserved in transgenic petunia plants. Plant Mol Biol 23(3):489–499. https://doi.org/10.1007/BF00019297

    Article  CAS  PubMed  Google Scholar 

  6. Ramalingam C, Srinath R, Islam NN (2012) Isolation and characterization of bromelain from pineapple (Ananas comosus) and comparing its anti-browning activity on apple juice with commercial anti- browning agents. Elixir Food Sci 45:7822–7826

    Google Scholar 

  7. Bresolin IRAP, Bresolin ITL, Silveira E, Tambourgi EB, Mazzola PG (2013) Isolation and purification of bromelain from waste peel of pineapple for therapeutic application. Braz Arch Biol Technol 56(6):971–979. https://doi.org/10.1590/S1516-89132013000600012

    Article  CAS  Google Scholar 

  8. da Silva LR (2017) Debridement applications of bromelain: a complex of cysteine proteases from pineapple. Adv Biotechnol Microbiol 3(5):6109–6111. https://doi.org/10.19080/AIBM.2017.03.555624

    Article  Google Scholar 

  9. Maurer HR (2001) Bromelain: biochemistry, pharmacology and medical use. Cell Mol Life Sci 58(9):1234–1245. https://doi.org/10.1007/PL00000936

    Article  CAS  PubMed  Google Scholar 

  10. Rathnavelu V, Alitheen N, Sohila S et al (2016) Potential role of bromelain in clinical and therapeutic applications (review). Biomed Rep 5(3):283–288

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Muhammad ZA, Ahmad T (2017) Therapeutic uses of pineapple-extracted bromelain in surgical care — a review. J Pak Med Assoc 67(1):121–125

    PubMed  Google Scholar 

  12. Kelly G (1996) Bromelain: a literature review and discussion of its therapeutic applications. Altern Med Rev 11(44):243–257

    Google Scholar 

  13. Manzoor Z, Nawaz A, Mukhtar H, Haq I (2016) Bromelain: methods of extraction, purification and therapeutic applications. Braz Arch Biol Technol 59:1–16. https://doi.org/10.1590/1678-4324-2016150010

    Article  CAS  Google Scholar 

  14. Wali N (2019) Pineapple (Ananas comosus). In: Nabavi S, Silva A (eds) Nonvitamin and nonmineral nutritional supplements. Elsevier, London, pp 367–373

    Chapter  Google Scholar 

  15. Bhattacharyya BK (2008) Bromelain: an overview. Nat Prod Radiance 7(4):359–363

    Google Scholar 

  16. Pavan R, Jain S, Shraddha KA (2012) Properties and therapeutic application of bromelain: a review. Biotechnol Res Int 2012:1–6

    Article  CAS  Google Scholar 

  17. Manohar J, Gayathri R, Vishnupriya V (2016) Tenderisation of meat using bromelain from pineapple extract. Int J Pharm Sci Rev Res 39(1):81–85

    Google Scholar 

  18. Han J, Cai Y, Xie X et al (2018) A simple method for purification of bromelain in a thermosensitive triblock copolymer-based protection system and recycling of phase components. Sep Sci Technol 53(4):636–644. https://doi.org/10.1080/01496395.2017.1398757

    Article  CAS  Google Scholar 

  19. Heredia-Sandoval NG, Valencia-Tapia MY, de la Barca AMC, Islas-Rubio AR (2016) Microbial proteases in baked goods: modification of gluten and effects on immunogenicity and product quality. Foods 5(3):1–10. https://doi.org/10.3390/foods5030059

    Article  CAS  Google Scholar 

  20. Nair IC, Jayachandran K (2019) Aspartic proteases in food industry. In: Parameswaran B, Varjani S, Raveendran S (eds) Green bio-processes: enzymes in industrial food processing. Springer, Singapore, pp 15–30

    Chapter  Google Scholar 

  21. Ismail B, Mohammed H, Nair AJ (2019) Influence of proteases on functional properties of food. In: Parameswaran B, Varjani S, Raveendran S (eds) Green bio-processes: enzymes in industrial food processing. Springer, Singapore, pp 31–53

    Chapter  Google Scholar 

  22. Mihasan M (2010) Basic protein structure prediction for the biologist: a review. Arch Biol Sci 62(4):857–871. https://doi.org/10.2298/ABS1004857M

    Article  Google Scholar 

  23. Pavlopoulou A, Michalopoulos I (2011) State-of-the-art bioinformatics protein structure prediction tools (review). Int J Mol Med 289(3):295–310. https://doi.org/10.3892/ijmm.2011.705

    Article  CAS  Google Scholar 

  24. Ganugapati J, Akash S (2017) Multi-template homology based structure prediction and molecular docking studies of protein ‘L’ of Zaire ebolavirus (EBOV). Inform Med Unlocked 9:68–75. https://doi.org/10.1016/j.imu.2017.06.002

    Article  Google Scholar 

  25. Chakravarty S, Godbole S, Zhang B, Berger S, Sanchez R (2008) Accuracy of comparative models of protein structure. BMC Struct Biol 8(31):1–13. https://doi.org/10.1186/1472-6807-8-31

    Article  CAS  Google Scholar 

  26. Ramli ANM, Manas NHA, Hamid AAA, Hamid HA, Illias RM (2018) Comparative structural analysis of fruit and stem bromelain from Ananas comosus. Food Chem 266:183–191. https://doi.org/10.1016/j.foodchem.2018.05.125

    Article  CAS  PubMed  Google Scholar 

  27. Sali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234(3):779–815. https://doi.org/10.1006/jmbi.1993.1626

    Article  CAS  PubMed  Google Scholar 

  28. Heo L, Feig M (2018) PREFMD: a web server for protein structure refinement via molecular dynamics simulations. Bioinformatics 34(6):1063–1065. https://doi.org/10.1093/bioinformatics/btx726

    Article  CAS  PubMed  Google Scholar 

  29. 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. https://doi.org/10.1016/j.softx.2015.06.001

    Article  Google Scholar 

  30. Feig M (2016) Local rotein structure refinement via molecular dynamics simulations with locPREFMD. J Chem Inf Model 56(7):1304–1312. https://doi.org/10.1021/acs.jcim.6b00222

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Huang J, Rauscher S, Nawrocki G et al (2016) CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat Methods 14(1):71–73. https://doi.org/10.1038/nmeth.4067

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Bowie JU, Ltcy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional structure. Science 253(58):164–170. https://doi.org/10.1126/science.1853201

    Article  CAS  PubMed  Google Scholar 

  33. Colovos C, Yeates TO (1993) Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 2(9):1511–1519. https://doi.org/10.1002/pro.5560020916

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK - a program to check the stereochemical quality of protein structures. J Appl Crystallogr 26:283–291

    Article  CAS  Google Scholar 

  35. Guex N, Peitsch MC (1997) SWISS-MODEL and the Swiss-PdbViewer: an environment for comparative protein modeling. Electrophoresis 18(15):2714–2723. https://doi.org/10.1002/elps.1150181505

    Article  CAS  PubMed  Google Scholar 

  36. Al-Sa’ady A, Al-Hadban W, Al-Zubaidy M (2016) Optimal conditions for bromelain extraction from pineapple fruit (Ananas comosus). Eng Technol J 34(5):675–682

    Google Scholar 

  37. Cupp-enyard C, Sigma-Aldrich (2008) Sigma’s non-specific protease activity assay-casein as a substrate. J Vis Exp 19:1–2

    Google Scholar 

  38. Dorn M, Silva MBE, Buriol LS, Lamb LC (2014) Three-dimensional protein structure prediction: methods and computational strategies. Comput Biol Chem 53:251–276. https://doi.org/10.1016/j.compbiolchem.2014.10.001

    Article  CAS  Google Scholar 

  39. Meier A, Söding J (2015) Automatic prediction of protein 3D structures by probabilistic multi-template homology modeling. PLoS Comput Biol 11(10):1–20. https://doi.org/10.1371/journal.pcbi.1004343

    Article  CAS  Google Scholar 

  40. Li J, Cheng J (2016) A stochastic point cloud sampling method for multi-template protein comparative modeling. Sci Rep 6:1–16. https://doi.org/10.1038/srep25687

    Article  CAS  Google Scholar 

  41. Ishitani R, Terada T, Shimizu K (2008) Refinement of comparative models of protein structure by using multicanonical molecular dynamics simulations. Mol Simul 34(3):327–336. https://doi.org/10.1080/08927020801930539

    Article  CAS  Google Scholar 

  42. Park H, Ovchinnikov S, Kim DE, DiMaio F, Baker D (2018) Protein homology model refinement by large-scale energy optimization. Proc Natl Acad Sci 115(12):3054–3059. https://doi.org/10.1073/pnas.1719115115

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Feig M (2017) Computational protein structure refinement: almost there, yet still so far to go. Wiley Interdiscip Rev Comput Mol Sci 7(3):1–16. https://doi.org/10.1002/wcms.1307

    Article  CAS  Google Scholar 

  44. Heo L, Feig M (2018) Experimental accuracy in protein structure refinement via molecular dynamics simulations. Proc Natl Acad Sci 115(52):13276–13281. https://doi.org/10.1073/pnas.1811364115

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Raval A, Piana S, Eastwood MP, Dror RO, Shaw DE (2012) Refinement of protein structure homology models via long, all-atom molecular dynamics simulations. Proteins 80(8):2071–2079. https://doi.org/10.1002/prot.24098

    Article  CAS  PubMed  Google Scholar 

  46. Lobanov MY, Bogatyreva NS, Galzitskaya OV (2008) Radius of gyration as an indicator of protein structure compactness. Mol Biol 42(4):623–628. https://doi.org/10.1134/S0026893308040195

    Article  CAS  Google Scholar 

  47. Dinner AR, Sali A, Smith LJ, Dobson CM, Karplus M (2000) Understanding protein folding via free-energy surfaces from theory and experiment. Trends Biochem Sci 25(7):331–339. https://doi.org/10.1016/S0968-0004(00)01610-8

    Article  CAS  PubMed  Google Scholar 

  48. Tsai J, Bonneau R, Av M, Kuhlman B, Rohl CA, Baker D (2003) An improved protein decoy set for testing energy functions for protein structure prediction. Proteins Struct Funct Genet 53(1):76–87. https://doi.org/10.1002/prot.10454

    Article  CAS  PubMed  Google Scholar 

  49. Dong R, Pan S, Peng Z, Zhang Y, Yang J (2018) mTM-align : a server for fast protein structure database search and multiple protein structure alignment. Nucleic Acids Res 46:380–386. https://doi.org/10.1093/nar/gky430

    Article  CAS  Google Scholar 

  50. Haimov B, Srebnik S (2016) A closer look into the α-helix basin. Sci Rep 6:1–12. https://doi.org/10.1038/srep38341

    Article  CAS  Google Scholar 

  51. Pace CN, Scholtz JM (1998) A helix propensity scale based on experimental studies of peptides and proteins. Biophys J 75(1):422–427. https://doi.org/10.1016/s0006-3495(98)77529-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Merkel JS, Strutevant JM, Regan L (1999) Sidechain interactions in parallel β sheets: the energetics of cross-strand pairings. Structure 7(11):1333–1343. https://doi.org/10.1016/S0969-2126(00)80023-4

    Article  CAS  PubMed  Google Scholar 

  53. Groves MR, Taylor MAJ, Scott M, Cummings NJ, Pickersgill RW, Jenkins JA (1996) The prosequence of procaricain forms an α-helical domain that prevents access to the substrate-binding cleft. Structure 4(10):1193–1203. https://doi.org/10.1016/S0969-2126(96)00127-X

    Article  CAS  PubMed  Google Scholar 

  54. Roy S, Choudhury D, Aich P, Dattagupta JK, Biswas S (2012) The structure of a thermostable mutant of pro-papain reveals its activation mechanism. Acta Crystallogr Sect D Biol Crystallogr 68(12):1591–1603. https://doi.org/10.1107/s0907444912038607

    Article  CAS  Google Scholar 

  55. Turk V, Stoka V, Vasiljeva O et al (2012) Cysteine cathepsins: from structure, function and regulation to new frontiers. Biochim Biophys Acta Proteins Proteomics 1824(1):68–88. https://doi.org/10.1016/j.bbapap.2011.10.002

    Article  CAS  Google Scholar 

  56. Amri E, Mamboya F (2012) Papain, a plant enzyme of biological importance: a review. Am J Biochem Biotechnol 8(2):99–104. https://doi.org/10.3844/ajbbsp.2012.99.104

    Article  CAS  Google Scholar 

  57. Menard R, Carriere J, Laflamme P et al (1991) Contribution of the glutamine 19 side chain to transition-state stabilization in the oxyanion hole of papain. Biochemistry 30(37):8924–8928

    Article  CAS  PubMed  Google Scholar 

  58. Coulombe R, Grochulski P, Sivaraman J, Ménard R, Mort JS, Cygler M (1996) Structure of human procathepsin L reveals the molecular basis of inhibition by the prosegment. EMBO J 15(20):5492–5503. https://doi.org/10.1002/j.1460-2075.1996.tb00934.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Sokalingam S, Raghunathan G, Soundrarajan N, Lee SG (2012) A study on the effect of surface lysine to arginine mutagenesis on protein stability and structure using green fluorescent protein. PLoS One 7(7):1–12. https://doi.org/10.1371/journal.pone.0040410

    Article  CAS  Google Scholar 

  60. Zou Z, Huang Q, Xie G, Yang L (2018) Genome-wide comparative analysis of papain-like cysteine protease family genes in castor bean and physic nut. Sci Rep 8(1):1–13. https://doi.org/10.1038/s41598-017-18760-6

    Article  CAS  Google Scholar 

  61. Butts CT, Zhang X, Kelly JE et al (2016) Sequence comparison, molecular modeling, and network analysis predict structural diversity in cysteine proteases from the Cape sundew, Drosera capensis. Comput Struct Biotechnol J 14:271–282. https://doi.org/10.1016/j.csbj.2016.05.003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Verma S, Dixit R, Pandey KC (2016) Cysteine proteases: modes of activation and future prospects as pharmacological targets. Front Pharmacol 7(107):1–12. https://doi.org/10.3389/fphar.2016.00107

    Article  CAS  Google Scholar 

  63. Jutamongkon R, Charoenrein S (2010) Effect of temperature on the stability of fruit bromelain from smooth Cayenne pineapple. Kasetsart J (Nat Sci) 44:943–948

    Google Scholar 

  64. Robinson PK (2015) Enzymes: principles and biotechnological applications. Essays Biochem 59:1–41. https://doi.org/10.1042/bse0590075

    Article  PubMed  PubMed Central  Google Scholar 

  65. Fields PA, Dong Y, Meng X, Somero GN Adaptations of protein structure and function to temperature: there is more than one way to ‘skin a cat’. J Exp Biol 218(12):1801–1811. https://doi.org/10.1242/jeb.114298

  66. Ab S, Rahim ASMA, Rahman RNZRA, Leow TC, Basri M (2012) The role of Arg157Ser in improving the compactness and stability of ARM lipase. J Comput Sci Syst Biol 5(2):39–46. https://doi.org/10.4172/jcsb.1000088

    Article  CAS  Google Scholar 

  67. Kato K, Nakayoshi T, Fukuyoshi S, Kurimoto E, Oda A (2017) Validation of molecular dynamics simulations for prediction of three-dimensional structures of small proteins. Molecules 22(10):1–15. https://doi.org/10.3390/molecules22101716

    Article  CAS  Google Scholar 

  68. Paul M, Hazra M, Barman A, Hazra S (2014) Comparative molecular dynamics simulation studies for determining factors contributing to the thermostability of chemotaxis protein ‘CheY’. J Biomol Struct Dyn 32(6):928–949. https://doi.org/10.1080/07391102.2013.799438

    Article  CAS  PubMed  Google Scholar 

  69. Gu J, Tong H, Sun L, Lin Z (2019) Molecular dynamics perspective on the thermal stability of mandelate racemase. J Biomol Struct Dyn 37(2):383–393. https://doi.org/10.1080/07391102.2018.1427631

    Article  CAS  PubMed  Google Scholar 

  70. Wu X, Xu P, Wang J et al (2015) Folding mechanisms of trefoil knot proteins studied by molecular dynamics simulations and go-model. In: Wei D, Xu Q, Zhao T, Dai H (eds) Advance in structural bioinformatics. Springer Netherlands, Dordrecht, pp 93–110

    Chapter  Google Scholar 

  71. Camilloni C, Bonetti D, Morrone A et al (2016) Towards a structural biology of the hydrophobic effect in protein folding. Sci Rep 6:1–9. https://doi.org/10.1038/srep28285

    Article  CAS  Google Scholar 

  72. Pucci F, Rooman M (2017) Physical and molecular bases of protein thermal stability and cold adaptation. Curr Opin Struct Biol 42:117–128. https://doi.org/10.1016/j.sbi.2016.12.007

    Article  CAS  PubMed  Google Scholar 

  73. Rosa M, Roberts CJ, Rodrigues MA (2017) Connecting high-temperature and low temperature protein stability and aggregation. PLoS One 12(5):1–12. https://doi.org/10.1371/journal.pone.0176748

    Article  CAS  Google Scholar 

  74. Mishra A, Ranganathan S, Jayaram B, Sattar A (2018) Role of solvent accessibility for aggregation-prone patches in protein folding. Sci Rep 8(1):1–13 https://doi.org/10.1038/s41598-018-31289-6

    Article  CAS  Google Scholar 

  75. Daniel RM, Dines M, Petach HH (1996) The denaturation and degradation of stable enzymes at high temperatures. Biochem J 317(1):1–11. https://doi.org/10.1042/bj3170001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Pace CN, Fu H, Fryar KL et al (2014) Contribution of hydrogen bonds to protein stability. Protein Sci 23(5):652–661. https://doi.org/10.1002/pro.2449

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Gao Y, Mei Y, Zhang JZH (2015) Treatment of hydrogen bonds in protein simulations. In: Liu J (ed) Advanced materials for renewable hydrogen production, storage and utilization. In Tech, Rijeka, pp 111–136

    Google Scholar 

  78. Mukherjee S, Majumdar S, Bhattacharyya D (2005) Role of hydrogen bonds in protein-DNA recognition: effect of nonplanar amino groups. J Phys Chem B 109(20):10484–10492. https://doi.org/10.1021/jp0446231

    Article  CAS  PubMed  Google Scholar 

  79. Vogt G, Argos P (1997) Protein thermal stability: hydrogen bonds or internal packing? Fold Des 2(4):40–46. https://doi.org/10.1016/S1359-0278(97)00062-X

    Article  Google Scholar 

  80. Mallamace D, Fazio E, Mallamace F, Corsaro C (2018) The role of hydrogen bonding in the folding/unfolding process of hydrated lysozyme: a review of recent NMR and FTIR results. Int J Mol Sci 19(12):1–21. https://doi.org/10.3390/ijms19123825

    Article  CAS  Google Scholar 

  81. Ning X, Zhang Y, Yuan T et al (2018) Enhanced thermostability of glucose oxidase through computer-aided molecular design. Int J Mol Sci 19(2):1–11. https://doi.org/10.3390/ijms19020425

    Article  CAS  Google Scholar 

  82. Du X, Sang P, Xia Y et al (2017) Comparative thermal unfolding study of psychrophilic and mesophilic subtilisin-like serine proteases by molecular dynamics simulations. J Biomol Struct Dyn 35(7):1500–1517. https://doi.org/10.1080/07391102.2016.1188155

    Article  CAS  PubMed  Google Scholar 

  83. Burgos MI, Ochoa A, Perillo MA (2019) β-Sheet to α-helix conversion and thermal stability of β-galactosidase encapsulated in a nanoporous silica gel. Biochem Biophys Res Commun 508(1):270–274. https://doi.org/10.1016/j.bbrc.2018.11.077

    Article  CAS  PubMed  Google Scholar 

  84. Emberly EG, Mukhopadhyay R, Wingreen NS, Tang C (2003) Flexibility of α-helices: results of a statistical analysis of database protein structures. J Mol Biol 327(1):229–237. https://doi.org/10.1016/S0022-2836(03)00097-4

    Article  CAS  PubMed  Google Scholar 

  85. Chaturvedi D, Mahalakshmi R (2017) Transmembrane β-barrels evolution, folding and energetics. Biochim Biophys Acta Biomembr 1859(12):2467–2482. https://doi.org/10.1016/j.bbamem.2017.09.020

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Perczel S, Gaspari Z, Csizmadia IG (2005) Structure and stability of the beta-pleated sheets. J Comput Chem 26(11):1155–1168. https://doi.org/10.1002/jcc.20255

    Article  CAS  PubMed  Google Scholar 

  87. Gessmann D, Mager F, Naveed H et al (2011) Improving the resistance of a eukaryotic β-barrel protein to thermal and chemical perturbations. J Mol Biol 413(1):150–161. https://doi.org/10.1016/j.jmb.2011.07.054

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Cebe P, Hu X, Kaaplan DL et al (2013) Beating the heat-fast scanning melts silk beta sheet crystals. Sci Rep 3:1–7. https://doi.org/10.1038/srep01130

    Article  CAS  Google Scholar 

  89. Ahmad S, Kumar V, Ramanand KB, Rao NM (2012) Probing protein stability and proteolytic resistance by loop scanning: a comprehensive mutational analysis. Protein Sci 21(3):433–446. https://doi.org/10.1002/pro.2029

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Zeiske T, Stafford KA, Palmer III AG (2016) Thermostability of enzymes from molecular dynamics simulations. J Chem Theory Comput 12(6):2489–2492. https://doi.org/10.1021/acs.jctc.6b00120

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Shehu A, Kavraki LE (2012) Modeling structures and motions of loops in protein molecules. Entropy 14(2):252–290. https://doi.org/10.3390/e14020252

    Article  CAS  Google Scholar 

  92. Yedavalli P, Rao NM (2013) Engineering the loops in a lipase for stability in DMSO. Protein Eng Des Sel 26(4):317–324. https://doi.org/10.1093/protein/gzt002

    Article  CAS  PubMed  Google Scholar 

  93. Yu H, Yan Y, Zhang C, Dalby PA (2017) Two strategies to engineer flexible loops for improved enzyme thermostability. Sci Rep 7:1–15. https://doi.org/10.1038/srep41212

    Article  CAS  Google Scholar 

  94. Wintrode PL, Zhang D, Vaidehi N, Arnold FH, Goddard WA (2003) Protein dynamics in a family of laboratory evolved thermophilic enzymes. J Mol Biol 327(3):745–757. https://doi.org/10.1016/S0022-2836(03)00147-5

    Article  CAS  PubMed  Google Scholar 

  95. Chang HJ, Jian JW, Hsu HJ et al (2014) Loop-sequence features and stability determinants in antibody variable domains by high-throughput experiments. Structure 22(1):9–21. https://doi.org/10.1016/j.str.2013.10.005

    Article  CAS  PubMed  Google Scholar 

  96. Wong SWK, Liu JS, Kou SC (2017) Fast de novo discovery of low-energy protein loop conformations. Proteins Struct Funct Bioinf 85(8):1402–1412. https://doi.org/10.1002/prot.25300

    Article  CAS  Google Scholar 

  97. Alvarez-Ponce D, Ruiz-González MX, Vera-Sirera F, Feyertag F, Perez-Amador MA, Fares MA (2018) Arabidopsis heat stress-induced proteins are enriched in electrostatically charged amino acids and intrinsically disordered regions. Int J Mol Sci 19(8):1–15. https://doi.org/10.3390/ijms19082276

    Article  CAS  Google Scholar 

  98. Sosa-Pagán JO, Iversen ES, Grandl J (2017) TRPV1 temperature activation is specifically sensitive to strong decreases in amino acid hydrophobicity. Sci Rep 7(1):1–10. https://doi.org/10.1038/s41598-017-00636-4

    Article  CAS  Google Scholar 

  99. Szilágyi A, Závodszky P (2000) Structural differences between mesophilic, moderately thermophilic and extremely thermophilic protein subunits: results of a comprehensive survey. Structure 8:493–504

    Article  PubMed  Google Scholar 

  100. Ramli ANM, Mahadi NM, Shamsir MS et al (2012) Structural prediction of a novel chitinase from the psychrophilic Glaciozyma antarctica PI12 and an analysis of its structural properties and function. J Comput Aided Mol Des 26(8):947–961. https://doi.org/10.1007/s10822-012-9585-7

    Article  CAS  PubMed  Google Scholar 

  101. Sinha R, Khare SK (2013) Thermostable protease. In: Satyanarayana T, Littlechild J, Kawarabayasi Y (eds) Thermophilic microbes in environmental and industrial biotechnology: biotechnology of thermophiles. Springer, Heidelberg, pp 859–880

    Chapter  Google Scholar 

  102. Brewer SH, Tang Y, Vu DM, Gnanakaran S, Raleigh DP, Dyer RB (2012) Temperature dependence of water interactions with the amide carbonyls of α-helices. Biochemistry 51(26):5293–5299. https://doi.org/10.1021/bi3006434

    Article  CAS  PubMed  Google Scholar 

  103. Kazlauskas R (2018) Engineering more stable proteins. Chem Soc Rev 47(24):9026–9045. https://doi.org/10.1039/c8cs00014j

    Article  CAS  PubMed  Google Scholar 

  104. Russell RJM, Ferguson JMC, Hough DW, Danson MJ, Taylor GL (1997) The crystal structure of citrate synthase from the hyperthermophilic archaeon Pyrococcus furiosus at 1.9 Å resolution. Biochemistry 36(33):9983–9994. https://doi.org/10.1021/bi9705321

    Article  CAS  PubMed  Google Scholar 

  105. Kumar V, Sharma N, Bhalla TC (2014) In silico analysis of β-galactosidases primary and secondary structure in relation to temperature adaptation. J Amino Acids 2014:1–9. https://doi.org/10.1155/2014/475839

    Article  CAS  Google Scholar 

  106. Ramli ANM, Azhar MA, Shamsir MS et al (2013) Sequence and structural investigation of a novel psychrophilic α-amylase from Glaciozyma antarctica PI12 for cold-adaptation analysis. J Mol Model 19(8):2013. https://doi.org/10.1007/s00894-013-1861-5

    Article  CAS  Google Scholar 

  107. Yennamalli RM, Rader AJ, Wolt JD, Sen TZ (2011) Thermostability in endoglucanases is fold-specific. BMC Struct Biol 11(10):1–15. https://doi.org/10.1186/1472-6807-11-10

    Article  CAS  Google Scholar 

Download references

Funding

This research was supported by the Universiti Malaysia Pahang through research grants RDU180345 and PGRS180362.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Aizi Nor Mazila Ramli.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(DOCX 1859 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pang, W.C., Ramli, A.N.M. & Hamid, A.A.A. Comparative modelling studies of fruit bromelain using molecular dynamics simulation. J Mol Model 26, 142 (2020). https://doi.org/10.1007/s00894-020-04398-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00894-020-04398-1

Keywords

Navigation