Homology Modeling and Molecular Dynamic Study of Candida Albicans Heat Shock Protein 21, a Potential Target Protein for Antifungal Drug Development

Author(s): Abdul Wadood, Nasir Ahmed, Muhammad Riaz, Sulaiman Shams, Javed Anwer, Ayaz Ahmed

Background: Species of Candida are ranked as the third most frequently isolated pathogens from blood. Although, Candida albicans (C. albicans) are one of the main etiologic agent for candidiasis, the small heat shock protein 21 (sHsp21) in C. albicans showed its pivotal role for environmental stress adaptation and fungal virulence. In C. albicans Hsp21 is a necessary factor for thermal and oxidative stress tolerance. Results: In the present work a homology model of Hsp21 from C. albicans was developed and evaluated using validated methods. Ramachandran plot for the model demonstrated that 98.02 percent of residues are in most favorable region, indicating that the model is reliable. The computed energy value, instability index and root mean square deviation (RMSD) fluctuation of back bone alpha carbon of the model, confirming the stability of the model. Molecular dynamics simulations in explicit solvent environments were carried out for the entire protein by using Molecular Operating Environment (MOE) with AMBER99 force field with the aim to characterize the dynamics of the protein. The results showed an open motion of the protein in solvent. Conclusions: Until the determination of three-dimensional structure of Hsp21 experimentally, the predicted model will serve as a supportive reference for exploring the interactions between Hsp21 and its antagonists. This research might help to understand the mechanism of action of Hsp21 protein and might facilitate the design of new and potent chemo-types to combat infection caused by Candida albicans.

Candida albicans, Heat shock protein, Homology modeling, MD simulation

1. Atsushi I. Thermostability and aliphatic index of globular proteins. J Biochem 1980, 88:1895-1898
2. Bond SD, Leimkuhler, BJ, Laird, BB. The Nose-Poincare method for constant temperature molecular dynamics. J Comput Phys 1999, 151:114-134
3. Buchner J. Supervising the fold: functional principles of molecular chaperones. FASEB J, 1996, 10:10-19
4. Calderone RA, Clancy CJ. Candida and candidiasis. Vol. 3. 2002: ASM Press Washington, DC
5. Colombo AL, Guimar TS. Epidemiology of hematogenous infections due to Candida spp. Revista da Sociedade Brasileira de Medicina Tropical 2003, 36:599-607
6. Colovos C, Yeates TO. Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sc 1993, 2:1511-1519
7. Cornell WD, Piotr C, Christopher IB, Ian RG, Kenneth MM, David MF, David CS, Thomas F, James WC, Peter AK. A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J Am Chem Soc 1995, 117:5179-5197
8. Deleage G, Roux B. An algorithm for protein secondary structure prediction based on class prediction. Protein Eng 1987, 1:289-294
9. Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucl Acids Res 2004, 32:1792-1797
10. Enjalbert, B, Moran GP, Vaughan C, Yeomans T, Maccallum DM, Quinn J, Coleman DC, Brown AJ, Sullivan DJ. Genome-wide gene expression profiling and a forward genetic screen show that differential expression of the sodium ion transporter Ena21 contributes to the differential tolerance of Candida albicans and Candida dubliniensis to osmotic stress. Mol Microb 2009, 72:216-228
11. Fradin C, De GP, MacCallum D, Schaller M, Klis F, Odds FC, Hube B. Granulocytes govern the transcriptional response, morphology and proliferation of Candida albicans in human blood. Mol Microb 2005, 56:397-415
12. Francois LM, Duncan W, Ilse DJ, Pedro M, Silvia S, Iryna MB, Alistair JPB, Bernhard H. Small but crucial: the novel small heat shock protein Hsp21 mediates stress adaptation and virulence in Candida albicans. PloS One 2012, 7:38584-38594
13. Frishman D, Argos P. Argos, Incorporation of non-local interactions in protein secondary structure prediction from the amino acid sequence. Protein Eng 1996, 9: 133-142
14. Geourjon C, Deleage G. SOPM: a self-optimized method for protein secondary structure prediction. Protein Eng 1994, 7:157-164
15. Gill SC, Hippel PHV. Calculation of protein extinction coefficients from amino acid sequence data. Anal Biochem 1989, 182:319-326
16. Guruprasad K, Reddy BVB, Pandit MW. Correlation between stability of a protein and its dipeptide composition: a novel approach for predicting in vivo stability of a protein from its primary sequence. Protein Eng 1990 4:155-161
17. Heringa J. Computational methods for protein secondary structure prediction using multiple sequence alignments. Curr Protein Peptide Sci 2000, 1:273-301
18. Hornby JM, Ellen CJ, Amber DL, Joseph JT, Brandon J, Richard S, Patick D, Kenneth WN. Quorum sensing in the dimorphic fungus Candida albicans is mediated by farnesol. Appl Environ Microb 2001, 67:2982-2992
19. Jabra-Rizk MA, Johnson JK, Forrest G, Mankes K, Meiller TF, Venezia RA. Prevalence of Candida dubliniensis fungemia at a large teaching hospital. Clin Infect Dis 2005, 41:1064-1067
20. Ki-Bong O, Hiroshi M, Toshimichi N, Hideaki M. Purification and characterization of an autoregulatory substance capable of regulating the morphological transition in Candida albicans. Proceedings of the National Academy of Sciences 2001, 98:4664-4668
21. King RD, Sternberg MJE. Identification and application of the concepts important for accurate and reliable protein secondary structure prediction. Protein Sc 1996, 5: 2298-2310
22. Kyte J, Doolittle RF. A simple method for displaying the hydropathic character of a protein. J Mol Biol 1982, 157:105-132
23. Labute P. The generalized Born/volume integral implicit solvent model: estimation of the free energy of hydration using London dispersion instead of atomic surface area. J Comput Chem 2008, 29:1693-1698
24. Levin JM, Robson B, Garnier J. An algorithm for secondary structure determination in proteins based on sequence similarity. FEBS Lett 1986, 205:303-308
25. Linding R, Jensen LJ, Diella F, Bork P, Gibson TJ, Russel RB. Protein disorder prediction: implications for structural proteomics. Structure 2003. 11:1453-1459
26. Linding R, Russel RB, Neduva V, Gibson TJ. GlobPlot: exploring protein sequences for globularity and disorder. Nucl Acids Res 2003, 31:3701-3708
27. Lindquist S. Heat-shock proteins and stress tolerance in microorganisms. Curr Opin Gen Dev 1992, 2:748-755
28. Lorenz MC, Bender JA, Fink GR. Transcriptional response of Candida albicans upon internalization by macrophages. Eukar Cell 2004, 3:1076-1087
29. Mukherjee PK, Zhou G, Munyon R, Ghannoum MA. Candida biofilm: a well-designed protected environment. Medical Mycology 2005, 43:191-208
30. Needleman SB, Wunsch CD. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol 1970, 48:443-453
31. Nicholls S, Leach MD, Priest CL, Brown AJ. Role of the heat shock transcription factor, Hsf1, in a major fungal pathogen that is obligately associated with warm-blooded animals. Mol Microb 2009, 74:844-861
32. Odds FC. Candida and candidosis: a review and bibliography. 1988: Bailliere Tindall
33. Walsh TJ, Groll AH. Emerging fungal pathogens: evolving challenges to immunocompromised patients for the twentyfirst century. Transplant Infect Dis 1999, 1:247-261
34. Patel PH, Loeb LA. Getting a grip on how DNA polymerases function. Nature Struct Biol 2001. 8:656-658
35. Richter K, Haslbeck M, Buchner J. The heat shock response: life on the verge of death. Mol Cell 40,:253-266
36. Ritossa F. Discovery of the heat shock response. Cell Stress Chaper 1996, 1:97-98
37. Rost B, Sander C. Prediction of protein secondary structure at better than 70% accuracy. J Mol Biol 1993, 232:584-599
38. Staib P, Nichterlein MKT, Michel GKS, Hof H, Hacker J, Joachim M. Host-induced, stage-specific virulence gene activation in Candida albicans during infection. Mol Microb 1999, 32:533-546
39. Saville SP, Anna LL, Carol M, Jose LL. Engineered control of cell morphology in vivo reveals distinct roles for yeast and filamentous forms of Candida albicans during infection. Eukaryotic Cell 2003, 2:1053-1060
40. Sippl MJ. Knowledge-based potentials for proteins. Curr Opin Struct Biol 1995, 5: 229-235
41. Sippl MJ. Recognition of errors in three‐dimensional structures of proteins. Proteins Struct Funct Bioinform 1993, 17:355-362
42. Smelcerovic A, Ranqelov M, Smelcerovic Z, Veljkovic A, Cherneva E, Yancheva D, Nikolic GM, Petronijevic Z, Kocic G. Two 6-(propan-2-yl)-4-methyl-morpholine-2, 5-diones as new non-purine xanthine oxidase inhibitors and anti-inflammatory agents. Food Chem Toxicol 2013, 55:493-497
43. Summa CM, Levitt M. Near-native structure refinement using in vacuo energy minimization. Proceed Nat Acad of Sci 2007, 104:3177-3182
44. Thewes S, Kretschmar M, Park H, Schaller M, Filler SG, Hube B. In vivo and ex vivo comparative transcriptional profiling of invasive and non-invasive Candida albicans isolates identifies genes associated with tissue invasion. Mol Microb 2007, 63:1606-1628
45. Weiner SJ, Peter AK, Dzung TN, David AC. An all atom force field for simulations of proteins and nucleic acids. J Comput Chem 1986, 7:230-252
46. Whitesell L, Lindquist SL. HSP90 and the chaperoning of cancer. Nature Rev Cancer 2005 5:761-772
47. Yang ZR, Thomson R, McNeil P, Esnouf RM. RONN: the bio-basis function neural network technique applied to the detection of natively disordered regions in proteins. Bioinformatics 2005, 21:3369-3376