PROTEIN STRUCTURE PREDICTION USING GAS PHASE MOLECULAR DYNAMICS SIMULATION: EOTAXIN-3 CYTOKINE AS A CASE STUDY

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1 International Conference Mathematical and Computational Biology 2011 International Journal of Modern Physics: Conference Series Vol. 9 (2012) World Scientific Publishing Company DOI: /S PROTEIN STRUCTURE PREDICTION USING GAS PHASE MOLECULAR DYNAMICS SIMULATION: EOTAXIN-3 CYTOKINE AS A CASE STUDY NURUL BAHIYAH AHMAD KHAIRUDIN Bioprocess Engineering Department, Faculty of Chemical Engineering Universiti Teknologi Malaysia, UTM Skudai, Malaysia nurul@cheme.utm.my HABIBAH A WAHAB School of Pharmaceutical Sciences, Universiti Sains Malaysia, Minden, Penang, Malaysia habibahw@usm.my In the current work, the structure of the enzyme CC chemokine eotaxin-3 (1G2S) was chosen as a case study to investigate the effects of gas phase on the predicted protein conformation using molecular dynamics simulation. Generally, simulating proteins in the gas phase tend to suffer from various drawbacks, among which excessive numbers of protein-protein hydrogen bonds. However, current results showed that the effects of gas phase simulation on 1G2S did not amplify the protein-protein hydrogen bonds. It was also found that some of the hydrogen bonds which were crucial in maintaining the secondary structural elements were disrupted. The predicted models showed high values of RMSD, 11.5 Å and 13.5 Å for both vacuum and explicit solvent simulations, respectively, indicating that the conformers were very much different from the native conformation. Even though the RMSD value for the in vacuo model was slightly lower, it somehow suffered from lower fraction of native contacts, poor hydrogen bonding networks and fewer occurrences of secondary structural elements compared to the solvated model. This finding supports the notion that water plays a dominant role in guiding the protein to fold along the correct path. Keywords: Molecular dynamics simulation; gas phase; protein structure prediction. 1. Introduction Molecular dynamics simulation has been widely applied in theoretically investigating the effects of solvation on protein structures with explicit representations of aqueous solvent 1,2. The correct treatment of solvent is crucial in realistic simulation of proteins especially when simulating the folding process. However, the presence of thousands of water molecules only renders MD impractical as it will slow down the computation due to the substantial amount of CPU cost involved in calculating the enormous amount of solvent-solvent and solvent-protein interactions. The simplest way to avoid this problem 193

2 194 N. B. A. Khairudin and H. A. Wahab is by simulating the protein in gas-phase or in vacuum, free of solvent interactions 2,3. While simulations in the gas phase are prone to suffer from various artifacts, there have been a lot of experimental studies investigating the behavior of proteins in the gas phase. Recent technological developments such as mass spectrometric based techniques have made it possible to study large proteins in vacuo 4,5. The gradual rise of awareness in exploring proteins in vacuo was the main motivation for the current work. Thus, the main objective of the current study was to investigate the effects of gas phase in predicting the 3D structure of proteins. This method was tested both in vacuum and condensed phase. The current work was carried out using the randomly chosen protein, eotaxin-3 cytokine, (PDB id: 1G2S) containing 71 amino acid residues. 2. Materials and Methods 2.1. Simulation details All the MD simulations in the current study were performed using the AMBER8 suites of programs 6 employing the amber.ff03 force field 7. The simulations were performed in isothermal-isobaric ensemble (NPT) at 300 K and 1 atm, respectively. The system was then subjected to 50 ns of MD simulation using periodic boundary condition Development of the model The 71-residues linear amino acid chain of 1G2S was subjected to sequence analysis using the web-interface BLAST 8 to locate for the appropriate template. The 3D model was built using the program Modeller7v Results and Discussion The results for the solvated phase (G2S wat ) and the gas phase (G2S vac ) models were presented. The structural properties were compared with the corresponding averaged NMR structures, G2S NMR-wat and G2S NMR-vac, respectively. RMSD back-all (Backbone RMSD for all residues) for both models showed almost a plateau phase fluctuating around 11.5 Å and 13.5 Å for G2S vac and G2S wat, respectively. It was observed that G2S wat showed higher RMSD back-all compared to G2S vac. This corresponded to the high mobility of the residues in the presence of water solvent which agreed well with the analysis of RMSF shown in Figure 1.

3 Protein Structure Prediction Using Gas Phase Molecular Dynamic Simulation 195 Fig. 1. Root mean square fluctuations (RMSF) (Å) averaged over 150 ns for G2S vac and G2S wat. Fig. 2. Time development (ps) of fraction of native contacts for G2S vac and G2S wat. Figure 2 showed the development of fraction of native contacts for G2S vac and G2S wat calculated as reference to their respective NMR average structures. The solvated system showed higher fraction (35% 45%) as compared to that of the gas phase (25% 30%). Two of the important native contacts worth highlighting were the contacts between the cysteines which form disulfide bridges in G2S NMR. Despite the fact that disulfide bridges usually form in the last stage of the protein folding process, the result showed that there seemed to be a gradual reduction in the distance between these cysteines which in turn suggested that the conformations were making slow adjustments in the early stage of folding process. In general, both contacts showed shorter distances in the gas phase than in the solvated phase. The distances between Cys11 Cys50 in the last few trajectories were observed to fluctuate between 5 12 Å and 8 15 Å, for G2S vac and G2S wat, respectively. On the other hand, the corresponding distances of Cys10 Cys34 fluctuated at higher values, Å for the former and Å for the latter.

4 196 N. B. A. Khairudin and H. A. Wahab With the inspection of the changes of secondary structural elements, it was observed that there was no formation of native α-helix (Lys56-leu65) in both simulations. However, a careful inspection revealed that there were transient formations of non-native 3 10 helix covering residues Ile62 to Leu65 from 80 ns onwards. This phenomenon however did not occur in G2S wat. Previous work by Millhauser suggested that the occurrence of 3 10 helix played an important role as an intermediate in the formation of α-helix 10. Armen and colleagues on the other hand showed that this helix had no role in the folding process of proteins 11. Thus, the presence of this non-native 3 10 helix was mere helix-coil transition intermediate which had no influence in directing the structure towards the native state. Conversely, the formation of the native 3 10 helix (Trp21 Trp23) was observed to occur in both models. This element seemed to be more stable in the solvated phase although there were a few transitions to α-helix in the beginning of the simulation. The α-helix transitions were common features in proteins as already highlighted by previous studies 12,13 As for the gas phase simulation, G2S vac showed frequent transitions between 310 helix and α-helix. This finding however was in contrast with previous claim that 310 helix was more stable and stronger in the gas phase 14. The core region that comprised the antiparallel strands was shown to be more stable in the solvated model than that in the vacuo model. Strand β1 which previously not present in the starting raw model was observed to form in both models. However, its occurrence was more pronounced in G2S wat especially in the last 70 ns. The occurrences of secondary structural elements had strong correlations with the formation of hydrogen bonds. Thus, in order to better understand the changes in these secondary structures, an analysis of the hydrogen bonding network was presented. The average number of hydrogen bonds in G2S wat was found to be slightly higher than the average number of hydrogen bonds in G2S vac, with 41 for the former and 36 for the latter. Again, this was in contrast with previous findings which claimed that proteins in vacuo or gas phase likely to have excessive amount of hydrogen bonds 15,16. The list of the proteinprotein hydrogen bonds that appeared for more than 30% of the simulation time in G2S wat were shown in Table 1 together with its correspondent formations in the gas phase. There were only nine equivalent native hydrogen bonds that appeared both in G2S wat and G2S vac with occupancies more than 30% in the solvated phase. 4. Conclusions It was previously demonstrated that simulating proteins in vacuo tended to suffer from serious artifacts, among which small positional fluctuations, small radius of gyration and excessive number of protein-protein hydrogen bonds (Levitt and Sharon, 1988). Current work showed both agreement and discrepancy on the influence of gas phase on proteins with that of earlier studies. The current findings showed that proteins in gas phase were not necessarily forming excessive hydrogen bonds as what previously claimed. In this case, the presence of the non-native hydrogen bonds mostly resulted from the breaking of

5 Protein Structure Prediction Using Gas Phase Molecular Dynamic Simulation 197 the existing native hydrogen bonds. These newly formed hydrogen bonds were unstable and kept on breaking, forming and swapping partners. This ensured consistent total numbers of hydrogen bonds for the gas and solvated protein. Table 1. Comparison of protein-protein hydrogen bonds 5. Acknowledgement This work is supported by the Top Down Grant No BTK/TD/004 awarded by the National Biotechnology Directorate, Ministry of Science, Technology and Innovation, Malaysia. The authors wish to acknowledge MIMOS (M) Berhad for providing the computing time and UTM for providing the scholarship. References G2S wat G2S vac Donor Acceptor % Occupancies % Occupancies Native 41@O 49@N Yes (strong) 42@O 25@N Yes (strong) 26@O 42@N Yes (strong) 43@O 46@N Yes (better) 49@O 41@N Yes (better) 51@OG1 52@N Yes (weak) 51@O 39@N Yes (strong) 39@O 51@N Yes (better) 47@O 43@N Yes (better) The listed hydrogen bonds were taken from the MD simulation of G2S wat occurring more than 30% of the simulation time. Also shown was their correspondent formation in G2S vac. The features of the hydrogen bonds were presented as the percentage (%) of occupancies. The column native checked whether the listed hydrogen bond was present in G2S NMR-wat with strength criteria based on the % of occupancies as follows: strong (60% above), better (30-59%), weak (10-29%). 1. M. Levitt, R. Sharon, Proc Natl Acad Sci USA 85, 7557 (1988). 2. W. F. van Gunsteren, M. Karplus, Biochemistry 21, 2259 (1982). 3. J. A. McCammon, B. R. Gelin, M. Karplus, Nature 267, 585 (1977). 4. K. Breuker, Int J Mass Spect 239, 33 (2004). 5. K. B. Shelimov, D. E. Clemmer, R. R. Hudgins, M. F. Jarrold, J Am Chem Soc 119, 2240 (1997). 6. D. A. Pearlman, D. A. Case, J. W. Caldwell, W. S. Ross, T. E. Cheatham III, S. DeBolt, N. Ferguson, G. Seibel, P. Kollman, Comp Phys Comm 91, 1 (1995).

6 198 N. B. A. Khairudin and H. A. Wahab 7. Y. Duan, C. Wu, S. Chowdhury, M. C. Lee, G. Xiong, W. Zhang, R. Yang, P. Cieplak, R. Luo, T. Lee, J. Caldwell, J. Wang, P. Kollman, J Comp Chem 24, 1999 (2003). 8. S. F. Altschul, W. Gish, W. Miller, E. W. Myers, D. J. Lipman, J Mol Biol 215, 403 (1990). 9. A. Sali, T. L. Blundell, J Mol Biol 234, 779 (1993). 10. G. L. Millhauser, Biochemistry 34, 3873 (1995). 11. R. Armen, D. O. V. Alonso, V. Daggett, Protein Sci 12, 1145 (2003). 12. N. Sreerama, R. W. Woody, Proteins 36, 400 (1999). 13. V. Daggett, M. Levitt, Proc Natl Acad Sci USA 89, 5142 (1992). 14. I. A. Topol, S. K. Burt, E. Deretey, T. H. Tang, A. Perczel, A. Rashin, I. G. Csizmadia, J Am Chem Soc 123, 6054 (2001). 15. D. S. Hartsough, K. M. Merz, J Am Chem Soc 115, 6529 (1993). 16. M. Norin, F. Haeffner, K. Hult, O. Edholm, Biophys J 67, 548 (1994).