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Aggregation mechanism investigation of the GIFQINS cross-β amyloid fibril
Computational Biology and Chemistry 33 (2009) 41–45
Contents lists available at ScienceDirect
Computational Biology and Chemistry journal homepage: www.elsevier.com/locate/compbiolchem
Research Article
Aggregation mechanism investigation of the GIFQINS cross- amyloid fibril Hai-Feng Chen ∗ College of Life Sciences and Biotechnology, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai 200240, China
a r t i c l e
i n f o
Article history: Received 26 May 2008 Received in revised form 16 July 2008 Accepted 16 July 2008 Keywords: Amyloid-like fibril Aggregation mechanism Intermediate state Transition state
a b s t r a c t Amyloid-like fibrils are found in many fatal diseases, such as Alzheimer’s disease, type II diabetes mellitus, and the transmissible spongiform encephalopathies, and prion diseases. The kinetics of fibril formation is still debated and becomes a hotspot. In this study, we intend to utilize room temperature simulation to study the stability of the modeling structure for GIFQINS. The results suggest that the hexamer of GIFQINS is highly stable and consistent with the prediction of Eisenberg. Furthermore, high-temperature molecular dynamics simulation in explicit water is used to study its aggregation mechanisms. The important findings from this work are (a) dimer is not thermodynamically stable state, (b) dissolution of the fibrils is more difficult than aggregation, (c) tetramer (2-2) is the intermediate state and (d) two transition states are corresponding to trimer (2-1) and pentamer (3-2). This is the first time to suggest the tetramer (2-2) as intermediate state with kinetics analysis and can shed light on possible mechanisms of aggregation. © 2008 Elsevier Ltd. All rights reserved.
1. Introduction Amyloid-like fibrils are found in many fatal diseases, including Alzheimer’s disease, type II diabetes mellitus, and the transmissible spongiform encephalopathies, and prion diseases (Dobson, 1999). These diseases are linked to proteins that have partially unfolded, misfolded, and aggregated into amyloid-like fibrils. Decades of investigations of the structural properties of amyloid-like fibrils have established that all fibrils have a common structural cross spine (Sipe and Cohen, 2000). Due to the noncrystalline and insoluble nature of the amyloid fibril, it is difficult to obtain atomic-resolution structures with traditional experimental methods. Recently, some crystal structures of a six or seven residue fragment were released (Nelson et al., 2005; Sawaya et al., 2007). At the same time, Thompson et al. suggests that other sequences such as GIFQINS from lysozyme, GGGVVIA from Abeta1–42 and GVQIVYK from tau protein have the propensity to form cross- structure (Thompson et al., 2006). In order to study their stability and aggregation mechanism, we construct their 3D structures. These atomic-resolution structures make it possible to investigate the mechanism of amyloid formation by molecular modeling methods and directly compare the modeling with experimental results.
∗ Tel.: +86 21 34204348; fax: +86 21 34204348. E-mail address: [email protected]. 1476-9271/$ – see front matter © 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.compbiolchem.2008.07.023
The kinetics of fibril formation is still hotly debated and remains an important open question. Several computational studies provide insight into the characteristics of the amyloid aggregate (DeMarco and Daggett, 2004; Wu et al., 2005; Gnanakaran et al., 2006; Nguyen and Hall, 2004; Lipfert et al., 2005; Zheng et al., 2008; Kent et al., 2008; Boucher et al., 2006; Mousseau and Derreumaux, 2005). These researches tell us the aggregation process of amyloid fibril. However, we still do not know if there is an intermediate state and transition state during the process of aggregation. The purpose of this study is to answer these questions. Since the timescale of the formation of amyloid fibrils from peptide selfassembly is on minutes to days (Kayed et al., 2003), which is much longer than the timescale of nanoseconds for classical allatom MD simulation. To some extend, prion aggregation is like protein folding. High-temperature simulations of protein unfolding have been widely used (Caflisch and Karplus, 1994; Daggett et al., 1996; Ladurner et al., 1998; Mayor et al., 2000, 2003; Gsponer and Caflisch, 2001; Chen and Luo, 2007), to study protein folding within reasonable time. Therefore, high-temperature molecular dynamics simulation was used to investigate the aggregation mechanism. Common structure character for these amyloid fibrils implies common mechanism of pathogenesis (Kayed et al., 2003). This indicates that the study of short peptide aggregation could illustrate the common fundamental mechanism that governs fibril formation in large protein systems. In this study, we intend to utilize the GIFQINS structure and high-temperature molecular dynamics simulation in explicit water to understand its aggregation mecha-
42
H.-F. Chen / Computational Biology and Chemistry 33 (2009) 41–45
Fig. 1. Hexamer of GIFQINS cross- prion. Fig. 2. C␣ variation of residues for GIFQINS.
nisms. A GIFQINS hexamer (shown in Fig. 1) model was adopted in our analysis. 2. Materials and Methods
3. Results
2.1. Room-temperature and High-temperature Molecular Dynamics Simulations
3.1. The Stability of Hexamer of Peptide GIFQINS, GGGVVIA and GVQIVYK
In the present work, the atomic coordinates of the cross- GIFQINS, GGGVVIA and GVQIVYK hexamer were constructed based on the template of the X-ray structure 1YJP (Nelson et al., 2005). All MD simulations are all-atom explicit solvent and are performed at 298 and 448 K, respectively. A total of 750 ns trajectories were collected at 298 and 448 K, respectively, taking about 25,940 CPU hours on the in-house Xeon (1.86 GHz) cluster. Details of MD protocols, and choice of force fields are described in elsewhere (Chen and Luo, 2007; Chen, 2008).
The stability of hexamer GIFQINS, GGGVVIA and GVQIVYK 10 trajectories of 10.0 ns each were simulated at 298 K. The summary of simulations is provided in Table 1s (supplementary). The C␣ atom RMSDs are shown in Fig. 1s (supplementary). The average RMSD is about 25 Å for GVQIVYK, 10 Å for GGGVVIA, and 2 Å for GIFQINS at the end of 10 ns simulation. This suggests that the hexamers of GVQIVYK and GGGVVIA are unstable. This is consistent to the recent experimental observation (Sawaya et al., 2007). On the contrary, the one of GIFQINS is very stable. This is in agreement with the prediction of Eisenberg that the peptide GIFQINS from lysozyme can from amyloid fibril (Thompson et al., 2006). Therefore, the hexamer of GIFQINS was used to study the aggregation mechanism. The average distance within the hexamer is about 4.88 ± 0.39 Å for interstrand, and 10.07 ± 0.38 Å for intersheet throughout the simulations. This is similar to the experimental observations for peptide GNNQQNY (Nelson et al., 2005). To study the stability in detail, C␣ fluctuations are illustrated in Fig. 2. This indicates that all chains have common characteristics of small variation for the five central residues whereas large variations for the two end residues, suggesting that the center residues are more rigid than the residues in the termini regions. This is in agreement with the reported of Zheng et al. (2006) However, the fluctuation of Asn6 and Ser7 is larger than those of Gly1 and Ile2 for strands 1–3, and the fluctuation for strands 4–6 is reverse. A little twist for amyloid fibril during room temperature was found. This is in agreement with other simulation (Esposito et al., 2006). To further monitor the interaction responsible for the aggregation stability, native contacts and hydrogen bonds of interstrand and intersheet were studied. 5 stable hydrogen bonds were found for a couple of peptide of interstrand with populations higher than 60% (shown in Fig. 3). There are two types of native contact. One is the contact of interstrand, and the other is intersheet. The populations of native contacts for a couple of peptide of interstrand and intersheet in simulation are shown in Fig. 4. Six stable interstrand and three stable intersheet native contacts can be found with populations higher than 60%. This suggests that these native contacts of interstrand and intersheet should be major driving forces for the aggregation.
2.2. The Definition of Ordered Peptide There are two types of native contact: interstrand and intersheet among the hexamer. Interstrand was defined as the two parallel strands, and intersheet represented two antiparallel sheets. The native contacts are counted when the C␣ atoms residues are closer than 6 Å for interstrands, and when the center mass distance of side chain is less than 6 Å for intersheet native contacts. The number of native contacts is about 6 for interstrands, and 3 for intersheets, respectively. At high temperature, the most native contacts can disrupt. Therefore, the ordered peptide is defined as the native contacts are more than 2 for interstrands and 1 for intersheets at high temperature. According to the definition of ordered peptide, there is one form for hexamer and pentamer. For tetramer, there are two forms, one three strands in one sheet and the fourth on the other sheet, or each two in the same sheet (3-1 vs. 2-2). For trimer, there are also two forms, all three strands belong to one single sheet, or two strands in one sheet and the third on the other sheet (3-0 vs. 2-1). For dimer, two conformations are defined. Two strands are on one sheet or belong to different sheets (2-0 vs. 1-1). 2.3. Transition State Simulations Based on the definition of transition state (TS), 40 test MD runs for each candidate snapshot were performed to calculate the transition probability (P) (Chong et al., 2005; Gsponer and Caflisch, 2002; Pande and Rokhsar, 1999). TS simulations were done at 448 K in order to accelerate simulated aggregation/disaggregation rate. The detail methods are described in elsewhere (Chen and Luo, 2007; Chen, 2008).
H.-F. Chen / Computational Biology and Chemistry 33 (2009) 41–45
Fig. 3. The statistics of hydrogen bond.
43
Fig. 6. Free energy vs. ordered number of peptide.
Fig. 7. Average transition state structures. A: TS1, B: TS2. (Green represents ordered peptide, red for disaggregation one). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.) Fig. 4. Native contact for interstrand and intersheet.
3.2. Disaggregation Kinetics Analysis In the following we first focus on disaggregation kinetics for cross- GIFQINS hexamer. Ordered number of peptides, i.e. number of peptides that form crystal-structure-like aggregates, is used to monitor disaggregation kinetics. Time evolution of the disaggregation at 448 K simulation is shown in Fig. 5. Overall, the peptide disaggregation can be represented well by two exponential functions, indicating that the process obeys second order kinetics. Our kinetics analysis shows that the first half-time is 1.01 ns, and the second half-time is 3.07 ns.
Fig. 5. Kinetics fitting for ordered number of peptides.
To understand the disaggregation pathway, the disaggregation landscape was analyzed with the progress variable of ordered number of peptides, and shown in Fig. 6. Besides the hexamer, there is one free energy-minima conformer corresponding to tetramer. This is in agreement with the kinetics analysis indicating the process is second order. Dimer is not thermodynamically stable conformation. A recent MD study by Zheng et al. (2006) suggested that dimer conformations of peptide GNNQQNY were unstable. For A1–42 , dimers are no observed by mass spectrometry or unstable with simulation (Bernstein et al., 2005; Urbanc et al., 2004; Wu et al., 2004). From monomer to tetramers, there is a barrier of 1.98 kT. From tetramers to hexamers there is a barrier of 0.060 kT. This suggests that tetramer can easily form hexamer. Note that the bar-
Fig. 8. RMSD of C␣ atom.
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H.-F. Chen / Computational Biology and Chemistry 33 (2009) 41–45
Fig. 9. Proposed aggregation pathway: (A) monomer, (B) tetramer and (C) hexamer.
rier is 0.90 kT from hexamer to tetramer. This suggests that there is higher barrier to disaggregate the fibrils to the intermediate state. Furthermore, there is a free energy-minimum intermediate state corresponding to tetramer. Nelson et al. hypothesized that the nucleus for the GNNQQNY aggregation is about four peptides (Nelson et al., 2005). Our simulation testifies this hypothesis. 3.3. Transition State Our kinetics analysis shows that disaggregation obeys second order kinetics. This suggests that hexamer disaggregates via a three-state process. Therefore, there are two transition states corresponds to the free energy maximum along the disaggregation pathways. We have scanned MD snapshots for two transition states (denoted as TS1 and TS2 below) structures in all 10 high-temperature trajectories at 448 K simulation. Transition probabilities vs. candidate snapshots for one of the trajectories are shown in Fig. 2s (supplementary). Overall 71 snapshots for TS1 and 62 snapshots for TS2 were found, respectively. Fig. 7 illustrates the average structures for all transition state snapshots for TS1 and TS2, respectively. Apparently from Fig. 7, TS1 is the aggregation of five peptides and TS2 is the state with three peptides. Nelson et al. hypothesized that the transition state complex on the pathway to the nucleus is approximately three peptides for GNNQQNY (Nelson et al., 2005). Our transition state analysis suggests highly similar result that the trimer is as the first transition state in aggregation for GIFQINS. For TS2, two strands are on the single sheet and third one is on other sheet (2 + 1 conformation). Strands 2 and 3 form a sheet and strand 6 enters into this sheet. There are 2 native contacts between strands 2 and 3, 3 native contacts between strands 2 and 6, and 1 native contact between strands 3 and 6. The result suggests that two monomers aggregate one -sheet, then add one monomer to form zipper transition state. 3.4. The Stability of Dimer, Trimer, Tetramer and Pentamer In order to conform the disaggregation kinetics, the stabilities at room temperature of dimer, trimer, tetramer and pentamer were studied. As shown in Fig. 8, the RMSDs were maintained at 3.1 ± 0.7 Å for tetramer (2-2) and 2.9 ± 0.4 Å for pentamer (32) within 10 ns, respectively, indicating significant stability of the structures of tetramer (2-2) and pentamer (3-2). For the model systems of dimer, the RMSDs increased quickly to ∼8 Å for dimer (1-1) and ∼12 Å for dimer (2-0) after 6 ns, which suggested that they lost their original dimer organization. This is consistent with the report of Zheng et al. for the dimer of GNNQQNY. For the model systems of trimer, the RMSDs were about 12 Å for trimer (2-1) and 8 Å for trimer (3-0) at the end of 10 ns. Furthermore, the RMSD of tetramer (3-1) gradually increased to 10 Å. These large RMSDs indicated that trimer (2-1, 3-0) and tetramer (3-1) are not thermodynamically stable state. The residue fluctuation of oligomers was shown in Fig. 3s (supplementary). The fluctuations of tetramer (2-
2) and pentamer (3-2) were the smallest among these oligomers. The significant stability of tetramer (2-2) suggests that the intermediate state is tetramer (2-2) and consistent with the result of disaggregation kinetics. For pentamer (3-2), it seems to have some conflicts with pentamer as TS2. However, the barrier is just 0.060 kT between tetramer and pentamer. Pentamer might also have high stability at room temperature. Collins et al. report that fibers grow by monomer addition (Collins et al., 2004). The possibility mechanism is that pentamer is aggregated by monomer added after the intermediate state is formed. 4. Discussion 4.1. Disaggregation Pathways and Likely Aggregation Pathways Finally if we assumed aggregation is the reverse of disaggregation, the proposed aggregation pathway is monomer, tetramer, and finally hexamer formation (shown in Fig. 9). For tetramer, there are strands 2, 3, 5, and 6 aggregating together. There are 2 native contacts between interstrands 2 and 3, and 3 native contacts between interstrands 5 and 6, respectively. For intersheets, there is 2 native contacts between strands 2 and 5, 3 native contacts between strands 2 and 6, and 1 native contact between strands 3 and 6, respectively. These native contacts can stabilize this intermediate state. TS1 is between tetramer and hexamer, and TS2 is between dimer and tetramer. 5. Conclusions In summary, our all-atom explicit solvent molecular dynamics reveals that the intermediate state during the aggregation of GIFQINS cross- spine is necessary and dissolution of the fibrils is unfavorable. The aggregation mechanism from our explicit solvent MD simulations is consistent with the prediction of Eisenberg and coworkers (Nelson et al., 2005). The important findings from this work are (a) dimer is not thermodynamically stable state, (b) dissolution of the fibrils is more difficult than aggregation, (c) tetramer (2-2) is the intermediate state and (d) two transition states are corresponding to trimer (2-1) and pentamer (3-2). Room temperature simulation of oligomers also suggests that dimer and trimer are not thermodynamically stable state, in agreement with the disaggregation kinetics. Acknowledgments This work is supported by National Natural Science Foundation of China (Grants No. 30770502 and 20773085). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.compbiolchem.2008.07.023.
H.-F. Chen / Computational Biology and Chemistry 33 (2009) 41–45
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Lipfert, J., Franklin, J., Wu, F., Doniach, S., 2005. Protein misfolding and amyloid formation for the peptide GNNQQNY from yeast prion protein Sup35: simulation by reaction path annealing. J. Mol. Biol. 349, 648–658. Mayor, U., Johnson, C.M., Daggett, V., Fersht, A.R., 2000. Protein folding and unfolding in microseconds to nanoseconds by experiment and simulation. Proc. Natl. Acad. Sci. U.S.A. 97, 13518–13522. Mayor, U., Guydosh, N.R., Johnson, C.M., Grossmann, J.G., Sato, S., Jas, G.S., Freund, S.M.V., Alonso, D.O.V., Daggett, V., Fersht, A.R., 2003. The complete folding pathway of a protein from nanoseconds to microseconds. Nature 421, 863–867. Mousseau, N., Derreumaux, P., 2005. Exploring the early steps of amyloid peptide aggregation by computers. Acc. Chem. Res. 38, 885–891. Nelson, R., Sawaya, M.R., Balbirnie, M., Madsen, A.O., Riekel, C., Grothe, R., Eisenberg, D., 2005. Structure of the cross-beta spine of amyloid-like fibrils. Nature 435, 773–778. Nguyen, H.D., Hall, C.K., 2004. Molecular dynamics simulations of spontaneous fibril formation by random-coil peptides. Proc. Natl. Acad. Sci. U.S.A. 101, 16180–16185. Pande, V.S., Rokhsar, D.S., 1999. Molecular dynamics simulations of unfolding and refolding of a beta-hairpin fragment of protein G. Proc. Natl. Acad. Sci. U.S.A. 96, 9062–9067. Sawaya, M.R., Sambashivan, S., Nelson, R., Ivanova, M.I., Sievers, S.A., Apostol, M.I., Thompson, M.J., Balbirnie, M., Wiltzius, J.J., McFarlane, H.T., Madsen, A.O., Riekel, C., Eisenberg, D., 2007. Atomic structures of amyloid cross-beta spines reveal varied steric zippers. Nature 447, 453–457. Sipe, J.D., Cohen, A.S., 2000. Review: history of the amyloid fibril. J. Struct. Biol. 130, 88–98. Thompson, M.J., Sievers, S.A., Karanicolas, J., Ivanova, M.I., Baker, D., Eisenberg, D., 2006. The 3D profile method for identifying fibril-forming segments of proteins. Proc. Natl. Acad. Sci. U.S.A. 103, 4074–4078. Urbanc, B., Cruz, L., Ding, F., Sammond, D., Khare, S., Buldyrev, S.V., Stanley, H.E., Dokholyan, N.V., 2004. Molecular dynamics simulation of amyloid beta dimer formation. Biophys. J. 87, 2310–2321. Wu, C., Lei, H., Duan, Y., 2004. Formation of partially ordered oligomers of amyloidogenic hexapeptide (NFGAIL) in aqueous solution observed in molecular dynamics simulations. Biophys. J. 87, 3000–3009. Wu, C., Lei, H., Duan, Y., 2005. Elongation of ordered peptide aggregate of an amyloidogenic hexapeptide NFGAIL observed in molecular dynamics simulations with explicit solvent. J. Am. Chem. Soc. 127, 13530–13537. Zheng, J., Ma, B., Tsai, C.J., Nussinov, R., 2006. Structural stability and dynamics of an amyloid-forming peptide GNNQQNY from the yeast prion sup-35. Biophys. J. 91, 824–833. Zheng, J., Jang, H., Nussinov, R., 2008. Beta2-microglobulin amyloid fragment organization and morphology and its comparison to Abeta suggests that amyloid aggregation pathways are sequence specific. Biochemistry 47, 2497–2509.
Contents lists available at ScienceDirect
Computational Biology and Chemistry journal homepage: www.elsevier.com/locate/compbiolchem
Research Article
Aggregation mechanism investigation of the GIFQINS cross- amyloid fibril Hai-Feng Chen ∗ College of Life Sciences and Biotechnology, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai 200240, China
a r t i c l e
i n f o
Article history: Received 26 May 2008 Received in revised form 16 July 2008 Accepted 16 July 2008 Keywords: Amyloid-like fibril Aggregation mechanism Intermediate state Transition state
a b s t r a c t Amyloid-like fibrils are found in many fatal diseases, such as Alzheimer’s disease, type II diabetes mellitus, and the transmissible spongiform encephalopathies, and prion diseases. The kinetics of fibril formation is still debated and becomes a hotspot. In this study, we intend to utilize room temperature simulation to study the stability of the modeling structure for GIFQINS. The results suggest that the hexamer of GIFQINS is highly stable and consistent with the prediction of Eisenberg. Furthermore, high-temperature molecular dynamics simulation in explicit water is used to study its aggregation mechanisms. The important findings from this work are (a) dimer is not thermodynamically stable state, (b) dissolution of the fibrils is more difficult than aggregation, (c) tetramer (2-2) is the intermediate state and (d) two transition states are corresponding to trimer (2-1) and pentamer (3-2). This is the first time to suggest the tetramer (2-2) as intermediate state with kinetics analysis and can shed light on possible mechanisms of aggregation. © 2008 Elsevier Ltd. All rights reserved.
1. Introduction Amyloid-like fibrils are found in many fatal diseases, including Alzheimer’s disease, type II diabetes mellitus, and the transmissible spongiform encephalopathies, and prion diseases (Dobson, 1999). These diseases are linked to proteins that have partially unfolded, misfolded, and aggregated into amyloid-like fibrils. Decades of investigations of the structural properties of amyloid-like fibrils have established that all fibrils have a common structural cross spine (Sipe and Cohen, 2000). Due to the noncrystalline and insoluble nature of the amyloid fibril, it is difficult to obtain atomic-resolution structures with traditional experimental methods. Recently, some crystal structures of a six or seven residue fragment were released (Nelson et al., 2005; Sawaya et al., 2007). At the same time, Thompson et al. suggests that other sequences such as GIFQINS from lysozyme, GGGVVIA from Abeta1–42 and GVQIVYK from tau protein have the propensity to form cross- structure (Thompson et al., 2006). In order to study their stability and aggregation mechanism, we construct their 3D structures. These atomic-resolution structures make it possible to investigate the mechanism of amyloid formation by molecular modeling methods and directly compare the modeling with experimental results.
∗ Tel.: +86 21 34204348; fax: +86 21 34204348. E-mail address: [email protected]. 1476-9271/$ – see front matter © 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.compbiolchem.2008.07.023
The kinetics of fibril formation is still hotly debated and remains an important open question. Several computational studies provide insight into the characteristics of the amyloid aggregate (DeMarco and Daggett, 2004; Wu et al., 2005; Gnanakaran et al., 2006; Nguyen and Hall, 2004; Lipfert et al., 2005; Zheng et al., 2008; Kent et al., 2008; Boucher et al., 2006; Mousseau and Derreumaux, 2005). These researches tell us the aggregation process of amyloid fibril. However, we still do not know if there is an intermediate state and transition state during the process of aggregation. The purpose of this study is to answer these questions. Since the timescale of the formation of amyloid fibrils from peptide selfassembly is on minutes to days (Kayed et al., 2003), which is much longer than the timescale of nanoseconds for classical allatom MD simulation. To some extend, prion aggregation is like protein folding. High-temperature simulations of protein unfolding have been widely used (Caflisch and Karplus, 1994; Daggett et al., 1996; Ladurner et al., 1998; Mayor et al., 2000, 2003; Gsponer and Caflisch, 2001; Chen and Luo, 2007), to study protein folding within reasonable time. Therefore, high-temperature molecular dynamics simulation was used to investigate the aggregation mechanism. Common structure character for these amyloid fibrils implies common mechanism of pathogenesis (Kayed et al., 2003). This indicates that the study of short peptide aggregation could illustrate the common fundamental mechanism that governs fibril formation in large protein systems. In this study, we intend to utilize the GIFQINS structure and high-temperature molecular dynamics simulation in explicit water to understand its aggregation mecha-
42
H.-F. Chen / Computational Biology and Chemistry 33 (2009) 41–45
Fig. 1. Hexamer of GIFQINS cross- prion. Fig. 2. C␣ variation of residues for GIFQINS.
nisms. A GIFQINS hexamer (shown in Fig. 1) model was adopted in our analysis. 2. Materials and Methods
3. Results
2.1. Room-temperature and High-temperature Molecular Dynamics Simulations
3.1. The Stability of Hexamer of Peptide GIFQINS, GGGVVIA and GVQIVYK
In the present work, the atomic coordinates of the cross- GIFQINS, GGGVVIA and GVQIVYK hexamer were constructed based on the template of the X-ray structure 1YJP (Nelson et al., 2005). All MD simulations are all-atom explicit solvent and are performed at 298 and 448 K, respectively. A total of 750 ns trajectories were collected at 298 and 448 K, respectively, taking about 25,940 CPU hours on the in-house Xeon (1.86 GHz) cluster. Details of MD protocols, and choice of force fields are described in elsewhere (Chen and Luo, 2007; Chen, 2008).
The stability of hexamer GIFQINS, GGGVVIA and GVQIVYK 10 trajectories of 10.0 ns each were simulated at 298 K. The summary of simulations is provided in Table 1s (supplementary). The C␣ atom RMSDs are shown in Fig. 1s (supplementary). The average RMSD is about 25 Å for GVQIVYK, 10 Å for GGGVVIA, and 2 Å for GIFQINS at the end of 10 ns simulation. This suggests that the hexamers of GVQIVYK and GGGVVIA are unstable. This is consistent to the recent experimental observation (Sawaya et al., 2007). On the contrary, the one of GIFQINS is very stable. This is in agreement with the prediction of Eisenberg that the peptide GIFQINS from lysozyme can from amyloid fibril (Thompson et al., 2006). Therefore, the hexamer of GIFQINS was used to study the aggregation mechanism. The average distance within the hexamer is about 4.88 ± 0.39 Å for interstrand, and 10.07 ± 0.38 Å for intersheet throughout the simulations. This is similar to the experimental observations for peptide GNNQQNY (Nelson et al., 2005). To study the stability in detail, C␣ fluctuations are illustrated in Fig. 2. This indicates that all chains have common characteristics of small variation for the five central residues whereas large variations for the two end residues, suggesting that the center residues are more rigid than the residues in the termini regions. This is in agreement with the reported of Zheng et al. (2006) However, the fluctuation of Asn6 and Ser7 is larger than those of Gly1 and Ile2 for strands 1–3, and the fluctuation for strands 4–6 is reverse. A little twist for amyloid fibril during room temperature was found. This is in agreement with other simulation (Esposito et al., 2006). To further monitor the interaction responsible for the aggregation stability, native contacts and hydrogen bonds of interstrand and intersheet were studied. 5 stable hydrogen bonds were found for a couple of peptide of interstrand with populations higher than 60% (shown in Fig. 3). There are two types of native contact. One is the contact of interstrand, and the other is intersheet. The populations of native contacts for a couple of peptide of interstrand and intersheet in simulation are shown in Fig. 4. Six stable interstrand and three stable intersheet native contacts can be found with populations higher than 60%. This suggests that these native contacts of interstrand and intersheet should be major driving forces for the aggregation.
2.2. The Definition of Ordered Peptide There are two types of native contact: interstrand and intersheet among the hexamer. Interstrand was defined as the two parallel strands, and intersheet represented two antiparallel sheets. The native contacts are counted when the C␣ atoms residues are closer than 6 Å for interstrands, and when the center mass distance of side chain is less than 6 Å for intersheet native contacts. The number of native contacts is about 6 for interstrands, and 3 for intersheets, respectively. At high temperature, the most native contacts can disrupt. Therefore, the ordered peptide is defined as the native contacts are more than 2 for interstrands and 1 for intersheets at high temperature. According to the definition of ordered peptide, there is one form for hexamer and pentamer. For tetramer, there are two forms, one three strands in one sheet and the fourth on the other sheet, or each two in the same sheet (3-1 vs. 2-2). For trimer, there are also two forms, all three strands belong to one single sheet, or two strands in one sheet and the third on the other sheet (3-0 vs. 2-1). For dimer, two conformations are defined. Two strands are on one sheet or belong to different sheets (2-0 vs. 1-1). 2.3. Transition State Simulations Based on the definition of transition state (TS), 40 test MD runs for each candidate snapshot were performed to calculate the transition probability (P) (Chong et al., 2005; Gsponer and Caflisch, 2002; Pande and Rokhsar, 1999). TS simulations were done at 448 K in order to accelerate simulated aggregation/disaggregation rate. The detail methods are described in elsewhere (Chen and Luo, 2007; Chen, 2008).
H.-F. Chen / Computational Biology and Chemistry 33 (2009) 41–45
Fig. 3. The statistics of hydrogen bond.
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Fig. 6. Free energy vs. ordered number of peptide.
Fig. 7. Average transition state structures. A: TS1, B: TS2. (Green represents ordered peptide, red for disaggregation one). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.) Fig. 4. Native contact for interstrand and intersheet.
3.2. Disaggregation Kinetics Analysis In the following we first focus on disaggregation kinetics for cross- GIFQINS hexamer. Ordered number of peptides, i.e. number of peptides that form crystal-structure-like aggregates, is used to monitor disaggregation kinetics. Time evolution of the disaggregation at 448 K simulation is shown in Fig. 5. Overall, the peptide disaggregation can be represented well by two exponential functions, indicating that the process obeys second order kinetics. Our kinetics analysis shows that the first half-time is 1.01 ns, and the second half-time is 3.07 ns.
Fig. 5. Kinetics fitting for ordered number of peptides.
To understand the disaggregation pathway, the disaggregation landscape was analyzed with the progress variable of ordered number of peptides, and shown in Fig. 6. Besides the hexamer, there is one free energy-minima conformer corresponding to tetramer. This is in agreement with the kinetics analysis indicating the process is second order. Dimer is not thermodynamically stable conformation. A recent MD study by Zheng et al. (2006) suggested that dimer conformations of peptide GNNQQNY were unstable. For A1–42 , dimers are no observed by mass spectrometry or unstable with simulation (Bernstein et al., 2005; Urbanc et al., 2004; Wu et al., 2004). From monomer to tetramers, there is a barrier of 1.98 kT. From tetramers to hexamers there is a barrier of 0.060 kT. This suggests that tetramer can easily form hexamer. Note that the bar-
Fig. 8. RMSD of C␣ atom.
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H.-F. Chen / Computational Biology and Chemistry 33 (2009) 41–45
Fig. 9. Proposed aggregation pathway: (A) monomer, (B) tetramer and (C) hexamer.
rier is 0.90 kT from hexamer to tetramer. This suggests that there is higher barrier to disaggregate the fibrils to the intermediate state. Furthermore, there is a free energy-minimum intermediate state corresponding to tetramer. Nelson et al. hypothesized that the nucleus for the GNNQQNY aggregation is about four peptides (Nelson et al., 2005). Our simulation testifies this hypothesis. 3.3. Transition State Our kinetics analysis shows that disaggregation obeys second order kinetics. This suggests that hexamer disaggregates via a three-state process. Therefore, there are two transition states corresponds to the free energy maximum along the disaggregation pathways. We have scanned MD snapshots for two transition states (denoted as TS1 and TS2 below) structures in all 10 high-temperature trajectories at 448 K simulation. Transition probabilities vs. candidate snapshots for one of the trajectories are shown in Fig. 2s (supplementary). Overall 71 snapshots for TS1 and 62 snapshots for TS2 were found, respectively. Fig. 7 illustrates the average structures for all transition state snapshots for TS1 and TS2, respectively. Apparently from Fig. 7, TS1 is the aggregation of five peptides and TS2 is the state with three peptides. Nelson et al. hypothesized that the transition state complex on the pathway to the nucleus is approximately three peptides for GNNQQNY (Nelson et al., 2005). Our transition state analysis suggests highly similar result that the trimer is as the first transition state in aggregation for GIFQINS. For TS2, two strands are on the single sheet and third one is on other sheet (2 + 1 conformation). Strands 2 and 3 form a sheet and strand 6 enters into this sheet. There are 2 native contacts between strands 2 and 3, 3 native contacts between strands 2 and 6, and 1 native contact between strands 3 and 6. The result suggests that two monomers aggregate one -sheet, then add one monomer to form zipper transition state. 3.4. The Stability of Dimer, Trimer, Tetramer and Pentamer In order to conform the disaggregation kinetics, the stabilities at room temperature of dimer, trimer, tetramer and pentamer were studied. As shown in Fig. 8, the RMSDs were maintained at 3.1 ± 0.7 Å for tetramer (2-2) and 2.9 ± 0.4 Å for pentamer (32) within 10 ns, respectively, indicating significant stability of the structures of tetramer (2-2) and pentamer (3-2). For the model systems of dimer, the RMSDs increased quickly to ∼8 Å for dimer (1-1) and ∼12 Å for dimer (2-0) after 6 ns, which suggested that they lost their original dimer organization. This is consistent with the report of Zheng et al. for the dimer of GNNQQNY. For the model systems of trimer, the RMSDs were about 12 Å for trimer (2-1) and 8 Å for trimer (3-0) at the end of 10 ns. Furthermore, the RMSD of tetramer (3-1) gradually increased to 10 Å. These large RMSDs indicated that trimer (2-1, 3-0) and tetramer (3-1) are not thermodynamically stable state. The residue fluctuation of oligomers was shown in Fig. 3s (supplementary). The fluctuations of tetramer (2-
2) and pentamer (3-2) were the smallest among these oligomers. The significant stability of tetramer (2-2) suggests that the intermediate state is tetramer (2-2) and consistent with the result of disaggregation kinetics. For pentamer (3-2), it seems to have some conflicts with pentamer as TS2. However, the barrier is just 0.060 kT between tetramer and pentamer. Pentamer might also have high stability at room temperature. Collins et al. report that fibers grow by monomer addition (Collins et al., 2004). The possibility mechanism is that pentamer is aggregated by monomer added after the intermediate state is formed. 4. Discussion 4.1. Disaggregation Pathways and Likely Aggregation Pathways Finally if we assumed aggregation is the reverse of disaggregation, the proposed aggregation pathway is monomer, tetramer, and finally hexamer formation (shown in Fig. 9). For tetramer, there are strands 2, 3, 5, and 6 aggregating together. There are 2 native contacts between interstrands 2 and 3, and 3 native contacts between interstrands 5 and 6, respectively. For intersheets, there is 2 native contacts between strands 2 and 5, 3 native contacts between strands 2 and 6, and 1 native contact between strands 3 and 6, respectively. These native contacts can stabilize this intermediate state. TS1 is between tetramer and hexamer, and TS2 is between dimer and tetramer. 5. Conclusions In summary, our all-atom explicit solvent molecular dynamics reveals that the intermediate state during the aggregation of GIFQINS cross- spine is necessary and dissolution of the fibrils is unfavorable. The aggregation mechanism from our explicit solvent MD simulations is consistent with the prediction of Eisenberg and coworkers (Nelson et al., 2005). The important findings from this work are (a) dimer is not thermodynamically stable state, (b) dissolution of the fibrils is more difficult than aggregation, (c) tetramer (2-2) is the intermediate state and (d) two transition states are corresponding to trimer (2-1) and pentamer (3-2). Room temperature simulation of oligomers also suggests that dimer and trimer are not thermodynamically stable state, in agreement with the disaggregation kinetics. Acknowledgments This work is supported by National Natural Science Foundation of China (Grants No. 30770502 and 20773085). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.compbiolchem.2008.07.023.
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