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Similarities in the thermodynamics and kinetics of aggregation of disease-related A(1–40) peptides

¹ Leibniz-Institut für Altersforschung, Fritz-Lipmann-Institut, D-07745 Jena, Germany
² Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
³ Leibniz-Institut für Naturstoff-Forschung und Infektionsbiologie, Hans-Knöll-Institut, D-07745 Jena, Germany

(RECEIVED December 18, 2006; FINAL REVISION February 27, 2007; ACCEPTED March 7, 2007)

	Abstract

TOP Abstract Introduction Results Discussion Materials and Methods Conflict of interest Acknowledgments References

 
Increasing evidence indicates that polypeptide aggregation often involves a nucleation and a growth phase, although the relationship between the factors that determine these two phases has not yet been fully clarified. We present here an analysis of several mutations at different sites of the A(1–40) peptide, including those associated with early onset forms of the Alzheimer's disease, which reveals that the effects of specific amino acid substitutions in the sequence of this peptide are strongly modulated by their structural context. Nevertheless, mutations at different positions perturb in a correlated manner the free energies of aggregation as well as the lag times and growth rates. We show that these observations can be rationalized in terms of the intrinsic propensities for aggregation of the A(1–40) sequence, thus suggesting that, in the case of this peptide, the determinants of the thermodynamics and of the nucleation and growth of the aggregates have a similar physicochemical basis.

Keywords: conformational disease; kinetics; neurodegeneration; protein folding; Alzheimer's disease

	Introduction

TOP Abstract Introduction Results Discussion Materials and Methods Conflict of interest Acknowledgments References

 
Amyloid fibrils represent a specific group of polypeptide aggregates characterized by a highly ordered -sheet structure that gives rise to a fibrillar overall arrangement (cross- structure) (Dobson 2003; Selkoe 2003). These fibrils have been associated with aging and with several neurodegenerative conditions, such as Alzheimer's and Creutzfeldt-Jakob diseases (Dobson 2003; Selkoe 2003). Increasing evidence shows that, despite the generic nature of amyloid structures (Chiti et al. 1999; Dobson 2001; Fändrich and Dobson 2002; Fändrich et al. 2001; Krebs et al. 2004), the propensity to form aggregates is strongly influenced by the nature of the amino acid side chains along with the properties of the environment in which aggregation occurs (Chiti et al. 2003; DuBay et al. 2004; Fernandez-Escamilla et al. 2004; Tartaglia et al. 2004; Christopeit et al. 2005; Thompson et al. 2006). The physicochemical factors affecting such propensities have been mapped out experimentally through mutational studies for a range of systems where the effects on the rate of aggregation were examined (Chiti et al. 2002; Wurth et al. 2002; Christopeit et al. 2005; O'Nuallain et al. 2005). This property (termed here "growth rate," k_g ) reports on the efficiency by which the early oligomeric species of low molecular weight are extended into larger aggregates. This rate was shown to depend, in the case of unstructured polypeptide chains, on intrinsic properties such as -sheet and -helical propensity, hydrophobicity, charge, aromaticity, and sequential context, along with properties of the physicochemical environment, such as pH, ionic strength, and temperature (Chiti et al. 2003; DuBay et al. 2004; Tartaglia et al. 2004; Christopeit et al. 2005). These studies have provided the basis for empirical equations for the prediction of absolute or environment-dependent aggregation propensities (Chiti et al. 2003; DuBay et al. 2004; Tartaglia et al. 2004; Pawar et al. 2005). In the case of globular proteins in their native states, additional factors are involved that are associated with the conversion, at least locally, into unstructured species (Chiti et al. 2000; Fändrich et al. 2003).

Aggregation reactions normally consist of two consecutive phases: a lag phase in which a steady-state distribution of oligomeric species is established, and a growth phase in which oligomers larger than a critical nucleus size are extended to form much larger aggregates or fibrils (Harper and Lansbury 1997; Dzwolak et al. 2004). The growth rate measures the efficiency of the growth phase, whereas the duration of the lag phase, i.e., the lag time (t_l ), describes the time required for the nucleation reaction to take place. Recently, analysis of the aggregation behavior of mutants of the Alzheimer's A(1–40) peptide, in which residue 18 was systematically replaced with other amino acids, revealed that k_g and t_l are related properties (Christopeit et al. 2005). This observation suggests that, at this position of the sequence, similar specific physicochemical interactions are involved in both steps of the aggregation reaction (Christopeit et al. 2005). Within the set of A(1–40) variants of residue 18, a further correlation was observed between the kinetics and the thermodynamics of aggregation, in that those variants that aggregate very readily, as defined by the rate of aggregation, are also those that are thermodynamically most favorable for aggregation, as defined by critical concentration measurements (Hortschansky et al. 2005b). In this work, we show that similar effects are observable at several positions of the A(1–40) sequence, and not just for residue 18, thus implying that, in the case of this peptide, the physicochemical determinants of the nucleation and growth and of the thermodynamics of aggregation are similar.

	Results

TOP Abstract Introduction Results Discussion Materials and Methods Conflict of interest Acknowledgments References

 
In the present work, we study variants of the A(1–40) peptide in which single amino acid replacements are present at different positions of the sequence and, therefore, within very different sequential contexts. We examined the eight known natural variants of A(1–40), most of which are associated with familial Alzheimer's disease, namely H6R, D7N, A21G (termed the "Flemish" mutant), E22G ("Arctic"), E22Q ("Dutch"), E22K ("Italian"), and D23N ("Iowa"). The eighth variant (A2T) is a natural polymorphism with no apparent association with disease (Peacock et al. 1993). We illustrate the location of the mutated residues within a structural model of the A(1–40) peptide in the fibril (Fig. 1A; Petkova et al. 2002) and for the aggregation propensity profile of this peptide, calculated by using the Zyggregator method (Fig. 1B; Pawar et al. 2005).

View larger version (38K): [in this window] [in a new window]   Figure 1. Location of the mutations within the A(1–40) peptide. (A) Location of the mutated residues within the structural model of the A(1–40) peptide (Petkova et al. 2002). Note that residues 1–8 are added here for completion, while they are not part of the structural model. The model was constructed based on literature data (Petkova et al. 2002) using the program Insight and displayed using the program Rasmol. (B) Profile of the aggregation propensity (Zagg) (Pawar et al. 2005). Light-gray areas indicate the aggregation-prone regions, as defined by the height of the aggregation propensity (Zagg) per residue; dark-gray areas show the location of the mutated residues.

G_agg

The aggregates that we describe in this work are distinct from the simple protein precipitates that arise from oversaturation, which retain the native structure of their constituent proteins. Thus, aggregates are associated with a specific type of -sheet structure that may not be present in the native globular protein, and is defined by a regular pattern of intermolecular backbone hydrogen bonds. Such aggregates form under conditions of at least partial denaturation and consist of -sheets that are defined by specific infrared and X-ray diffraction properties and usually by their affinity for certain dyes (thioflavin-T and Congo red) (Jackson and Mantsch 1991; Fink 1998; Fändrich et al. 2001; Zandomeneghi et al. 2004; Christopeit et al. 2005). Beside occurring as amyloid fibrils, aggregates can also be nonfibrillar. In these cases, the -sheets and the overall aggregate structures possess no preferential orientations. However, nonfibrillar and fibrillar aggregates are distinguished here from filaments and oligomers formed by native, globular proteins, such as actin or tubulin fibrils. Under the conditions adopted for the present study, aggregates are formed in all samples analyzed here. These aggregates are characterized by their ability to bind ThT, thereby increasing the fluorescence of this dye. Furthermore, ThT-free samples exposed to the same incubation conditions show a high affinity for Congo red dye and they give rise to green birefringence in their Congo-red bound form when viewed in a polarizing microscope (Table 1). These properties are characteristics of polypeptide aggregates and amyloid structures (Westermark et al. 1999). However, none of these properties is exclusively specific to the formation of linear aggregates or fibrils (Christopeit et al. 2005). Consistent with these observations, we find by electron microscopy that only A21G, E22G, and D23N—among the mutational variants that we considered—form detectable amounts of well-ordered fibril structures (Table 1), while all other variants of the A(1–40) peptide produce only nonfibrillar aggregates. Since all peptides were disaggregated and filtered prior to analysis, these data suggest that the A21G, E22G, and D23N mutations have a particular strong tendency to form highly ordered nuclei leading to amyloid-like structures, while the other variants form less well-ordered nuclei and aggregates. However, the fibrils seen in A21G, E22G, and D23N samples are sparse, and most aggregates formed in these reactions are nonfibrillar. Hence, in further discussion we relate the presently determined thermodynamic and kinetic properties to aggregate formation without assuming specifically the formation of an amyloid-like cross- structure. In the case of mutations carried out at the single position 18 of A(1–40), at least, it was found recently that mutation affects the formation of amyloid fibrils and nonfibrillar aggregates in a similar manner (Peim et al. 2006).

View this table: [in this window] [in a new window]   Table 1. Aggregation properties of natural A(1–40) variants

G_agg

G_agg

G_agg

Next, we explored whether the effects of mutations are similar depending on their location within the sequences of the A(1–40) peptide. Comparison with equivalent mutants of position 18 shows a strong context dependence (Fig. 2), which affects not only the absolute values of k_g , t_l , or c_c but also the ratios k_g(X)/k_g(Z), t_l(X)/t_l(Z), or c_c(X)/c_c(Z) of a Z X replacement (Fig. 2). For example, there is a clear difference in the effect of an alanine to glycine mutation on the values of k_g(X)/k_g(Z), t_l(X)/t_l(Z), or c_c(X)/c_c(Z), depending on whether this replacement occurs at position 18 or at position 21, such as in the "Flemish" mutation (Fig. 2). In contrast, we find that replacement of alanine with threonine has very similar effects on k_g(X)/k_g(Z), t_l(X)/t_l(Z), and c_c(X)/c_c(Z), irrespective of whether it occurs at position 2, such as in the natural variant, or at position 18, which we generated in vitro (Christopeit et al. 2005; Hortschansky et al. 2005b). This context dependence is not obviously correlated with the aggregation propensity profile (Fig. 1B), for the latter indicates that both positions 18 and 21 are in a region that is very prone to aggregation. Although positions 2 and 18 occur in regions of the peptide that differ considerably in their contribution to the aggregation properties (Fig. 1B), the effect of Ala Thr mutations on k_g(X)/k_g(Z), t_l(X)/t_l(Z), or c_c(X)/c_c(Z) are rather similar at these two positions (Fig. 2).

View larger version (17K): [in this window] [in a new window]   Figure 2. Context dependence of the effects of mutations on the aggregation process. Comparison of the effect of a mutation of residue Z into residue X at different positions as indicated (top, natural A[1–40] variants) and at position 18 (bottom). Data are represented here as cc(X)/cc(Z) (white bar), tl(X)/tl(Z) (gray bar), and kg(X)/kg(Z) (dark-gray bar). Error bars show standard deviations. The data for valine 18 variants are taken from Christopeit et al. (2005) and Hortschansky et al. (2005b).

k_g = /t_l

k_g = /t_l

View larger version (41K): [in this window] [in a new window]   Figure 3. Correlation between Gagg , ln(kg ), and tl . (A,B) Plot of Gagg(X)/Gagg(Z) vs. ln[kg(X)/kg(Z)] (A) or vs. tl(X)/tl(Z) (B), where X is the mutant and Z the wild-type residue. (C) Correlation of ln[kg(X)/kg(Z)] and tl(X)/tl(Z). (D) 3D-correlation plot of the absolute values. Filled symbols: natural A(1–40) variants; open symbols: data from valine 18 variants of A(1–40). Fitting of the data points with a straight line gives coefficients of correlation of 0.67 for Gagg and kg , 0.82 for Gagg and tl , and 0.85 for tl and kg , irrespective of whether the kg , Gagg , and tl values are normalized to the wild-type values or not.

G_agg

G_agg

G_agg

G_agg

G_agg

In order to rationalize the dependence of the aggregation behavior on the physicochemical properties of A(1–40) caused by mutations, we compared these results with predictions of the changes in the intrinsic aggregation propensities in each case. These predictions are based on the computation of the Z_agg score, which represents the intrinsic aggregation propensity of a polypeptide chain. The Z_agg score is calculated by a method that exploits an empirical correlation between experimentally determined values for the growth rate k_g and intrinsic properties of the polypeptide chain, such as hydrophobicity, -helical and -sheet propensities, charges, or the presence or absence of distinctive bipolar patterns (Pawar et al. 2005). Indeed, when we compare the predicted aggregation propensity (Z_agg score) for the valine 18 variants examined previously (Christopeit et al. 2005) and the set of mutants studied here with the experimentally determined values for the growth rate k_g , we find a statistically significant correlation (Fig. 4A). Variants predicted to be particularly prone to aggregation also show fast growth kinetics (correlation coefficient 0.76). Next, we examined the relationship between the Z_agg score and the nucleation behavior expressed in terms of the lag time. We found a negative proportionality between t_l and the calculated Z_agg scores (Fig. 4B) with a correlation coefficient of –0.82 (Table 2), i.e., polypeptides with a short lag time are associated with high aggregation propensity values. Finally, Figure 4C shows the correlation between G_agg and the aggregation propensity predictions, represented by the Z_agg scores, which indicates that a large Z_agg score is generally associated with a mutational variant with a low G_agg value; the coefficient of correlation between G_agg and Z_agg is –0.66. This analysis strongly supports the idea that, despite the context dependencies described above, common physicochemical principles may determine both the thermodynamics and kinetics of aggregation, which are defined by the G_agg values and the length of the lag phase and the growth rate.

View larger version (22K): [in this window] [in a new window]   Figure 4. Correlation between the experimental measurements (kg , tl , and Gagg ) and the predicted aggregation propensities (Zagg): (A) kg and Zagg (coefficient of correlation 0.76), (B) tl and Zagg (coefficient of correlation –0.82), (C) Gagg , and Zagg (coefficient of correlation –0.66). Filled symbols indicate natural A(1–40) variants and open symbols the data from valine 18 variants of A(1–40).

	Discussion

TOP Abstract Introduction Results Discussion Materials and Methods Conflict of interest Acknowledgments References

 
We have presented a kinetic and thermodynamic analysis of the aggregation propensities of all known natural sequence variants of the Alzheimer's A(1–40) peptide and from experiments carried out under identical conditions. The experimental results have been rationalized through theoretical predictions of the aggregation propensities of these mutational variants. We have found that several of such variants (A2T, E22G, E22K) are significantly more aggregation-prone than the wild-type sequence (Table 1). In contrast, the Flemish mutant (A21G) produces somewhat mixed results. While the G_agg and t_l values of this variant are lower than those of the wild type, k_g values seems to be slightly higher. This observation is consistent with the previous notion that k_g values are more difficult to define experimentally and perhaps less reliable (Christopeit et al. 2005).

We have analyzed our results in light of literature data available for the natural variants of the A(1–40) peptide. Such comparisons are made difficult, however, by the sometimes extremely different environmental conditions and methods of investigation and by the use of peptides of different lengths or fragments. For example, the "Flemish" mutant (A21G) was reported to delay aggregation in A(1–40) (Walsh et al. 2001; Fernandez-Escamilla et al. 2004), while the "Arctic" mutation (E22G) was found to accelerate its fibrillogenesis (Nilsberth et al. 2001; Lashuel et al. 2003). These observations are consistent with the findings of the present study that used the oxidized forms of A(1–40). Furthermore, the present data confirm the previous observations that the "Dutch" mutation (E22Q) promotes aggregation or aggregate stability (Fraser et al. 1992; Van Nostrand et al. 2001; Bitan et al. 2003). Often, however, only single aggregation traces are reported for each of the mutants, and comparisons of different studies and of peptides of different lengths lead to a mixed outcome. For example, A(1–40) peptides carrying the "Italian" mutation (E22K) were reported to have aggregation propensities almost indistinguishable from the wild type (Miravalle et al. 2000), although this mutation seems to promote aggregation when inserted into A(1–42) (Murakami et al. 2002). In addition, the "Iowa" variant (D23N) was found to accelerate the formation of aggregates of the A(1–40) peptide (Lashuel et al. 2003), while a study of the A(1–42) peptide showed a kinetic behavior close to, or slower than wild type (Murakami et al. 2002). Although these results might be related to the different peptide lengths or different experimental setups, other mutants have similar effects when inserted into A(1–40) or A(1–42) or when using different conditions, for example, the "Flemish" (Walsh et al. 2001; Murakami et al. 2002) or the "Dutch" mutations (Fraser et al. 1992; Van Nostrand et al. 2001; Murakami et al. 2002; Bitan et al. 2003).

These apparently conflicting results could also be explained by the strong influence of stochastic factors that give rise to a discernible heterogeneity of the kinetic traces used to monitor the aggregation process (Hortschansky et al. 2005a; Pellarin and Caflisch 2006). Moreover, there is the general problem of comparing peptides from different sources (Zagorski et al. 1999). For these reasons, the values reported here are averages from 10 separate measurements, and all peptides were generated by using the same production and purification scheme. In our study, four natural variants aggregate faster than wild-type A(1–40), while the other four do not. These results strongly suggest that beside the intrinsic physicochemical factors promoting the aggregation propensity of disease-related polypeptide variants, further cellular factors must be considered to explain the origins of certain disease phenotypes. Consistent with this conclusion, the A2T variant aggregates much more readily than the wild type, although the intrinsically high aggregation propensity of this mutant is not known to give rise to familial Alzheimer's disease (Peacock et al. 1993).

Our previous analysis of 18 single-site mutants of position 18 of the A(1–40) sequence revealed a relationship between the two kinetic parameters k_g and t_l and also between these two properties and G_agg (Christopeit et al. 2005; Hortschansky et al. 2005b). As that study was limited to one position along the sequence of A(1–40), we have extended this analysis to the eight known natural single-site variants of A(1–40) in the present study. These data support the view that for the A(1–40) peptide, the determinants of the nucleation step are similar to those of the growth reaction in the aggregation process. Furthermore, those sequences that aggregate rapidly are also those that have the highest thermodynamic propensities to aggregate (G_agg ). These relationships seem largely unaffected by the position of the mutation. However, it is also clear that all peptides compared in the present study are very similar and arise from single-site replacements of the same host sequence, the A(1–40) peptide.

Comparison of the valine 18 variants with the present set of natural variants show differences in the extent to which the ratios k_g(X)/k_g(Z), t_l(X)/t_l(Z), or c_c(X)/c_c(Z) are affected by the mutation, suggesting their dependence on the structural context (Fig. 2). Such context dependences can arise either directly from the proximity of certain residues within the sequence or indirectly from different conformations, for example, depending on whether the replaced residue occurs within the center of a -strand, at its ends, or within a loop. Therefore, it is important to establish whether the correlation between k_g , t_l , or G_agg does still hold when comparing mutants at different positions in the sequence.

We have found that the changes in the thermodynamics of the A(1–40) peptide caused by a mutation, measured through G_agg , correlate well with the predicted changes in the intrinsic aggregation propensity, Z_agg. In addition, the Z_agg scores correlate well with the changes in the lag times and growth rates. This is a surprising result, because the algorithm developed to calculate the Z_agg scores was derived from experimental growth rates; it indicates, therefore, that the factors governing the lag time are the same as those determining the growth rate, at least for A(1–40). Since the oligomeric species present in the lag phase appear to be the species responsible for high toxicity (Bucciantini et al. 2002; Silveira et al. 2005; Lesne et al. 2006), it is tempting to speculate that these results could contribute to our understanding of the neurotoxicity of different peptides. Comparison of the two kinetic parameters and also the G_agg values with a variety of physicochemical properties suggests that within the precision of the measurement, hydrophobicity has the greatest relative influence on t_l and, therefore, on the nucleation reaction (Table 2). This result would imply that hydrophobic interactions are particularly important for the association of peptide molecules onto pre-existing nuclei or for the initial interactions of the soluble species, leading to nucleus formation, resembling perhaps the hydrophobic collapse reaction known from native protein-folding reactions (Miranker and Dobson 1996).

The critical nucleus size is defined as the number of molecules capable of forming an oligomer in which the enthalpy gain in forming intermolecular interactions balances the entropy loss in losing diffusional mobility. In principle, the interactions stabilizing the oligomer need not be specific and they can be different from those responsible for growth, since the latter are likely to involve specific networks of hydrogen bonds in highly organized structures. We have shown that targeted mutations have the same effect on nucleation and growth (Fig. 3), thus strongly suggesting that despite variations, the same key interactions are involved in both processes. Kelly and colleagues recently suggested that the interaction of just two molecules is rate limiting in the aggregation process of the A(1–40) peptide (Bieschke et al. 2005). Although more quantitative estimations are necessary, a critical nucleus of aggregation formed by two or three molecules would suffice, in principle, to provide a molecular mechanism that explains the observed effect. These observations would suggest that the same type of association between peptide pairs during growth also plays an important role in the nucleation process, while the formation of larger critical nuclei would involve more complex processes, resulting in distinct effects of mutations on the nucleation and on the growth steps.

Conclusions
We have shown here that the in vitro aggregation kinetics and thermodynamics of mutational variants of the A(1–40) peptide are highly correlated and that it is possible to rationalize them based on intrinsic properties of the polypeptide chain. In addition, the context dependence that we found for specific amino acid replacements indicates the importance of the structure of the oligomeric species along the aggregation pathway. Therefore, it will be interesting to test whether the relationships that we found here between lag times, growth rates, and the thermodynamics of aggregation would also be found for polypeptide sequences differing in their hydrophobicity and charge distributions from the ones examined here.

	Materials and Methods

TOP Abstract Introduction Results Discussion Materials and Methods Conflict of interest Acknowledgments References

 
Mutagenesis and oxidation of A(1–40)
Mutagenesis, expression, and purification of the A(1–40) peptide variants were performed as described previously (Christopeit et al. 2005; Hortschansky et al. 2005a). Based on a Coomassie-stained protein gel, the purity of the peptide was estimated to be better than 95%. Pure peptides were oxidized by incubation of 5 mg/mL of the peptide in 3% H₂O₂ for 4 h at 20°C (Christopeit et al. 2005). The oxidized peptide was repurified with reversed-phase chromatography and lyophilized. The identity of all peptides was verified by mass spectrometry after oxidation.

Polarization and electron microscopy analysis
Congo-red staining of fibril solutions dried on glass slides was performed according to published methods (Christopeit et al. 2005) and analyzed using a Leica DM/DR x450 polarizing microscope with parallel (bright field) or crossed polarizers (dark field). Electron microscopy was carried out of samples from the kinetic analysis after 4 d of incubation as described previously (Fändrich et al. 2003), using an EM 400 T Philips electron microscope.

Kinetic and thermodynamic aggregation measurements
All kinetic data were recorded at 37°C using a BMG FLUOstar Galaxy reader (BMG) and black-wall 96-well plates (Nunc). A detailed description of these methods can be found elsewhere (Christopeit et al. 2005; Hortschansky et al. 2005a). The samples contained 120 µM A(1–40) peptide, 20 µM ThT, and 10 mM sodium azide solution in 50 mM sodium phosphate buffer (pH 7.4). The aggregation kinetics were recorded experimentally for 10 individual samples of each mutational variant. From each experimental trace, the two kinetic parameters were extracted, and the values then averaged over all traces to give the reported values k_g and t_l . For analysis of the critical concentration, peptides were dissolved in 50 mM sodium phosphate buffer (pH 7.4) at 1.0–1.5 mg/mL concentration and UV sterilized by irradiation at 254 nm for 1.5 h. Incubation at 37°C was carried out for 28 d in oil-free tubes (MoBiTec) and terminated by spinning down the sample for 30 min at 120,000 rpm (513,000g) and 4°C in a Beckman TLA-100 tabletop centrifuge using a TLA-120.2 fixed angle rotor. After centrifugation, the supernatant was taken off and the soluble peptide fraction was determined using a Micro-BC-Assay (Interchim). The latter was calibrated with a standard of wild-type A peptide that was prepared based on the optical absorption at 280 nm (Pace et al. 1995; Hortschansky et al. 2005b). Measurements were carried out in the standard range, where linear regression produces a correlation of better than 0.95. The critical concentration c_c is converted into the Gibb's free energy of aggregation, G_agg , as described by the simple formula G_agg = –RT ln (1/c_c) (Oosawa and Asakura 1975).

Aggregation propensity prediction
We used the Zyggregator algorithm (available for academic use at http://www-vendruscolo.ch.cam.ac.uk/zyggregator.php) to analyze the behavior of the A(1–40) peptide in terms of its physicochemical properties (Pawar et al. 2005). The intrinsic aggregation propensity score, Z_agg, was calculated for the wild-type sequence and all experimentally characterized mutants.

	Conflict of interest

TOP Abstract Introduction Results Discussion Materials and Methods Conflict of interest Acknowledgments References

 
The authors claim a conflict of interest arising from a commercial partner that precludes free distribution of any DNA constructs described here. Basis vectors are available from commercial vendors.

	Footnotes

 
Reprint requests to: Marcus Fändrich. Leibniz-Institut für Altersforschung, Beutenbergstraße 11, D-07745 Jena, Germany; e-mail: fandrich{at}fli-leibniz.de; fax: 49 3641 656310; or Michele Vendruscolo, University of Cambridge, Department of Chemistry, Lensfield Road, Cambridge, UK; e-mail: mv245{at}cam.ac.uk; fax: 44-1223-763418.

Abbreviations: A, amyloid- peptide; c_c , critical concentration; CR, Congo red; G_agg , Gibb's free energy of aggregation; EM, electron microscopy; k_g , growth rate; ThT, thioflavin-T; t_l , lag time; Z_agg, calculated propensity of aggregation.

Article and publication are at http://www.proteinscience.org/cgi/doi/10.1110/ps.062734207.

	Acknowledgments

TOP Abstract Introduction Results Discussion Materials and Methods Conflict of interest Acknowledgments References

 
This work was supported by the Deutsche Forschungsgemeinschaft (DFG). M.F. acknowledges a BioFuture grant from the Bundesministerium für Bildung und Forschung (BMBF). The research of M.V. and C.M.D. is supported by the Leverhulme Trust. We thank S. Fricke, U. Knüpfer, and R. Wenderoth for technical assistance as well as M. Nache for molecular modeling.

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