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Dipartimento di Scienze Biochimiche, Università degli Studi di Firenze, 50134, Firenze, Italy
(RECEIVED October 17, 2005; FINAL REVISION January 5, 2006; ACCEPTED January 13, 2006)
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Abstract |
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-stacking of aromatic residues. We have studied the amyloid aggregation of several mutants of human muscle acylphosphatase in which an aromatic residue was substituted with a non-aromatic one. The aggregation rate was determined using the Thioflavin T test under conditions in which the variants populated initially an ensemble of partially unfolded conformations. Substitutions in aggregation-promoting fragments of the sequence result in a dramatically decreased aggregation rate of the protein, confirming the propensity of aromatic residues to promote this process. Nevertheless, a statistical analysis shows that the measured decrease of aggregation rate following mutation arises predominantly from a reduction of hydrophobicity and intrinsic 
-sheet propensity. This suggests that aromatic residues favor aggregation because of these factors rather than for their aromaticity. Keywords: assembly; aggregation mechanism; phenylalanine; molecular recognition; aromatic-aromatic interaction; 2,2,2-trifluoroethanol
Abbreviations: A, amyloid peptideAcP, human muscle acylphosphataseADA2h, activation domain of procarboxypeptidase A2 (human)CD, circular dichroismFTIR, Fourier transform infrared spectroscopySS-NMR, solid state nuclear magnetic resonanceTEM, transmission electron microscopyTFE, 2,2,2-trifluoroethanolThT, Thioflavin T
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Introduction |
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" X-ray diffraction pattern (Sunde and Blake 1997; Jiménez et al. 1999), and peculiar tinctorial properties upon binding with Congo Red and Thioflavin T (ThT) (Klunk et al. 1989; LeVine 1995). While it has been widely demonstrated that under appropriate conditions many, if not all, polypeptide chains can convert into amyloid-like fibrils (Guijarro et al. 1998; Chiti et al. 1999b), it is also clear that they do so with very different propensities. Therefore, an understanding of the parameters that modulate the aggregation propensity of a polypeptide chain and of the mechanism by which it forms fibrils is fundamental to gain insight into the pathogenesis of protein deposition diseases and to better understand the process of amyloid formation of polypeptide chains more generally.
A great effort has been expended in the past few years to predicting the key determinants of the aggregation propensity and the aggregation-prone regions of a given sequence. The hydrophobic content of a sequence has been suggested as a determinant of the aggregation rate of an unstructured polypeptide chain (Calamai et al. 2003; Chiti et al. 2003; DuBay et al. 2004). Other authors have shown that sequences designed to share an identical pattern of alternating polar and non-polar residues are able to promote aggregation into amyloid-like fibrils (West et al. 1999). Indeed, this particular pattern is highly prone to form -sheets, the underlying type of secondary structure observed in amyloid fibrils. The role of the propensity to form secondary structure has been extensively investigated by many other investigators. It has been shown that several proteins forming amyloid structures under physiological conditions present an 
-helix in a fragment that has rather a high propensity to form a -strand according to secondary structure predictions (Kallberg et al. 2001). Various mutants of the activation domain of human procarboxypeptidase A2 (ADA2h) designed to increase the local stability of the two helical regions have been found to be less prone to form fibrils and this is due to a decreased aggregation propensity of the unfolded state (Villegas et al. 2000). It has also been shown that destabilizing the -conformer of a given sequence is not enough to start the aggregation and that only an increase of the -sheet propensity can favor aggregation (Ciani et al. 2002).
Moreover, it has been demonstrated that a highly significant inverse correlation exists between the rates of aggregation in a set of protein mutants under denaturing conditions and their overall net charge, clearly suggesting the protein charge as a major determinant for amyloid formation (Chiti et al. 2002a). Accordingly, it has been demonstrated that the tetrapeptide KFFE is able to aggregate whereas KFFK and EFFE are not (Tjernberg et al. 2002). Taking into account the parameters mentioned above, some algorithms have been proposed to predict the effects of mutations on the aggregation rate of an unstructured polypeptide chain (provided that the mutation falls within the aggregation prone regions) (Chiti et al. 2003; Tartaglia et al. 2004), the absolute aggregation rate (DuBay et al. 2004), and the aggregation-prone regions of an unfolded protein (Fernandez-Escamilla et al. 2004; Pawar et al. 2005) solely on the basis of its primary structure.
It was also suggested that the presence of aromatic residues, particularly phenylalanine and tyrosine, promotes amyloid formation and stabilizes the resulting amyloid fibrils (Azriel and Gazit 2001; Chelli et al. 2002; Gazit 2002; Porat et al. 2003, 2004). Usually, the interactions formed by several aromatic ring planes that are parallel to each other are referred to as -stacking (Gazit 2002). A possible role of -stacking in protein aggregation has been initially prompted by the observation that substitution of Phe23 with alanine in the most amyloidogenic fragment of amylin (the fragment NFGAILSS, corresponding to residues 22–29) results in a dramatically decreased aggregation propensity (Azriel and Gazit 2001). A molecular dynamics simulation has suggested that Phe23 may potentially allow a coherent association between sheets by cementing the macromolecular assemblies due to its low conformational flexibility when interacting with other aliphatic residues (Zanuy et al. 2004). Given the high frequence of aromatic residues in aggregation-prone fragments deriving from disease-related proteins, it was concluded that aromatic residues play a major role in the molecular recognition, which is likely to be a fundamental step in amyloid formation (Gazit 2002). Based on these data, an algorithm which predicts the change of aggregation rate upon mutation has been developed by taking into account aromaticity as an additional parameter (Tartaglia et al. 2004).
As evidence was accumulating on the possible role of -stacking in amyloid formation, other authors have reported results that induce the importance of aromatic-aromatic interactions in amyloid assembly to be reconsidered (Tracz et al. 2004). A Phe to Leu substitution in the NNFGAILSS amylin fragment (residues 21–29) does not prevent aggregation and formation of fibrils as observed with Fourier transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM), and Congo Red staining (Tracz et al. 2004). The F23L variant of the NFGAIL fragment also results in amyloid formation at low and high pH values (Tracz et al. 2004). Substitution of the phenylalanine with an alanine results in a fragment that is less prone to form fibrils than the corresponding wild-type and F23L variants (Tracz et al. 2004). Aromatic residues are characterized by a high hydrophobicity and propensity to form -sheet structure. Both leucine and alanine residues are non-aromatic; the only difference is that leucine possesses a hydrophobicity and a -sheet propensity greater than those of alanine; based on these results it was proposed that aromatic residues only affect aggregation because of these features rather than for their ability to form -stacking. These conflicting reports raise the question as to whether aromaticity performs any role in the mechanism of amyloid formation and on the nature of the stabilizing interactions in the resulting amyloid fibrils.
To answer to these questions we have focused our attention on human muscle acylphosphatase (AcP), an / enzyme of 98 residues (Pastore et al. 1992; Stefani et al. 1997) whose sequence is shown in Figure 1A. Although AcP is not associated with any known human disease, it can form aggregates which show an extensive -sheet structure, as revealed by circular dichroism (CD) and FTIR, tinctorial properties typical of amyloid, such as a yellow-green birefringence under cross-polarized light in the presence of Congo Red and a high fluorescence in the presence of ThT and a fibrillar appearance as detected with TEM (Chiti et al. 1999b). Initially, when incubated in a solution containing moderate concentrations of 2,2,2-trifluoroethanol (TFE), AcP converts, on a time scale of a few seconds, into an ensemble of partially unfolded conformations with a far-UV CD spectrum indicative of extensive -helical content. Within 1–2 h the protein shows a transition from this state to an aggregated state that appears to be rich in -sheet structure, with increased fluorescence with ThT, but lacking evidence for extended fibrils. No structural models have been proposed for these aggregates and it is not still clear whether their strands are parallel or anti-parallel. After 1–2 mo electron micrographs show isolated as well as bundles of 3–5 nm wide protofilaments, which display Congo Red birefringence (Chiti et al. 1999b). This sequential appearance of species during AcP aggregation is shown in Figure 1B.
A protein engineering approach has allowed the regions of the sequence that promote the conversion of the partially unfolded ensemble into -structured aggregates to be determined (Chiti et al. 2002b). All the mutations that significantly alter the aggregation rate have been found in two regions of the primary structure corresponding to residues 16–31 and 87–98 (Fig. 1A). These two fragments correspond to two insoluble peptides when dissected from the remainder of the sequence (Chiti et al. 2002b). Moreover, they have values of -sheet propensity and hydrophobicity above the average values calculated from the entire AcP sequence (Chiti et al. 2002b) and appear to be solvent-exposed and/or flexible in the initial partially unfolded state (Monti et al. 2004).
The ability of AcP to convert into -structured oligomers and fibrils, under conditions in which the protein is initially in a partially unfolded state, and the presence of aromatic residues within the two segments that promote such conversion make AcP a good model-system to study the importance of aromaticity in amyloid aggregation. Here we have determined the aggregation rate for a series of single point mutants of AcP in which aromatic residues have been substituted with other residues. The data have been analyzed to assess the importance of aromatic residues in aggregation and to distinguish between aromaticity and other more generic effects in the possible aggregation-promoting action of these residues.
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Results |
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The rate of conversion of the TFE-denatured state into aggregates rich in -sheet and able to bind ThT was measured for each variant. This was achieved by incubating all variants separately in 50 mM acetate buffer, 25% TFE, pH 5.5, 25°C and monitoring the formation of aggregates using the ThT assay. Under these conditions wild-type and destabilized variants of AcP are known to denature rapidly on the time scale of a few seconds (Chiti et al. 1999b). The process of aggregation that is therefore monitored in the following minutes/hours consists in the conversion of the denatured ensemble into aggregates. The apparent change of aggregation rate following mutation is hence fully attributable to the effect of the amino acid substitution on this process rather than on the conformational destabilization of the native state.
Phenylalanine 22
Four mutants have been purified for this residue, with substitutions to leucine, alanine, serine, and arginine (Table 1). The results show that the substitution of the phenylalanine at this position results in a dramatically decreased aggregation rate (Fig. 2A). The kinetic constants measured for all of the four mutants are significantly lower than that of the wild type (Table 1). The most intense effect is observed for the F22R mutant, while the least effective decelaration is obtained when substituting phenylalanine with leucine. Mutations to alanine and serine result in similar effects, although substitution of the wild-type residue with a hydrophilic one displays a slightly greater change in the aggregation rate.
Tyrosine 25
This residue has been substituted with leucine, alanine, and serine. Results are similar to those obtained for phenylalanine 22, with all substitutions resulting in slower aggregation (Fig. 2B; Table 1). By comparing the Y25L and Y25S mutants we can see that the latter is much less prone to aggregate. Indeed, the kinetic constants vmut are reduced by factors equal to 7.4 and 33, respectively. Similar decelerations were obtained for the F22L and F22S variants.
Tyrosine 91
Tyr91 is located in the second aggregation-promoting fragment (Fig. 1A). For this residue we have collected the kinetic profiles for the Y91A, Y91Q, and Y91R mutants (Fig. 2C). We have also designed a Y91L mutant, but it was not analyzed due to purification problems. The results of the analysis of these mutants are shown in Figure 2C. The replacement of Tyr91 with a small hydrophobic residue or with a hydrophilic one results, within experimental error, in an identical effect (Table 1). Interestingly, the order of aggregation speed is similar for the variants involving Phe22 and Tyr91: Substitution of either residue to alanine and a hydrophilic group leads to decelerations that are fairly similar to each other and comparable in the two sets of variants. In addition, in both cases, replacement to arginine leads to the most remarkable deceleration effect, with the aggregation reaction reaching equilibrium after many days.
Tyrosine 98
The aggregation kinetic profiles for mutants Y98Q, Y98A, and Y98R were also recorded (Fig. 2D). As for tyrosine 91, the Y98L variant could not be purified due to aggregation into inclusion bodies after expression in Escherichia coli. Although all mutations resulted in slower aggregation, along the lines observed for the other sets of variants, mutation to glutamine results in a less marked deceleration compared to the mutation of Tyr98 to alanine and also of Tyr91 to glutamine (Table 1). Since tyrosine 98 is the C-terminal residue, it is possible that the C-terminal carboxyl group masks some of the effects observed for mutations at other positions. Apart from this effect, it is still evident that the most anti-amyloidogenic mutation is obtained if a charge residue is added, i.e., in the Y98R variant.
Statistical analysis
The results presented here clearly show that aromatic residues play a fundamental role in promoting aggregation of AcP. Indeed, in all cases elimination of an aromatic residue results in a significant, sometimes remarkable, decrease of the aggregation rate. Nevertheless, it is important to understand if aromatic residues favor amyloid formation because of their high hydrophobicity and propensity to form -structure, or by forming aromatic-aromatic interactions. To address this issue, we have compared the experimentally obtained aggregation rates with those calculated theoretically by using a previously described algorithm (Chiti et al. 2003). The equation is reported in the Materials and Methods as Equation 2 and allows the natural logarithm of the ratio of the aggregation rates for the mutant and wild type, ln(vmut/vwt), to be calculated. The equation contains terms for hydrophobicity, net charge and free energy variation when a residue changes its conformation from an -helix to a -strand. Moreover, the equation was derived by considering mainly mutations that did not involve aromatic residues (Chiti et al. 2003).
A summary of the experimental and theoretical values of ln(vmut/vwt) are reported in Table 1 and Figure 3. In Figure 3, we compare the data for the mutants which we have examined here (black circles) with those for other AcP mutants involving non-aromatic residues (white circles). These mutants have been previously used to develop the algorithm mentioned above (Chiti et al. 2003). Thus, if the experimental ln(vmut/vwt) values for aromatic mutants deviated from the expected behavior, our conclusion would be that aromatic residues play a role in amyloid formation different from that expected on the basis of their hydrophobicity and ability to form secondary structure. By using Equation 2 we notice that some experimental values of ln(vmut/vwt) are slightly lower or slightly higher than those calculated by considering only hydrophobicity, ability to form secondary structure, and net charge as parameters (Fig. 3). More importantly, the calculated aggregation rates of the mutants are not systematically higher than the observed values (Fig. 3). The data points for aromatic mutants are scattered around the line of best fit to a degree comparable with those of the non-aromatic variants. The p-parameter calculated for the best linear fit carried out with aromatic residues (continuous line) is lower than 0.05% (Fig. 3); the same result is obtained if the data from aromatic mutants are not included in the analysis (dotted line). A 
2 test has also been carried out for the data set. The test yields the probability to obtain the distribution if the studied phenomenon is well represented by the tested law. A probability value lower than 5% has been obtained when Equation 2 is used. This suggests that it is not necessary to add an aromaticity term in the equation to improve the correlation between experimental and theoretical values.
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Discussion |
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-sheet propensity-sheet propensity. On the one hand the side chains of phenylalanine and tyrosine contain seven carbons. These residues, particularly phenylalanine, are ranked as highly hydrophobic according to all scales of hydrophobicity so far edited (Creighton 1993). On the other hand, it has been shown that the avoidance of steric clashes between the side chain and its local backbone causes the -sheet propensity of aromatic residues to be particularly high (Street and Mayo 1999). The high occurrence of phenylalanine and tyrosine residues in natural -sheets of proteins also confirms the high propensity of these residues to form -structure (Chou and Fasman 1974). In addition to having a high hydrophobicity and a high propensity to form -sheet structure, phenylalanine and tyrosine residues also contain planar rings with six covalently bonded carbon atoms and a delocalised system. This chemical characteristic, termed aromaticity, has been proposed to be responsible for the ability of these residues to promote amyloid fibrillation.
Our results show that substitution of any of the four aromatic residues present in the aggregation promoting stretches of AcP invariably results in a slower aggregation process of the entire molecule (Fig. 2; Table 1). These observations confirm previous reports that phenylalanine and tyrosine side chains promote amyloid aggregation very effectively. However, our analysis rules out that aromaticity is responsible per se for the aggregating potential of these residues. An equation derived from mutations involving predominantly non-aromatic residues and considering physicochemical factors other than aromaticity can account for the observed reductions of aggregation rates when the aromatic residues of AcP are substituted with others (Fig. 3). The observed decelerations can be entirely attributed to the decrease of hydrophobicity and -sheet propensity (and increase of net charge if applicable) following substitution. The effectiveness of aromatic residues in promoting amyloid formation has probably to be sought in these, rather than other, chemical characteristics of their side chains. Importantly, the scales of hydrophobicity and -sheet propensity values for the 20 amino acid residues used in the algorithm were derived from partition coefficients between water and octanol (Creighton 1993) and effective stabilities when adopting a -sheet structure in monomeric proteins (Street and Mayo 1999), respectively. These factors and the resulting algorithm are not therefore indirectly influenced by -stacking or other types of aromatic-aromatic interactions.
Aromatic residues are frequent in the cross- core of fibrils but are not necessarily required
Although aromaticity does not seem to be the origin of the efficacy of aromatic residues to promote aggregation, the fact remains that they can establish a network of hydrophobic interactions and pay the minimal entropic cost in formation of the cross- structure. For these reasons they may have a significant stabilizing effect on the resulting fibrils and it is not surprising that they are highly recurrent in amyloidogenic sequences. X-ray studies have led to a detailed characterization of the crystals formed by assemblies of an amyloidogenic 12-mer peptide (Makin et al. 2005) and a 7-residue peptide derived from the yeast prion Sup35p (Nelson et al. 2005). In the first case stacking between phenylalanine residues is found to stabilize the intersheet packing of the structure (Makin et al. 2005), while in the second report tyrosine side chains stack on the solvent-exposed faces of the two sheets, presumably forming stabilizing interactions (Nelson et al. 2005).
Several peptides containing residues 16–20 of the amyloid peptide (A) readily form fibrils: The sequence of this fragment, KLVFF, presents two aromatic residues (Tjernberg et al. 1999). A search for the residues that promote the aggregation of the entire A peptide has shown the importance of Phe19 (Wurth et al. 2002). More interestingly, solid state nuclear magnetic resonance (SS-NMR) experiments have led some authors to a model of the A 1–40 protofilaments in which two -strands formed by residues 12–24 and 30–40 give rise to two in register parallel -sheets which interact through side chain-side chain contacts (Petkova et al. 2002). This structure clearly shows that Phe19 and Phe20 form inter-strand -stacking (Petkova et al. 2002).
The contacts between phenylalanine residues in fibrils formed from the amylin-derived NFGAIL peptide are thought to be important stabilizing interactions (Azriel and Gazit 2001; Gazit 2002; Porat et al. 2003, 2004). In the recently proposed structure of fibrils from full-length amylin any individual peptide molecule contributes to three -strands, each of which is parallel and in register with the corresponding ones from other molecules (Kajava et al. 2005). In this structure not just Phe23, but also Phe15, His18, and Tyr37 are stacked along the axis of the fibril to form long rows of intermolecular interactions (Kajava et al. 2005). Phe23 and Tyr37 form additional intramolecular interactions in each peptide. It was suggested that in the NFGAIL fragment the phenylalanine residue directs ordered -sheet stacking through both specific interactions between aromatic rings and non-specific clustering of phenylalanine with other hydrophobic residues (Wu et al. 2005). This suggests that the hydrophobic properties of this residue are able to favor the aggregation of the entire fragment without invoking its ability to form specific interactions.
If many peptides and proteins aggregate via interactions of aromatic residues, many others have been shown to promptly aggregate without any involvement of aromatic residues. A saturation mutagenesis analysis on the de novo designed amyloid peptide STVIIE has allowed an aggregation-prone sequence pattern to be determined (Lopez de la Paz and Serrano 2004). Although each position of this sequence can be mutated to aromatic residues without losing the ability of the hexapeptide to aggregate, the presence of aromatic residues does not seem to be an essential requirement for the aggregation of the hexapeptide (Lopez de la Paz and Serrano 2004). In -synuclein, a protein whose aggregation is related to Parkinson's disease, several short fragments of 
10 residues have been found to aggregate even if separated from the remainder of the sequence, for example the 71–82 (Giasson et al. 2001), 66–74 (Du et al. 2003), and 69–79 sequences (El-Agnaf and Irvine 2002). These three fragments, whose sequences largely overlap to each other allowing an aggregation-prone region of the entire protein to be identified, do not contain aromatic residues. It has been proposed that the 31–37 region of the A sequence is important in aggregation of the full-length A peptide as mutations to proline in this region cause a significant decrease in the stability of the resulting fibrils (Williams et al. 2004). Accordingly, other authors have shown that the fragment spanning approximately residues 30–38 gives rise to a -strand in fibrils (Petkova et al. 2002; Torok et al. 2002). This fragment does not contain aromatic residues.
Recently, the structure of the amyloid fibrils formed by HET-s from Podospora anserina has been investigated with SS-NMR, quenched hydrogen exchange and fluorescence (Ritter et al. 2005). The proposed structure shows two -strand-turn--strand motifs interconnected by a long loop (Ritter et al. 2005). Only -strand 4 contains one aromatic residue, Tyr281. This residue interacts with a non-aromatic residue from -strand 2 and does not form aromatic-aromatic interactions (Ritter et al. 2005). X-ray studies carried out on the crystals derived from assemblies of the amyloidogenic fragment 1–24 from barnase show that the various phenylalanine residues at position 7 from different molecules do not form intermolecular interactions (Saiki et al. 2005). They seem to participate to a specific pattern in which Phe7 and Val10 residues are alternating on one face of the sheet (Saiki et al. 2005). Similarly, a pattern of alternating Tyr13 and Ile4 is present on the other side of the sheet.
Conclusions
Our results on AcP confirm that aromatic residues have fundamental importance in amyloid assembly. Moreover, a survey of the amyloid fibril structures that have been determined with atomic or nearly atomic resolution in the past three years shows that the intermolecular interactions between the side chains of these residues appear to be highly frequent. However, the establishment of specific contacts between the aromatic rings of these residues is not an essential requirement to initiate aggregation and stabilize the resulting fibrils. When present, the forces that maintain in close contact the side chains of aromatic residues and stabilize the whole fibril do not arise from specific interactions involving the -electrons or the aromatic nature of these residues but, rather, from their high hydrophobicity and high tendency to form -sheets.
We believe that the elucidation of the precise role played by aromatic moieties in determining the mechanism of amyloid fibril formation, the rate by which this process occurs, and the stability of the resulting fibrils is fundamental to gaining a better understanding of the processes occurring in amyloidogenesis. Such an understanding would also be crucial for improving the accuracy of the existing algorithms in determining aggregation rates and aggregation-promoting regions within polypeptide sequences.
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Materials and methods |
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Aggregation kinetics with thioflavine T fluorescence
Aggregation kinetics of AcP and its variants were carried out as previously described (Chiti et al. 2002b). In brief, the reaction was started by diluting the native protein into a solution to reach a final concentration equal to 0.4 mg/mL in 25% (v/v) TFE, 50 mM acetate buffer (pH 5.5), and 25°C. At several times, 60 µL of the sample were added to 440 µL of 25 µM ThT, 25 mM phosphate buffer (pH 6.0), at 25°C. The resulting fluorescence was measured with a Perkin-Elmer LS-55 fluorimeter and thermostated with a Thermo-HAAKE F8 bath. Excitation and emission wavelengths were 440 nm and 485 nm, respectively. The resulting plot of the ThT fluorescence, expressed as percentage of the maximum value versus time, was fitted to a single exponential equation of the following form:
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| (1) |
where A is the ThT fluorescence at the apparent equilibrium, B is the change of ThT fluorescence during the exponential phase, v is the apparent rate constant, and t is the time. Before starting the experiment, the sample was centrifuged at 20,000g for 5 min and the protein concentration was measured using an 
280 value calculated as previously described (Gill and von Hippel 1989).
Data calculation
The change of aggregation rate upon mutation is reported as ln(vmut/vwt), where vmut is the experimentally obtained aggregation rate constant of the considered protein variant and vwt is the corresponding value for the wild type. For the calculation of the theoretical values of ln(vmut/vwt) the follwing equation was used (Chiti et al. 2003):
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| (2) |
where 
Hydr, Gcoil-, G-coil, and Charge are the change of hydrophobicity, -helical propensity, -sheet propensity, and charge upon mutation, respectively.
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Footnotes |
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Article and publication are at http://www.proteinscience.org/cgi/doi/10.1110/ps.051915806.
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Acknowledgments |
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References |
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) and for mutations to leucine (
), alanine (
), a hydrophilic residue (
). The insets show the first day of recording. The lines represent the best fits of the collected data to single exponential equations (see Materials and Methods, Equation 