School of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400 076, India
1 To whom correspondence should be addressed. E-mail: balaji{at}iitb.ac.in
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Abstract |
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Keywords: aliphatic index/discordant stretch/instability index/sheet propensity/thermostability
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Introduction |
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Several factors have been found to contribute to amyloid formation. These include high protein concentration, proteolysis, mutations, local change in pH at membranes, oxidative or heat stress (Zerovnik, 2002; DuBay et al., 2004
) and the presence of molecules such as serum amyloid P component, metal ions, apolipoprotein E and proteoglycans (Fink, 1998
). Nevertheless, the primary structure of the protein is critical since the propensity of the protein to form fibrils is ultimately dictated by its amino acid sequence (Fink, 1998
; Zanuy and Nussinov, 2003
). Some of the sequence-related parameters that have been implicated in deciding the rate of aggregation of a polypeptide are hydrophobicity, hydrophobichydrophilic patterning, charge (Chiti et al., 2003
; DuBay et al., 2004
), high ß-sheet propensity (Kallijarvi et al., 2001
; Tjernberg et al., 2002
) and low ß-turn propensity (Kallijarvi et al., 2001
). However, the contribution/importance of these factors to fibril formation varies among proteins. For example, sequence-dependent factors such as secondary structure propensity, peptide length, pI and hydrophobicity were found not to affect the amyloidogenecity of ß2-microglobulin peptides; only the high content of aromatic side chains was found to be the major determinant (Jones et al., 2003
). This is a possible reason for the absence of any prominent sequence or structural characteristics among the known human amyloidogenic proteins (Sipe, 1992
; Horwich, 2002
).
Proteins that form inclusion bodies on overexpression in Escherichia coli and those that form amyloids share certain similarities. Amyloid and inclusion body formation, being different manifestations of aggregation phenomenon, are influenced by factors such as increased protein concentration, nature of folding intermediates and in vivo half-life of the protein (Fink, 1998; Horwich, 2002
). Amyloid fibrils have a lower helical content and higher sheet content than the corresponding native protein (Kallberg et al., 2001
). Similar secondary structural changes have also been observed in the inclusion bodies of some proteins (Przybycien et al., 1994
). Another feature shared by these proteins is their sensitivity to point mutations (Wetzel, 1994
; Villegas et al., 2000
; Horwich, 2002
). Certain mutations which alter the aggregation propensity of the proteins can be explained based on hydrophobicity, solvent accessibility and charge of the amino acid residues involved in the mutation (Dale et al., 1994
; Malissard and Berger, 2001
; Chiti et al., 2003
; Monti et al., 2004
).
The present study was aimed at delineating the relationship of the primary structure of a protein to its amyloid-forming propensity and at predicting the potential transition-prone helices within the protein. Towards this objective, datasets of proteins were created to represent amyloid forming proteins (dataset A), intrinsically unstructured proteins (dataset U), inclusion body forming proteins (dataset I) and proteins which are soluble on overexpression in E.coli (dataset S) (Table S1, available as Supplementary data at PEDS Online). The sequences of the proteins in these datasets were analyzed to identify features that are unique to amyloidogenic proteins. It was found that that the amyloidogenic proteins are enriched in order-promoting residues with high sheet propensity and have low thermostability and increased in vivo half-life. Based on these features, an index AP was heuristically determined. The correlation of AP with amyloidogenic propensity was established by predicting the amyloidogenicity of peptides that have been experimentally shown to form amyloids (dataset P) and by rationalizing the reported effect of mutations on amyloid formation.
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Methods of analyses |
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PubMed, available at the National Center for Biotechnology Information (www.ncbi.nlm.nih.gov), was used to access the MEDLINE bibliographic database. The DSSP secondary structure assignments of proteins with known three-dimensional structures were taken from the DSSP database (Kabsch and Sander, 1983). NCBI and Swiss-Prot databases were used for procuring protein sequences for the various datasets created in this study. The software SPSS 10.0 (www.spss.com) was used to perform discriminant analysis and the KruskalWallis test.
Creation of datasets
Fifty-four amyloid-forming proteins were identified from PubMed and, of these, 36 were randomly chosen to constitute the training dataset (dataset A; Table S1) and the remaining 18 constitute the test dataset (dataset Atest). Similarly, datasets of natively unstructured proteins, datasets U (training; 36 proteins) and Utest (test; 18 proteins) were created. Proteins which are soluble on overexpression in E.coli constitute datasets S (training; 27 proteins) and Stest (test; 13 proteins). The datasets I (training; 115 proteins) and Itest (test; 57 proteins) consist of proteins that form inclusion body on overexpression in E.coli. The training datasets were used to identify the factors significantly varying amongst them and the test datasets were used to validate the observations made from the analysis on the training datasets. The sizes of the training and test datasets are in an 2:1 ratio and the proteins were segregated into these two datasets randomly. The NCBI accession numbers of proteins in all the above datasets are given in Table S1. Dataset P consists of 133 peptides experimentally shown to form amyloid fibrils (Table S2) and this dataset was used as a test dataset. Dataset SP consists of 162 780 proteins deposited in the Swiss-Prot database as of October 11, 2004 (Release 45.7).
Calculation of instability index
The instability index of a protein IIP was calculated as
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Calculation of aliphatic index
The aliphatic index AI was calculated as
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Calculation of the frequencies of occurrence of amino acid residues
The frequency of occurrence FA,X of an amino acid residue A in a given dataset X was calculated as
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Calculating the net orderliness of a protein
The disorder-promoting amino acids are Ala, Arg, Gln, Glu, Gly, Lys, Pro and Ser and the order-promoting amino acids are Asn, Cys, Ile, Leu, Phe, Trp, Tyr and Val (Uversky et al., 2000; Williams et al., 2001
; Tompa, 2002
). The net orderliness of a protein ORD is calculated as the difference between the frequencies of occurrence of the order- and disorder-promoting residues in the protein.
Calculating the helix and sheet propensity of tripeptides
The propensity PXYZ,SS of a tripeptide XYZ to be present in the secondary structure type SS (SS is for
-helix and ß for ß-strand) was calculated as
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Calculating the sheet propensity of a protein based on the secondary structure propensity of the constituting tripeptides
The tripeptide (TP)-based sheet propensity of a protein SPTP was calculated as
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Results and discussion |
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Residues Ala, Arg, Gln, Glu, Gly, Lys, Pro and Ser have been classified as disorder-promoting amino acids whereas the amino acids Asn, Cys, Ile, Leu, Phe, Trp, Tyr and Val as order-promoting amino acids (Uversky et al., 2000; Williams et al., 2001
; Tompa, 2002
). Among the order-promoting residues, Asn, Cys, Trp and Tyr occur more frequently in proteins of datasets A and I compared with proteins of dataset S (Table I). As reported earlier (Williams et al., 2001
), dataset U proteins are enriched in disorder-promoting residues (Table I). Proteins of dataset S do not show any significant bias in their amino acid composition with regard to order- or disorder-promoting amino acids.
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Instability index
The instability index of proteins that have an in vivo half-life of <5 h was shown to be >40. For proteins whose instability index is <40, the in vivo half-life was shown to be >16 h. Hence this index could be used to compare the metabolic stabilities of two or more proteins (Guruprasad et al., 1990).
The average instability index IIP of the proteins of datasets A and I is lower than that of dataset S (Table II). Intrinsically unstructured proteins (dataset U) are reported to be highly sensitive to protease action and to have a low in vivo lifetime (Tompa, 2002); this is reflected in their high average values of IIP. Hence dataset A and I proteins seem to have a longer in vivo half-life compared with dataset S proteins.
This inference gains significance from the observation that the lifetime of partially folded intermediates influences the propensity of the protein to aggregate. Longer lived partially folded intermediates are known to have a greater chance of interaction with other partially folded intermediates; they would also exhaust the available molecular chaperones that otherwise prevent protein aggregation by interacting with them in an in vivo system (Fink, 1998; Rosen et al., 2002
).
Aliphatic index
The aliphatic index AI of proteins of thermophilic bacteria has been found to be higher and the index can be used as a measure of thermostability of proteins (Ikai, 1980). This index is directly related to the mole fraction of Ala, Ile, Leu and Val in the protein (Ikai, 1980
). The mean aliphatic index AI of proteins of datasets A, I and U are lower than that of the proteins of dataset S (Table II). However, it should be noted that the residues Ile, Leu and Val are both order-promoting and involved in increasing the aliphatic index and therefore a decrease in AI of the dataset may reflect in a decrease in the ORD (e.g. dataset A) and vice versa.
Apart from the AI, a few other parameters have also been associated with an increase in thermostability, namely a higher ratio of (Glu + Lys) to (Gln + His) (Farias et al., 2004) and a higher content of Glu, Lys and Arg and fewer uncharged polar residues (Ser, Thr, Asn and Gln) (Haney et al., 1999
). As expected, the (Glu + Lys)/(Gln + His) ratio is higher in dataset S proteins (2.0) than proteins of dataset I (1.9) or A (1.5). Although the AI of dataset U proteins is lower, the above ratio is higher (2.3) than for dataset S proteins because of their high content of Glu and Lys residues. The content of Arg residues is also lower for datasets A and I than dataset S. The contents of Thr, Asn and Gln are higher for datasets A, U and I than dataset S (Table I). These observations suggest that the aggregation-prone datasets A and I are more thermolabile than dataset S proteins. In fact, thermolabile folding intermediates have been suggested to contribute to protein aggregation by exhausting the in vivo supply of chaperonins (King et al., 1996
).
Identification of features that correlate with the amyloidogenic propensity of a protein
The KruskalWallis test is a non-parametric test that can be used to compare values from three or more datasets. The AI, SPTP, ORD and IIP values of the datasets A, U, I and S were compared using this test. In addition to the above factors, the net charge (NC) of the protein was also included, since the role of charge of the protein on amyloid formation has also been reported (Chiti et al., 2003). The results of this test show that the parameters AI, SPTP, ORD and IIP, but not the net charge, vary significantly between the datasets A, U, I and S (Table S3). The average aliphatic index AI was plotted against the average instability index of the protein IIP, the average tripeptide-based sheet propensity SPTP and the mean orderliness ORD for all the datasets and a linear regression trendline was fitted using Microsoft Excel (Figure S1; panels AC; available as Supplementary data at PEDS Online). The datasets A and I are on the same side of the trendline whereas the dataset U is on the same side of the trendline as dataset S. However, when the mean orderliness ORD is plotted against the mean tripeptide-based sheet propensity SPTP (Figure S2; panel D), dataset U falls in the category of aggregation-prone proteins. This is understandable since most of the proteins of dataset U are shown to be amyloidogenic, although they share many features of dataset S proteins. These observations suggest that the parameters AI, SPTP, ORD and IIP relate to the amyloidogenic/aggregation behavior of proteins, since, in all these four cases, the datasets A and I clearly fall on the same side of the trendline opposite to the side occupied by dataset S. Dataset U proteins share commonalities between aggregation prone (datasets A and I) and soluble proteins (dataset S).
Discriminant analysis
The proteins of the datasets A, Atest, U, Utest, I, Itest, S and Stest (Table S1) were pooled and were represented by the factors AI, SPTP, ORD, IIP and NC. Discriminant analysis by the stepwise method was performed to validate the correlation between these variables and the amyloidogenic propensity. The prediction accuracy was determined by the leave-one-out cross-validation. The analysis was done in two ways. (1) Comparing the 54 amyloidogenic proteins (i.e. datasets A and Atest) with 500 randomly chosen proteins from the Swiss-Prot database, 35 were classified as amyloidogenic (Table S4). Except for the net charge, all the other four factors were considered to be significant for classification. (2) A four-class classification was performed by considering the amyloidogenic proteins (datasets A and Atest), intrinsically unstructured proteins (datasets U and Utest), inclusion body-forming proteins (datasets I and Itest) and proteins soluble on overexpression in E.coli (datasets S and Stest). As in the binary classification, all four factors except net charge were considered to be significant for classification. However, only 29 of the 54 amyloidogenic proteins (datasets A and Atest) were classified as amyloidogenic (Table S4). The lower classification accuracy of the four-class method compared with the binary classification is attributable to the fact that three of the four classes considered for classification are aggregation-prone, viz. datasets A, U and I.
The classification function obtained by discriminant analysis cannot be used for the prediction of amyloidogenic proteins since the datasets are of small size and are also not normally distributed. Nevertheless, it can be inferred that the factors AI, SPTP, ORD and IIP vary significantly among the datasets considered in this study.
APan index for amyloidogenic propensity
A heuristic approach was undertaken wherein the amyloidogenic propensity of a protein or a peptide, denoted by the index AP, is correlated with the parameters AI, SPTP, ORD and IIP. AP is calculated as follows:
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A protein/peptide is inferred to have a high propensity for amyloid formation if AP > 0. It was observed that 75, 53 and 33% of proteins in the datasets A, U and I, respectively, are predicted to be amyloidogenic. None of the proteins of the dataset S were predicted to have a high propensity for amyloid formation; 32% of the proteins deposited in the Swiss-Prot database were predicted to be amyloidogenic.
Validation of AP as an index for amyloidogenic propensity
AP-based predictions were done on the test datasets to validate the use of AP as an index for amyloidogenic propensity. As expected, datasets Atest and Utest have a higher percentage of amyloidogenic proteins (72 and 83%, respectively); 21% of dataset Itest proteins are predicted to be amyloidogenic whereas none of the dataset Stest proteins are predicted to be amyloidogenic. Of the 133 peptides shown experimentally to form amyloid fibrils (dataset P), 77% are predicted to be amyloidogenic (Table III). The amyloidogenicity of peptides predicted for a protein could be ranked based on the AP index. A peptide with a higher AP index would be predicted to be more amyloidogenic than a peptide with a lower value of AP.
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It has been observed that the intrinsic effects of mutations on the rates of aggregation of unfolded polypeptides can be correlated with changes in physicochemical properties such as hydrophobicity, secondary structure propensity and charge (Chiti et al., 2003). A total of 40 point mutants which display a difference in the rate of aggregation relative to the wild-type protein were considered for validating the predictions based on AP; the change in the rate of aggregation was predicted correctly for 33 of the 40 point mutations; an increase or decrease in amyloidogencity accompanying these mutation was reflected in their AP index (Table S5).
Amyloidogenicity is also dictated by structural features of the protein such as the helix stability (Andreola et al., 2003), ß-strand stability, presence of unsatisfied hydrogen bonds, buried uncompensated charges, solvent accessibility of the amyloidogenic peptide/strand (Thirumalai et al., 2003
), ß-turn propensity (Kallijarvi et al., 2001
) and the secondary structure of the peptide in the soluble state (Soto et al., 1995
). The nature of the folding intermediates (Horwich, 2002
) and the presence/absence of various kinds of interactions in an oligomeric intermediate involved in fibril formation such as hydrogen bonding, electrostatic and hydrophobic interactions also play a crucial role in dictating the aggregation propensity/rate of a polypeptide (Horwich, 2002
; Thirumalai et al., 2003
). In addition, experimental conditions also contribute to deciding the amyloidogenicity of a peptide (DuBay et al., 2004
). For these reasons, a quantitative correlation between amyloidogenecity and the AP index has not been attempted at this stage. The inclusion of the secondary structure information in the AP index will enhance the prediction accuracy; however, this needs the availability of the 3-D structures of many more amyloidogenic proteins.
Prediction of transition-prone helices within a protein
The heuristic approach was extended to predict the transition-prone helices within proteins with known secondary structure, since a helix to ß-strand transition is seen to be frequently associated with amyloid formation (Kallberg et al., 2001; Forloni et al., 2002
). The protein is scanned for such helices with a seven-residue sliding window and a heptapeptide is identified as a potential amyloidogenic helix if the following four conditions are satisfied:
When a protein is predicted to have more than one putative transition-prone helix, the helix with the highest value of SPTP is considered as the critical transition-prone helix for that protein. It might be possible that the additional helices picked up may also be potential amyloidogenic sites. Here, the size of the sliding window considered for the prediction is optimized to seven residues. If the size of the window is reduced or increased from seven residues, it is seen that the number of predicted potential helices increases or decreases steeply (data not shown).
The proteins wherein a helical region has been shown experimentally to from amyloid fibrils (the data on such helical fragments are limited) or those that have been predicted to have such discordant helices (the protein is shown to form amyloid fibrils) were analyzed to predict the transition-prone helices. All nine proteins showed the presence of transition-prone helices and, in seven of the eight proteins, the transition-prone helices predicted to be critical in amyloid formation were part of the previously identified /ß discordant stretch (Kallberg et al., 2001
; Table S6). The transition-prone helices were also identified correctly for all three proteins whose amyloidogenic helices have been identified experimentally (Table S6).
The N-terminal region of human amylin peptide has been found to influence the overall kinetics of fibril formation of the peptide (Goldsbury et al., 2000). The N-cap residues play a role in helix stabilization (Serrano and Fersht, 1989
; Parker and Hefford, 1998
; Iqbalsyah and Doig, 2004
). It is probable that the presence of residues having a higher sheet propensity at the N-cap positions initiates a helix to sheet transition under denaturing conditions. This assumption comes in the light of the observation that a helix to sheet transition accompanies amyloid formation (Kallberg et al., 2001
) and mutations that decrease the stability of individual helices in proteins are associated with prion diseases (Kazmirski et al., 1995
). Fibril formation was also observed to be inhibited by association of the amyloidogenic peptide with ligands that could prevent a helical segment from forming a ß-strand (Andreola et al., 2003
).
Intrinsically unstructured proteins and proteins overexpressed in the soluble form share common features
Proteins belonging to datasets S and U display certain similarities. Both of them have higher net negative charge (Uversky et al., 2000; Tompa, 2002
; data not shown) and are rich in residues with a higher helix propensity (Table I). Proteins of datasets S and U have a higher mean instability index IIP (40 and 46, respectively; Table II) than proteins of other datasets. The higher instability index of datasets S and U suggest that these proteins have a lower in vivo half-life than proteins of other datasets. It has been reported that intrinsically unstructured proteins have a high proteolytic sensitivity and hence a lower in vivo half-life (Tompa, 2002
). However, a feature of dataset U proteins that is not shared by dataset S proteins is the low average aliphatic index as reflected by the lower frequencies of occurrence of Ala, Ile, Leu and Val in dataset U proteins than dataset S proteins; in this respect, dataset U proteins are similar to datasets A and I proteins (Table I).
Conclusions
The aim of this study was to understand the sequence characteristics of proteins that are prone to amyloid formation. The analyses reveal that amyloidogenicity is correlated with thermostability, in vivo half-life and the presence of order-promoting residues with high sheet propensity. These parameters were used to define a prediction function (AP index) for amyloidogenicity. It was observed that 72% of the known amyloidogenic proteins and 77% of the peptides shown experimentally to form amyloid fibrils were correctly classified based on the AP index. The prediction accuracy can be increased by considering other features, such as the nature of the folding intermediate and the environmental conditions. However, to date such information is available for only a few amyloidogenic proteins.
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Acknowledgements |
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Received July 31, 2004; revised December 3, 2004; accepted March 9, 2005.
Edited by Luis Serrano
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