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Open Access

Peer-reviewed

Research Article

Coupling between Properties of the Protein Shape and the Rate of Protein Folding

Abstract

There are several important questions on the coupling between properties of the protein shape and the rate of protein folding. We have studied a series of structural descriptors intended for describing protein shapes (the radius of gyration, the radius of cross-section, and the coefficient of compactness) and their possible connection with folding behavior, either rates of folding or the emergence of folding intermediates, and compared them with classical descriptors, protein chain length and contact order. It has been found that when a descriptor is normalized to eliminate the influence of the protein size (the radius of gyration normalized to the radius of gyration of a ball of equal volume, the coefficient of compactness defined as the ratio of the accessible surface area of a protein to that of an ideal ball of equal volume, and relative contact order) it completely looses its ability to predict folding rates. On the other hand, when a descriptor correlates well with protein size (the radius of cross-section and absolute contact order in our consideration) then it correlates well with the logarithm of folding rates and separates reasonably well two-state folders from multi-state ones. The critical control for the performance of new descriptors demonstrated that the radius of cross-section has a somewhat higher predictive power (the correlation coefficient is −0.74) than size alone (the correlation coefficient is −0.65). So, we have shown that the numerical descriptors of the overall shape-geometry of protein structures are one of the important determinants of the protein-folding rate and mechanism.

Citation: Ivankov DN, Bogatyreva NS, Lobanov MY, Galzitskaya OV (2009) Coupling between Properties of the Protein Shape and the Rate of Protein Folding. PLoS ONE 4(8): e6476. https://doi.org/10.1371/journal.pone.0006476

Editor: Michael Levitt, Stanford University, United States of America

Received: June 15, 2009; Accepted: June 21, 2009; Published: August 3, 2009

Copyright: © 2009 Ivankov et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was supported by the programs “Molecular and cellular biology” and “Fundamental sciences – medicine”, by the Russian Foundation for Basic Research (08-04-00561), and by the “Russian Science Support Foundation”. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

There is enormous diversity in the protein folding behavior from small proteins usually folding with simple two-state kinetics to large proteins usually folding with multi-state kinetics. Since misfolding, slow folding, and aggregation of proteins are responsible for many of the most devastating amyloid-related and other “conformational” diseases of the 21st century, it will be interesting and important to find new factors and parameters that correlate with protein folding rates.

There appear some general trends and correlations for the structural, thermodynamic, and kinetic properties of proteins [1]–[7]. The first comparison of a parameter with experimentally observed folding rates was made when it was shown that topology may be a critical determinant of two-state folding kinetics [3]. But the topology itself cannot explain the differences in the refolding rates for some proteins sharing the same fold (SH3 domains, cold shock proteins, fibronectin domains, proteins of the ferredoxin fold) [8]–[12].

A number of basic correlations between the protein size and folding rate have been suggested [1], [13], [14]. All of them point out that, as might be expected, the folding rate decreases with protein size, but suggest different scaling laws for this decrease. However, the current statistical analysis of protein folding data shows that all the suggested scalings, from –ln L to –L1/2 and –L2/3 correlate with the observed folding rates nearly equally: the correlation between folding rates and protein sizes is not large, about 65% [14]–[17]. It has been shown, that protein size per se determines folding rates of three-state folding proteins [5]. However, protein size, being the major determinant of the type of folding behavior, is not sufficient to determine the folding type of a protein since large proteins do not necessarily exhibit multi-state kinetics (for example, large helical protein Variable surface antigen VlsE folds with two-state kinetics [18]).

In the last years several models have been suggested to estimate the logarithm of the folding rate and structural parameters such as the contact order along with its modifications, the number of contacts, or the protein “effective length” [3], [7], [19]–[23]. These algorithms show a large magnitude of correlation coefficient between the folding rate and different structural features; however, they do not contribute to discriminating between two and multi-state kinetics.

Simultaneously, statistical and different machine-learning techniques were used to get high correlation with protein folding rates. Sometimes neural networks were used: for predicting folding rates of two-state proteins with known native structure, Dinner and Karplus [24] considered contact order and protein stability as the inputs to the neural network, while Zhang with collegues [25] used contact order, long-range order and total contact distance. A multiple regression technique was used for predicting protein folding rates from the protein secondary [26], [27] and primary structures [28]. Capriotti and Casadio [29] used a support vector machine for prediction of the protein folding kinetic order and rate from the known atomic structure. The multiple regression technique has allowed finding that proteins with two-state and multi-state kinetics have different rate-determining amino acids [30]. Although the amino acid composition may be one of the determinant factors for protein folding behavior, it does not make clear why the difference in intrinsic properties leads to a different folding type. On the contrary, it was demonstrated on a simple model that folding rates depend only on the topology of the native state but not on the sequence composition [31]. Overall, bioinformatical methods per se can not provide physical explanation of the obtained results.

The above rather conflicting results demonstrate that the theory of protein folding rate should be developed further. Therefore, the search for the factors affecting the protein folding process goes on.

There are several important questions on the coupling between properties of the protein shape and the rate of protein folding. Consideration of protein compactness specifically addresses the issue of why some proteins fold more rapidly than others. First, it has been shown that among proteins of the same size, α/β proteins have, on average, a greater number of contacts per residue due to their more compact (i.e., more “spherical”) structure [32], [33]. Next, we have suggested a relationship between the compactness expressed as the number of contacts per residue and folding rates (for 75 proteins for which both folding rates and tertiary structures are known): α-helical proteins have on average the fastest folding kinetics and the smallest number of contacts per residue (they are less compact than others), whereas α/β proteins have on average the slowest folding kinetics and the largest number of contacts (they are more compact than others) [33]. An explanation is that the expected surface of the boundary between folded and unfolded phases in the transition state for a more spherical protein is larger than for a non-spherical protein leading to a higher barrier and slower folding. Thus, the fact that α/β proteins are more spherical explains both the more average number of contacts per residue and the slower folding kinetics.

Since on average, the folding of multi-state proteins is slower than that of two-state ones, we should get further and define some numerical descriptors of the overall shape-geometry of protein structures to analyze their performance in predicting the folding behavior for a database of experimentally studied proteins. It turned out that parameters taking into account both the size and characteristics of the protein shape correlate well with the logarithm of the folding rate. We demonstrated that the radius of cross-section is a highly sensitive parameter that can be used to predict the protein folding rates and their possible mechanism of folding.

Methods

Data Sets

We have considered 84 single-domain proteins or separate domains of multi-domain proteins for which both folding rates and tertiary structures are known [33], [34]. Among them 26 proteins exhibit multi-state kinetics and 58 proteins exhibit two-state kinetics (see http://phys.protres.ru/resources/compact.html).

We have selected single-domain proteins or separate domains of multi-domain proteins having from 51 to 350 residues with less than 25% sequence identity belonging to classes ‘a’ (all-α proteins), ‘b’ (all-β proteins), ‘c’ (α/β proteins), and ‘d’ (α+β proteins), according to the SCOP classification [35], release 1.65. The obtained database includes 3413 proteins: 702 all-α proteins, 868 all-β proteins, 914 α/β proteins, and 929 α+β proteins.

Calculation of protein structure compactness

We have calculated the solvent-accessible surface area and volume surrounded by this surface, and also volume enclosed by the protein molecular surface for each protein considered. We accomplished the calculations using the YASARA program [36] [http://yasara.org], setting the radius of a probe molecule to be 1.4 Å.

We consider a series of structural descriptors intended to describe protein shapes:

is proportional to the average radius of the minimal cross-section in the center of protein molecule (for short, sometimes we will use the name “radius of cross-section” for this parameter). Among different geometric bodies of equal volumes, this ratio should be maximal for a sphere. This value has the dimension of length and depends on the protein size.

which Zehfus and Rose called the “coefficient of compactness” [37] is the ratio of the accessible surface area of protein to the surface area of sphere of equal volume VASA as that of the protein (for sphere this ratio is 1).

Radius of gyration. If we consider atoms as points in the 3D space, the radius of gyration Rg of a protein is calculated as:(1)where mi is the mass of the i-th atom, ri is its Cartesian coordinates, M is the mass of the protein, and RC is the coordinate vector of the mass center of the protein calculated as follows:(2)Since PDB files with protein structures often lack hydrogen atoms, then in eq. (1) only non-hydrogen atoms should be taken into account, and M is the net mass of non-hydrogen atoms.

For calculation of the normalized radius of gyration we computed the radius of gyration of a ball of uniform density and of equal volume as that of the considered protein according to the following equation:(3)where Vmol is the volume enclosed within the protein molecular surface. Note that the radii of gyration , , and of a ball of uniform density corresponding to the rotations around Cartesian axes x, y, and z going through the mass center are calculated as . The factor 3/5 in eq. (3) instead of 2/5 in the latter equation is explained by the fact that there are many axes a protein can be rotated around; in this case the radius of gyration “averaged” over all possible rotations is calculated as [38].

Results

Different measures of compactness in protein folding study

After more than 30 years we return to the measure of compactness which was suggested by Wetlaufer [39]. Wetlaufer measured the compactness of a protein (or a protein part) by the use of its surface to volume ratio, normalized by the surface to volume ratio for a ball of equal volume. The parameter introduced by Wetlaufer is equal to the protein surface normalized by the surface of a ball of equal volume. In analogy to hydrodynamic frictional ratios, this relative surface area should form a numerical scale on which the more compact structure will have smaller values [39]. In a number of modifications, this parameter appeared later as “roughness index” [40], “globularity index” [41], “coefficient of compactness” [37], and “compactness” [42]. We use here the name “coefficient of compactness” [37] and its definition as the accessible surface area of a protein normalized by the surface area of a ball of equal volume (minimum possible surface area).

The overall shape of an object becomes the factor that determines compactness if packing efficiency is uniform [37]. Our analysis of 3413 protein structures having from 51 to 350 residues revealed that packing efficiency, indeed, is the same for proteins from different structural classes evidenced by the molecular volume per atom (for all-α proteins – 18.520±0.010 Å3, for all-β proteins −18.577±0.009 Å3, for α/β proteins −18.618±0.007 Å3, and for α+β proteins −18.598±0.009 Å3).

In addition to the coefficient of compactness, we have used other measures of compactness in our work: the radius of gyration and the radius of cross-section (see Methods).

First, we have inspected the properties of unnormalized radius of gyration as a measure of compactness describing the shape of the protein globule. The statistical analysis of radii of gyration for 3413 protein structures from four general structural classes (all-α, all-β, α/β, α+β) demonstrates that each class of proteins has its own class-specific radius of gyration, which determines the shape of protein structures: α-proteins have the largest radius of gyration while α/β-proteins have the least radius of gyration (see Figure 1). This shows that α-proteins are less spherical, and α/β proteins are most spherical among proteins of four structural classes. This is similar to the result obtained earlier for other measures of compactness, namely, for the coefficient of compactness and the number of contacts per residue [33].

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Figure 1. Dependence of radius of gyration on the number of residues in proteins for general structural classes.

Average errors are given. Structural classes are indicated. Below each point corresponding to α/β proteins, the probability that the observed difference in average values is occasional is given. The probabilities were calculated with Student's t-test (probabilities, α/β vs. α proteins is shown).

https://doi.org/10.1371/journal.pone.0006476.g001

The clearly seen dependence of the radius of gyration on the protein length (Figure 1) is not convenient for an analysis since it forces us to use several size windows. From the other measures of compactness, the number of contacts per residue and the radius of cross-section, similar to the radius of gyration, are expected to depend on the protein size and they do so [33]. The only measure that could be independent of the protein size is the coefficient of compactness, since it is a normalized parameter. Though, it also depends on the protein size (Figure 2a), and this dependence is crucial since consideration of all proteins taken together (i.e. without sorting them according to their size) changes the result dramatically, namely: α/β proteins are erroneously classified as proteins with intermediate compactness (Figure 2c) while actually they have the highest compactness as judged by the analysis using different window sizes (Figure 2a). Such a situation is a result of a different number of proteins in each region of size. Therefore, the average value over six regions does not necessarily coincide with the average over all proteins without dividing them into regions. Thus, to avoid the dependence on length, we introduce a new measure of compactness as a protein radius of gyration normalized to the radius of gyration of a ball with equal volume. It turns out that this measure does not depend on the protein size (Figure 2b), and as a consequence, the results of averaging over all proteins are the same as those for the window-sized analysis, namely, α/β proteins are the most compact, i.e. have a more spherical shape than the others (Figure 2b,d).

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Figure 2. Average coefficient of compactness and average normalized radius of gyration for proteins from different structural classes.

(A) Dependence of average coefficient of compactness on the number of protein residues for general structural classes. (B) Dependence of average normalized radius of gyration on the number of protein residues for general structural classes. (C) Average coefficient of compactness for proteins from different structural classes. (D) Average normalized radius of gyration for proteins from different structural classes. In each panel average errors are given. Structural classes are indicated.

https://doi.org/10.1371/journal.pone.0006476.g002

The results for different measures of compactness and for different structural classes are presented in Table 1.

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Table 1. Average values of different measures of compactness for proteins from four general structural classes given for different size windows and for whole classes.

https://doi.org/10.1371/journal.pone.0006476.t001

Relationship between the parameters describing the protein globule shape and the protein folding rate

Structural descriptors intended to describe protein shapes have been divided into four groups: parameters of compactness connected with the radius of cross-section, parameters of compactness which are not connected with the protein size, other parameters depending on the protein size, and other parameters not depending on the protein size.

According to analytical theory of protein folding based on the nucleation model [1], the logarithm of the folding rate should be proportional to the surface of the boundary between two phases (folded and unfolded) in the transition state. However, parameters of compactness considered above are closely related to this boundary, so the surface of the boundary can be roughly estimated from the protein native structure. For example, (VASA/SASA)2 is proportional to the square of the minimal cross-section drawn through the center of a protein molecule, while Rg2 is roughly proportional to the surface of the maximal cross-section. We have also considered parameters VASA/Rg and L2/3; the latter is the size of the average cross-section drawn through the protein center for a spherical protein since it does not take into account the protein shape. Correlation coefficients between the considered parameters and the logarithm of folding rates for 84 proteins have been calculated and are given in Table 2. One can see that these parameters can predict the folding rate (the correlation coefficient is larger than 0.7 in most cases). It should be mentioned that the correlation of protein folding rates with Rg2 (proportional to the surface of maximal cross-section) is worse than with (VASA/SASA)2 (proportional to the surface of minimal cross-section drawn through the center of the protein molecule). This is quite predictable since a protein prefers to fold through the transition state with the least surface of the boundary between two phases.

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Table 2. Correlation coefficients between logarithms of folding rates in water and different parameters of protein structure.

https://doi.org/10.1371/journal.pone.0006476.t002

Parameters normalized for excluding the dependence on protein size, whether they describe compactness (Rg/Rg*, SASA/S*ASA) or average length of loop (CO), correlate worse with the logarithm of folding rate (see Table 2 and Figure 3a,b). It turned out that when a descriptor is normalized to eliminate the influence of protein size, it completely looses its ability to predict folding rates. On the other hand, when a descriptor correlates well with protein size (the radius of cross-section and absolute contact order in our case) it would correlate well with the logarithm of folding rates (see Table 2, Figure 3c,d). It should be stressed that among normalized parameters, SASA/S*ASA which slightly depends on protein size (Figure 2a) correlates somewhat better with folding rates than Rg/Rg*, which is independent of the protein size (Figure 2b).

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Figure 3. Dependence of the logarithm of the folding rate in water for multi-state and two-state folders on several investigated parameters.

Black circles correspond to two-state folders and open circles correspond to multi-states folders.

https://doi.org/10.1371/journal.pone.0006476.g003

Since all measures of the size of the cross-section ((VASA/SASA)2 and L2/3) are highly dependent on the protein size, we have controlled critically the performance of the new descriptors with protein size. The correlations of protein size in the various colors (L, ln L, L1/2) with the logarithm of folding rates have been calculated (see Table 2 and Figure 4). One can see that not normalized parameters of cross-section, (VASA/SASA)2 and L2/3 have a slightly higher predictive power than the size alone [r = −0.73 for (VASA/SASA)2, the P-value associated with this correlation, P = 0.00002, is extremely low, suggesting that the observed correlation is highly improbable to have arisen by chance; r = −0.69 for L2/3, P = 0.0002. r = −0.65 for L, P = 0.0009.]. However, pure theoretical parameter L2/3 is statistically indistinguishable from L (see errors of correlation in Table 2). On the other hand, the new descriptor (VASA/SASA)2 is statistically distinguishable from L, i.e. is not just a complicated reformulation of size scaling effects, thus, providing some information on the compactness of the protein globule. It should be noted, that ln L and L1/2 have almost the same performance [r = −0.71 for ln L, the P-value associated with this correlation, P = 0.00004; r = −0.70 for L1/2, P = 0.00004] as VASA/SASA; however, ln L does not ensue from any physical theory. At the same time one can see that such a parameter as AbsCO has a slightly higher predictive power than the radius of cross-section (VASA/SASA)2 [r = −0.77 for AbsCO; the P-value associated with this correlation, P = 0.000001, is extremely low, suggesting that the observed correlation is highly improbable to have arisen by chance. r = −0.73 for (VASA/SASA)2; the P-value associated with this correlation, P = 0.00002].

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Figure 4. Dependence of the logarithm of the folding rate in water for multi-state and two-state folders on the protein size in various colors (L, L1/2, ln L) and absolute contact order.

Black circles correspond to two-state folders and open circles correspond to multi-states folders.

https://doi.org/10.1371/journal.pone.0006476.g004

We performed also a very useful analysis which represents the connection between the correlation coefficient and the shape of a cloud of points (see Table S1). One can see that the forms of ellipsoids are different for correlation coefficients −0.77, −0.73, −0.69 and −0.65: the relations of short to long axes are 0.26, 0.30, 0.35, and 0.41, respectively. In an ideal case when correlation coefficient tends to one, the relation of axes tends to zero.

Considering the correlations of protein size in various colors (ln L, L1/2, L2/3) with the logarithm of folding rates one can summarize up that, similar to ln L and L1/2, not normalized parameters of cross-section, (VASA/SASA)2 (reflecting the shape of the protein globule) and L2/3, work slightly better than L. This shows that the improvement over protein length can be interpreted in different ways, one of which is the proposed here importance of cross-section in determining of protein folding kinetics.

Comparison of different parameters describing the shape of protein globules for two-state and multi-state folders

On average, the folding of multi-state proteins is slower than that of two-state ones. At the same time, slow folding proteins are more spherical (compact) than fast folding ones [43]. Therefore, it is natural to expect that multi-state proteins are more compact than two-state folders, and the larger boundary expected for more spherical proteins results in a higher free-energy barrier for folding.

For two-state and multi-state proteins, we have averaged the coefficient of compactness for accessible and molecular surfaces, the radius of cross-section, the normalized radius of gyration, the absolute contact order and the logarithm of folding rates in order to compare them. One can see that multi-state proteins are indeed more spherical and rough than proteins with two-state kinetics as judged by all considered descriptors of the protein shape (see Table 3) except for that has a slight dependence on the protein size (see Fig. 2a). If to consider proteins from some size range (50–100 or 101–150 a.a. residues, see Table 4) one can see that is smaller for multi-state proteins than for two-state ones. The differences are more distinct for other parameters if proteins are divided into groups by size range (see Table 4). From Table 4 one can see that proteins with multi-state kinetics, on average, are more spherical than proteins with two-state kinetics; this is still true for normalized parameters. As concern coefficient of compactness, then from Fig. 2a one can see that it grows with protein size. That is, it can be concluded that longer polypeptides are more likely to both fold via non-two-state mechanisms and to be more spherical.

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Table 3. Average values of normalized radius of gyration, coefficient of compactness for accessible and molecular surface, radius of cross-section, absolute contact order and logarithms of in-water folding rates for two- and multi-state folders.

https://doi.org/10.1371/journal.pone.0006476.t003

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Table 4. Average values of different measures of compactness and logarithms of in-water folding rates for two- and multi-state folders for the considered size range.

https://doi.org/10.1371/journal.pone.0006476.t004

We suggest the following explanation: the slower folding for multi-state proteins of the same size can be explained by their more spherical structure so the expected surface of the boundary between folded and unfolded phases in the transition state [16] for a more spherical protein is larger than that for a non-spherical protein (see Figure 5).

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Figure 5. Transient semi-unfolded (and semi-folded) state of protein.

The unfolded part is shown by dashed lines, the folded structure is shown by solid lines. Unfolded closed loops protruding from the folded part (the nucleus) create an additional surface tension.

https://doi.org/10.1371/journal.pone.0006476.g005

Discussion

One of the first analytical theories of protein folding for one-domain globular proteins was Finkelstein-Badretdinov's theory based on the capillarity model [1]. In the frame of this model, at the point of thermodynamic equilibrium between folded and unfolded states the rate of protein folding depends on the size of the boundary between two phases (folded and unfolded), and unfolding closed loops protruding from the folded part, the nucleus, create an additional surface tension, which results in deceleration of the protein folding. Since the boundary between two phases depends on the number of amino acids for spherical globules as L2/3, then the folding rate at the point of equilibrium between native and unfolded states should depend on the number of amino acids in a similar way

In this work we demonstrate that the radius of cross-section is a highly sensitive parameter that can be used to predict the protein folding rates and their possible mechanism of folding. Both parameters of cross-section, L2/3 and, especially (VASA/SASA)2 which reflects the shape of the protein globule, work slightly better than L.

Comparison of proteins having similar native topologies is an important test for understanding fundamental aspects of the protein folding process. One of the known families of homologous proteins is that of fibronectin type III modules (FNIII). Though the proteins are homologous, the ninth module folds several hundred times slower than the tenth in the absence of a denaturant [44]. The authors who studied these proteins explained the different folding kinetics of the two modules by a large difference in their thermodynamic stability. The analysis done in this study can provide an additional explanation for this difference: despite structural similarities, the coefficient of compactness of the ninth module (1.47) is less than that of the tenth module (1.53). The difference for the radius of cross-section demonstrates the same trend in compactness (3.75 and 3.62, for the 9th and 10th modules, correspondingly). Both modules fold via two-state kinetics [44] (it should be mentioned that the use of a more strong denaturant reveals the population of intermediates in the faster folding of the 10th module [45]), and the relatively slow folding of the ninth module is not due to the occurrence of a slowly folding on-pathway intermediate [46]. Different refolding rates of the set of homologous proteins can be explained by different compactness of the protein structures.

Another interesting example is the folding of cold shock proteins. The absence of correlation between the thermodynamic stability and folding rate, as observed for cold shock proteins, indicates that proteins with a more stable folded state do not necessarily fold faster. At the same time, Cold shock proteins B are less compact (average compactness for three proteins is 1.50) than Cold shock proteins A (compactness is 1.43), and the folding of Cold shock proteins B is faster [11]. The difference for the radius of cross-section demonstrates the same trend in compactness (3.40 Å and 3.54 Å for Cold shock proteins B and Cold shock protein A, correspondingly). However, among three Cold shock proteins B the differences in folding rates are too small to be explained by the differences in compactness.

Thus, more spherical proteins indeed fold more slowly than proteins with an elongated shape. Under equal conditions, a more spherical, more compact protein is not able to avoid the large boundary between two phases (folded and unfolded) in the transition state independent of the folding pathway. A more elongated, less compact protein has a possibility to choose such a pathway of folding in which the protein folding goes through the small boundary between two phases and consequently through a rather low free energy barrier of folding.

Most striking examples of protein folding illustrating this scenario are as follows: the variable surface antigen VlsE (PDB entry 1L8W) is less spherical than a protein with a similar number of amino acid residues which has multi-state kinetics (Tryptophan synthase α subunit, PDB entry 1QOP, see Figure 5 and Tables 1 and 2 at the http://phys.protres.ru/resources/compact.html). VlsE does not obey the contact-order correlation which can be explained in terms of the entropy cost of the size of loops and/or the ordering of residues between contacting residues [18]. Another example is a pair of proteins RNase HI and p16 (PDB entries 2RN2 and 2A5E, respectively, see Figure 6). From our analysis we can conclude that the barrier height for folding of large proteins is defined by the size of the boundary surface between folded and unfolded phases in the transition state.

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Figure 6. Influence of minimal protein globule cross-section on the folding rate.

More elongated, cylinder-like proteins VlsE (PDB entry 1L8W, 294 residues) and p16 (PDB entry 2A5E, 156 residues) having small cross-section fold more rapidly than more spherical proteins RNase HI (PDB entries 2RN2, 155 residues) and Tryptophan-synthase α-subunit (PDB entry 1QOP, 267 residues) having large cross-section.

https://doi.org/10.1371/journal.pone.0006476.g006

The prediction of protein folding rates has its own practical value due to the fact that aggregation directly depends on the rate of protein folding. It is worthwhile to underline that the results of our analysis allow us to suggest additional parameters for determining the folding type of a protein.

As a result of our analysis, two conclusions can be made. First, similar to some other papers emphasizing the influence of protein chain length on folding rate [1], [5], [13], [14], [19], [47], we have found the same effect on our set of protein shape parameters: in order to predict protein folding rates, a parameter should correlate well with the protein chain length. As a very illustrative example of such kind was observed with relative contact order (the normalized parameter, which has poor correlation with the logarithm of protein folding rates, see Table 2) and absolute contact order, which includes both the size of protein and the average length of loops and correlates well with the logarithm of protein folding rates [3], [19]. Second, we have found that the protein shape expressed by different parameters could be an important determinant of the protein folding kinetics and protein folding type. The more spherical is the protein the slower folding it exhibits. Proteins with multi-state kinetics, on average, are more spherical than proteins with two-state kinetics. The barrier height for folding of large proteins is defined by the size of the boundary between folded and unfolded phases in the transition state. This boundary is larger for a spherical shape of the protein globule than for the elongated one.

Supporting Information

Table S1.

Connection between the correlation coefficient and the shape of a cloud of points

https://doi.org/10.1371/journal.pone.0006476.s001

(0.23 MB DOC)

Acknowledgments

We are grateful to A.V. Finkelstein for discussion.

Author Contributions

Conceived and designed the experiments: OVG. Performed the experiments: NSB. Analyzed the data: DNI NSB OVG. Contributed reagents/materials/analysis tools: NSB MYL. Wrote the paper: DNI OVG.

References

  1. 1. Finkelstein AV, Badretdinov A (1997) Rate of protein folding near the point of thermodynamic equilibrium between the coil and the most stable chain fold. Fold Des 2: 115–121.
  2. 2. Jackson SE (1998) How do small single-domain proteins fold? Fold Des 3: R81–91.
  3. 3. Plaxco KW, Simons KT, Baker D (1998) Contact order, transition state placement and the refolding rates of single domain proteins. J Mol Biol 277: 985–994.
  4. 4. Shakhnovich EI (1998) Protein design: a perspective from simple tractable models. Fold Des 3: R45–58.
  5. 5. Galzitskaya OV, Garbuzynskiy SO, Ivankov DN, Finkelstein AV (2003) Chain length is the main determinant of the folding rate for proteins with three-state folding kinetics. Proteins 51: 162–166.
  6. 6. Kuznetsov IB, Rackovsky S (2004) Class-specific correlations between protein folding rate, structure-derived, and sequence-derived descriptors. Proteins 54: 333–341.
  7. 7. Ivankov DN, Finkelstein AV (2004) Prediction of protein folding rates from the amino acid sequence-predicted secondary structure. Proc Natl Acad Sci U S A 101: 8942–8944.
  8. 8. Plaxco KW, Guijarro JI, Morton CJ, Pitkeathly M, Campbell ID, et al. (1998) The folding kinetics and thermodynamics of the Fyn-SH3 domain. Biochemistry 37: 2529–2537.
  9. 9. Guijarro JI, Morton CJ, Plaxco KW, Campbell ID, Dobson CM (1998) Folding kinetics of the SH3 domain of PI3 kinase by real-time NMR combined with optical spectroscopy. J Mol Biol 276: 657–667.
  10. 10. Zerovnik E, Virden R, Jerala R, Turk V, Waltho JP (1998) On the mechanism of human stefin B folding: I. Comparison to homologous stefin A. Influence of pH and trifluoroethanol on the fast and slow folding phases. Proteins 32: 296–303.
  11. 11. Perl D, Welker C, Schindler T, Schroder K, Marahiel MA, et al. (1998) Conservation of rapid two-state folding in mesophilic, thermophilic and hyperthermophilic cold shock proteins. Nat Struct Biol 5: 229–235.
  12. 12. van Nuland NA, Chiti F, Taddei N, Raugei G, Ramponi G, et al. (1998) Slow folding of muscle acylphosphatase in the absence of intermediates. J Mol Biol 283: 883–891.
  13. 13. Thirumalai D (1995) From minimal models to real proteins: time scales for protein folding kinetics. Journal de Physique Orsay Fr 5: 1457–1467.
  14. 14. Gutin AM, Abkevich VI, Shakhnovich EI (1996) Chain length scaling of protein folding time. Phys Rev Lett 77: 5433–5456.
  15. 15. Koga N, Takada S (2001) Roles of native topology and chain-length scaling in protein folding: a simulation study with a Gō-like model. J Mol Biol 313: 171–180.
  16. 16. Galzitskaia OV, Ivankov DN, Finkelstein AV (2001) Folding nuclei in proteins. FEBS Lett 489: 113–118.
  17. 17. Finkelstein AV, Galzitskaya OV (2004) Physics of protein folding. Phys LifeRev 1: 23–56.
  18. 18. Jones K, Wittung-Stafshede P (2003) The largest protein observed to fold by two-state kinetic mechanism does not obey contact-order correlation. J Am Chem Soc 125: 9606–9607.
  19. 19. Ivankov DN, Garbuzynskiy SO, Alm E, Plaxco KW, Baker D, et al. (2003) Contact order revisited: influence of protein size on the folding rate. Protein Sci 12: 2057–2062.
  20. 20. Gromiha MM, Selvaraj S (2001) Comparison between long-range interactions and contact order in determining the folding rate of two-state proteins: application of long-range order to folding rate prediction. J Mol Biol 310: 27–32.
  21. 21. Punta M, Rost B (2005) Protein folding rates estimated from contact predictions. J Mol Biol 348: 507–512.
  22. 22. Zhou H, Zhou Y (2002) Folding rate prediction using total contact distance. Biophys J 82: 458–463.
  23. 23. Makarov DE, Keller CA, Plaxco KW, Metiu H (2002) How the folding rate constant of simple, single-domain proteins depends on the number of native contacts. Proc Natl Acad Sci U S A 99: 3535–3539.
  24. 24. Dinner AR, Karplus M (2001) The roles of stability and contact order in determining protein folding rates. Nat Struct Biol 8: 21–22.
  25. 25. Zhang L, Li J, Jiang Z, Xia A (2003) Folding rate prediction based on neural network model. Polymer 44: 1751–1756.
  26. 26. Gong H, Isom DG, Srinivasan R, Rose GD (2003) Local secondary structure content predicts folding rates for simple, two-state proteins. J Mol Biol 327: 1149–1154.
  27. 27. Prabhu NP, Bhuyan AK (2006) Prediction of folding rates of small proteins: empirical relations based on length, secondary structure content, residue type, and stability. Biochemistry 45: 3805–3812.
  28. 28. Gromiha MM, Thangakani AM, Selvaraj S (2006) FOLD-RATE: prediction of protein folding rates from amino acid sequence. Nucleic Acids Res 34: W70–74.
  29. 29. Capriotti E, Casadio R (2007) K-Fold: a tool for the prediction of the protein folding kinetic order and rate. Bioinformatics 23: 385–386.
  30. 30. Ma BG, Chen LL, Zhang HY (2007) What determines protein folding type? An investigation of intrinsic structural properties and its implications for understanding folding mechanisms. J Mol Biol 370: 439–448.
  31. 31. Voelz VA, Dill KA (2007) Exploring zipping and assembly as a protein folding principle. Proteins 66: 877–888.
  32. 32. Galzitskaya OV, Garbuzynskiy SO (2006) Entropy capacity determines protein folding. Proteins 63: 144–154.
  33. 33. Galzitskaya OV, Danielle C, Reifsnyder DC, Bogatyreva NS, Ivankov DN, Garbuzynskiy SO (2008) More Compact Protein Globules Exhibit Slower Folding Rates. Proteins 70: 329–332.
  34. 34. Bogatyreva NS, Osypov AA, Ivankov DN (2009) KineticDB: a database of protein folding kinetics. Nucleic Acids Res 37: D342–D346.
  35. 35. Murzin AG, Brenner SE, Hubbard T, Chothia C (1995) SCOP: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol 247: 536–540.
  36. 36. Krieger E, Koraimann G, Vriend G (2002) Increasing the precision of comparative models with YASARA NOVA–a self-parameterizing force field. Proteins 47: 393–402.
  37. 37. Zehfus MH, Rose GD (1986) Compact units in proteins. Biochemistry 25: 5759–5765.
  38. 38. Sivukhin DV (1989) General course of physics. 1:
  39. 39. Wetlaufer DB (1973) Nucleation, rapid folding, and globular intrachain regions in proteins. Proc Natl Acad Sci U S A 70: 697–701.
  40. 40. Richards FM (1977) Areas, volumes, packing and protein structure. Annu Rev Biophys Bioeng 6: 151–176.
  41. 41. Wodak SJ, Janin J (1981) Location of structural domains in protein. Biochemistry 20: 6544–6552.
  42. 42. Tsai CJ, Nussinov R (1997) Hydrophobic folding units at protein-protein interfaces: implications to protein folding and to protein-protein association. Protein Sci 6: 1426–1437.
  43. 43. Galzitskaya OV, Bogatyreva NS, Ivankov DN (2008) Compactness determines protein folding type. J Bioinform Comput Biol 6: 667–680.
  44. 44. Plaxco KW, Spitzfaden C, Campbell ID, Dobson CM (1997) A comparison of the folding kinetics and thermodynamics of two homologous fibronectin type III modules. J Mol Biol 270: 763–770.
  45. 45. Cota E, Clarke J (2000) Folding of beta-sandwich proteins: three-state transition of a fibronectin type III module. Protein Sci 9: 112–120.
  46. 46. Dobson CM, Evans PA, Radford SE (1994) Understanding how proteins fold: the lysozyme story so far. Trends Biochem Sci 19: 31–37.
  47. 47. De Sancho D, Doshi U, Munoz V (2009) Protein folding rates and stability: how much is there beyond size? J Am Chem Soc 131: 2074–2075.