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Department of Biochemistry and Cell Biology, Stony Brook University, Stony Brook, New York 11794-5215, USA
(RECEIVED April 12, 2006; FINAL REVISION May 19, 2006; ACCEPTED May 19, 2006)
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Abstract |
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 TOP Abstract IntroductionMaterials and methodsResultsDiscussionReferences |
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Keywords: Kyte-Doolittle scale; hydrophobicity; hydropathy; transmembrane helix; transmembrane sequence prediction; transmembrane tendency; transmembrane propensity
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Introduction |
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TOPAbstractIntroductionMaterials and methodsResultsDiscussionReferences |
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In this study, we used analysis of databases of known soluble and TM proteins in order to resolve the total genomic hydrophobicity profiles into TM and soluble sequence components. This approach allowed formulation of a scale refinement procedure that caused different scales to collapse to a single scale that approaches a theoretical limit for accuracy.
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Materials and methods |
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TOPAbstractIntroductionMaterials and methodsResultsDiscussionReferences |
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Duplicated sequences within the databases were identified and removed. Signal sequences predicted by Phobius (Kall et al. 2004) were also identified and removed from the protein sequences unless otherwise noted.
To analyze protein sequences with known TM structures, nonredundant 
-helical sequences in the TransMembrane Protein DataBase (TMPDB) (Ikeda et al. 2003) version 6.3 were downloaded from http://bioinfo.si.hirosaki-u.ac.jp/~TMPDB/. Protein sequences in TMPDB were obtained, and the sequences of transmembrane (TM) helices were extracted based on the annotation in the database. The distributions of the helices’ length and hydrophobicity were calculated. To evaluate the accuracy of the boundary of the TM annotation, the lengths of the helices were either extended or shortened by one or two residues at the N and C termini, and their hydrophobicity was calculated. Correct identification of TM sequence boundaries was inferred from the observation that shortening putative TM segments did not increase their hydrophobicity, while extending them decreased it significantly (see below).
For soluble proteins, non-transmembrane proteins with known 3D structures were collected from the ASTRAL database, which as described, "is partially derived from, and augments, the Structural Classification of Proteins (SCOP) database (Murzin et al. 1995). ASTRAL SCOP genetic domain sequences with less than 95% identity to each other, based on Protein Data Bank (PDB) (Berman et al. 2000) SEQRES records," were downloaded from the ASTRAL Compendium (Chandonia et al. 2004) at http://astral.berkeley.edu/. Abundance versus TM tendency profiles of almost identical shape were obtained when the analysis was restricted to sequences with <40% identity.
Membrane proteins under class 6 ("membrane and cell surface proteins and peptides") of SCOP except the first ("toxins’ membrane translocation domains") and the 36th folds ("anthrax protective antigen") were removed. However, it should be noted that the resulting collection of "soluble" proteins still contains a residual population of proteins that were crystallized in the presence of detergents, such as pilin (PDB ID code 2PIL).
Hydrophobicity analysis
A Perl program (genome-TM) was scripted to implement a sliding-window algorithm (Kyte and Doolittle 1982; Claros and von Heijne 1994) to analyze the amino acid sequences in the protein databases. Copies of genome-TM are available from the authors. Unless otherwise noted, a sliding-window size (N) of 19 residues was used. A standard hydrophobicity plot for each protein was generated by selecting a hydrophobicity scale, which assigns a hydrophobicity index value for each of the 20 amino acids, and progressively averaging the hydrophobicity values for each and every N residues along a protein's primary sequence. The resultant averaged values were plotted versus sequence position, which was defined as the position of the residue at the center of the window being averaged. Hydrophobicity was calculated with a stepwise decreasing hydrophobicity threshold value. If the average hydrophobicity value for a specific sequence was equal to or above the threshold being used, then the program would mark the position as being an initial estimate for the sequence of a qualifying candidate. The program shifted the window for that sequence by one residue at a time until it came to a residue whose inclusion resulted in an averaged hydrophobicity value falling below the threshold, and then marked the segment minus the last residue as a qualifying candidate. If the gap between two neighboring candidates was within a certain distance M (merging factor, the default value was 4 residues), they were merged into a single candidate sequence (Deber et al. 2001). If a candidate contained segments previously identified at a higher threshold value, it was discarded, so that only the segments whose hydrophobicity value fell between the current threshold and the threshold hydrophobicity one level higher were stored as falling into the hydrophobicity range being scored. If a segment was more than 35 residues long, and thus likely to contain multiple TM sequences, it was discarded for the purpose of our calculations. However, such sequences were very rare for N 
5 and M = 4.
Not every residue in a protein was scored by this approach. One reason is that sequences located between two higher hydrophobicity sequences could sometimes be less than N residues long. In addition, the probability that hydrophilic sequences are surrounded by more highly hydrophilic sequences, such that the former can be identified as being <35 residues long, decreases progressively as hydrophobicity decreases. Overall, somewhat over 50% of the total amino acid residues in a genome were in sequences within the range of hydrophobicities that were scored.
Normalization of abundance versus hydrophobicity distributions for known TM and soluble sequences to genomic abundance versus hydrophobicity distributions
To normalize the abundance versus hydrophobicity distributions for sequences known to be soluble or TM to abundance versus hydrophobicity distributions for all the proteins in a genome, we scaled abundance data for known soluble and TM sequences to the height of the soluble and TM "peaks" in the genomic data. To do this, we assumed that the most highly hydrophobic sequences in a genome are all TM sequences, and the most hydrophilic sequences are all soluble (see Results for the validity of this assumption). We defined highly hydrophobic as being more hydrophobic than sequences at the peak of the TM sequence abundance versus hydrophobicity curve (Fig. 1, open circles), i.e., more hydrophobic than the most abundant hydrophobic sequences. We defined highly hydrophilic as being less hydrophobic than sequences at the peak of the soluble sequence abundance versus hydrophobicity curve (Fig. 1, open squares). Abundance values for the soluble protein and TM helix (TMPDB) databases were then normalized to have about the same number of sequences as found in genomic data over the hydrophobicity ranges in which genomic sequences were assumed to be all soluble or all TM, respectively.
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Calculation of genomic hydrophobicity (GH) scale
We wished to use as many TM sequences as could be identified from genomic data to develop a hydrophobicity scale based on a very large number of TM sequences. Using previous hydrophobicity scales to identify TM sequences from genomic data would bias amino acid abundances in identified sequences so as to reflect the bias in the scale being used. To avoid this circular reasoning, a naive, but effective, assumption was made: that many TM helices could be identified by a high content of the hydrophobic amino acids Ile, Leu, and Val. ILV-rich sequences were then used to investigate the relative abundance of residues other than I, L, and V. ILV-enriched sequences were identified with an algorithm assigning I, L, and V a "hydrophobicity" value of 1 and a "hydrophobicity" value of 0 to all other residues. As estimated from the distribution of Kyte-Doolittle hydropathies for sequences identified by this algorithm, ILV content had to be at least 50% for E. coli, and at least 55% for S. cerevisiae to avoid inclusion of a significant number of non-TM segments (data not shown). This procedure did not capture all TM helices but did identify 2647 E. coli and 820 S. cerevisiae sequences with 19–21 residue lengths for further analysis.
To define a genomic hydrophobicity (GH) scale, the ratio of percent abundance of residues in ILV-rich TM sequences to their percent abundance in soluble proteins (i.e., in the soluble protein database) was then calculated. The average of S. cerevisiae and E. coli TM/soluble ratios were calculated for each amino acid (except I, L, and V) and then normalized to that of Gly. Such relative TM/soluble ratios are in a format analogous to a relative "equilibrium" constant. Since the total value of the free energy of association of individual residues with membranes is likely the main parameter controlling TM insertion, an apparent "
Go" = –RT ln(TM/soluble) would potentially be linearly related to the tendency of residues to participate in a TM structure. Thus, the logarithm of the relative TM/soluble ratios was calculated as a parameter likely to be linearly related to TM propensities. The GH scale was then normalized so that the difference between values for the most and least hydrophobic residues was the same as for the Wimley-White scale, which is directly related to Go (Jayasinghe et al. 2001). The genomic hydrophobicity value of Gly was arbitrarily set at zero. To complete the GH scale, it was necessary to define genomic hydrophobicity values for I, L, and V. This was done by using the Cowan-Whittaker (CW) scale (Cowan and Whittaker 1990), which performed well in terms of distinguishing between TM and soluble sequences (see Results), and extrapolating to derive I, L, and V GH values based on the approximately linear relationship between CW and GH scales.
Calculation of the transmembrane (TM) tendency scale
Hydrophobicity scale values from the GH scale derived above (and several other scales; see Results) were refined to improve the ability to distinguish between soluble sequences and TM helices by using the constraint that the populations of known soluble and TM sequences scored as having an equal probability of forming in TM helices should have, on the average, almost the same amino acid composition (see Results). Sequences within the hydrophobicity range in which both TM and soluble sequences were abundant were used to define the soluble/TM abundance ratio (percent abundance of each amino acid in soluble sequences)/(percent abundance in TM helices). A TM penalty was then assigned to amino acid residues that were more abundant in soluble sequences (abundance ratio >1) and a TM bonus was assigned to residues that were more abundant in TM helices (abundance ratio <1). This calculation was performed for sequences in each of a series of five narrow hydrophobicity intervals covering the range over which the initial scale being used predicted there was a 30%–80% probability that the sequences would be found in a TM helix, i.e., a 30%–40% chance of being in a TM helix, a 40%–50% chance of being in a TM helix, etc. (see Results), and then the values for different hydrophobicity intervals were averaged.
Penalty/bonus values were calculated in the form of a pseudo Go = –RT ln(Kpseudo), where Kpseudo = (percent abundance in soluble sequences)/(percent abundance in TM sequences). In this format, a positive value of pseudo Go is equivalent to a TM bonus and is added to the original hydrophobicity value for scales in which hydrophobic sequences have higher values than hydrophilic sequences. Pseudo-Go values were added to scale values after adjusting them so that the size of the corrections would equal the same fraction of total hydrophobicity range as in the G-based augmented Wimley-White scale (Jayasinghe et al. 2001). In other words, the correction equation was C = (pseudo-Go/Gomax)Cmax, where C is the correction, Cmax is the difference between "hydrophobicity" values for the most and least hydrophobic residues in that scale, and Gomax is the difference between Go values for the most and least hydrophobic residues in the augmented Wimley-White scale.
We found that adding the correction factor once did not tend to fully equalize the probability that populations of soluble and TM sequences scored as having the same propensity to participate in TM helices had the same average abundance of each amino acid, and thus did not minimize misidentification of TM helices. Instead, misidentification of TM helices was minimized when the correction factors were added twice or three times to the uncorrected values, and so this was done to define TM tendency values. After using the TM tendency values derived from this procedure to recalculate genomic hydrophobicity profiles, we found that a small further improvement in TM tendency values, resulting in even less misidentification, could be obtained by repeating the entire refinement technique, and then adding re-correction factors so obtained once to the preliminary TM tendency scale values. This was done to define the final TM tendency values.
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Results |
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TOPAbstractIntroductionMaterials and methodsResultsDiscussionReferences |
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0.2, while that for TM sequences (circles) occurs near 1.7. For the TM tendency scale (Fig. 1B), the corresponding values are –0.1 and 0.7. Figure 1 also shows that a mixture of the normalized TM and normalized soluble sequence distributions fits the genomic data fairly well (cf. solid lines to triangles). To a first approximation, genomic sequences represent a combination of TM and non-TM soluble segments. It is noteworthy that there is a substantial overlap between the TM and non-TM peaks at intermediate hydrophobicities. We call sequences with intermediate hydrophobicity "semihydrophobic," and define those with hydrophobicities near that of the peak for soluble proteins "subhydrophobic."
In the calculations above, the minimum length of potentially TM segments was defined as 19 residues. Distributions of sequence abundance versus hydrophobicity were not greatly altered when the minimum length of potentially TM segments was varied from 15 to 21 (data not shown). Increasing minimum length only shifted peaks to slightly smaller hydrophobicity values. This reflects the fact that a hydrophobic sequence too short to satisfy the minimum length value being used will be recorded as a sequence of greater length (including some flanking polar residues) but lesser hydrophobicity. In contrast, long hydrophobic sequences with ends that are less hydrophobic than their cores will tend to be recorded as being shorter sequences with a higher hydrophobicity. This means that hydrophobic sequence length and hydrophobicity cannot be defined independently, and thus this type of analysis cannot precisely define TM helix length.
The ratio of the abundance of soluble to TM sequences can be calculated as a function of hydrophobicity value from the normalized abundance of TM and non-TM sequences versus hydrophobicity. This yields the hydrophobicity dependence of percent TM probability = the statistical percent probability that a sequence at a particular hydrophobicity value forms a TM segment. For all hydrophobicity scales tested (see below), sigmoidal curves were found to fit the percent TM probability versus hydrophobicity profile (see example in Fig. 1A, inset). The parameters that describe these sigmoidal curves for different hydrophobicity scales are given in Table 1. Percent TM probability values are useful for comparing the probability that different sequences with borderline hydrophobicity will form a TM helix.
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The refinement procedure was also applied to several other scales that were relatively accurate in terms of predicting TM sequences (Eisenberg consensus, Cowan-Whittaker, and biological hydrophobicity; see below). The refined scales thus derived had values that were much more highly correlated than the scales from which they were derived (e.g., Fig. 2, cf. B and D), and were so highly correlated to the TM tendency scale (r > 0.99) as to indicate that refinement results in convergence to basically the same scale (Fig. 2D). This means that the scale defined by the refinement procedure is not affected by the scale chosen as the starting point, and that the refined scale represents a universal TM tendency scale. Unless otherwise noted, the results reported below are for the refined TM tendency scale derived from our GH scale.
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To evaluate accuracy, abundance versus hydrophobicity distributions of the type shown in Figure 1 were calculated for a variety of hydrophobicity/hydropathy/TM probability scales (Kyte and Doolittle 1982; Fauchere and Pliska 1983; Eisenberg et al. 1984; Rose et al. 1985; Engelman et al. 1986; Cowan and Whittaker 1990; Boyd et al. 1998; Jayasinghe et al. 2001; Chen et al. 2002; Deber et al. 2002; Hessa et al. 2005). For simplicity, all of these scales will be referred to as hydrophobicity scales. These scales include the more accurate hydrophobicity-type scales (Boyd et al. 1998; Chen et al. 2002). In all cases, the basic features of distributions were similar to those shown in Figure 1 (data not shown).
Next, misidentification parameters measuring the ability of a scale to distinguish between TM helices and soluble sequences were defined. The first parameter was the fraction of sequences identified as being TM (i.e., with a TM probability >50%), but which are really soluble (Pfalse TM). The second parameter was the fraction of TM helices not correctly identified as being TM (Pmissing TM). The formal definition of these parameters is given in Table 1, with the positions of the populations of false TM (area A1) and missing TM sequences (area A2) schematically illustrated in Figure 3A. Table 1 compares the accuracy of various scales in terms of Pfalse TM and Pmissing TM. The generally similar performance of different scales reflects their moderate degree of correlation (range 0.65 < r < 0.96; average value 0.85, calculation not shown).
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The misidentification values in Table 1 seem relatively large, but this is an artifact of the way they are defined. If misidentification of soluble sequences is expressed as a fraction of the total sequences in the soluble database (rather than in the TM database), only 1% of soluble sequences are misidentified as TM by the TM tendency scale. Error rates are also exaggerated because they are calculated for an entire genome. This increases the relative amount of non-TM sequence being analyzed relative to methods in which accuracy is tested by trying to predict TM sequences in individual membrane proteins. In the latter situation, one can increase the accuracy of predicting TM helices by decreasing threshold values for defining TM sequences without significantly increasing the amount of non-TM sequence identified as being TM.
It is also noteworthy that the hydrophobicity value equivalent to a 50% probability of being TM depends slightly on the genome chosen for analysis (Table 1). The reason for this is that the hydrophobicity value corresponding to 50% TM probability depends on the relative number of soluble and TM sequences in the genome chosen for analysis (Fig. 3B).
Further comparison of the ability to distinguish TM and non-TM sequences
Although Table 1 shows that the TM tendency scale minimizes misidentification values, these parameters probe accuracy with the soluble and TMP databases, some sequences of which were used to derive the TM tendency scale. Thus, they do not use fully independent training and testing sets and therefore are not a fully valid test of accuracy for the TM tendency scale (although a valid test for comparison of all other scales). Therefore, additional criteria were used to evaluate TM tendency scale accuracy. First, the ability to resolve TM and non-TM populations from whole genomic data was analyzed. Abundance versus "hydrophobicity" profiles were calculated for three prokaryotic and three eukaryotic genomes. Visual inspection of these profiles (Fig. 4) confirms that the TM tendency scale gives a greater degree of resolution between soluble protein and TM sequence peaks relative to the GH scale from which it was derived, with a difference in "hydrophobicity" values at the "peaks" for hydrophilic and hydrophobic proteins averaging 0.05–0.1 units greater for the TM tendency scale than for the GH scale. This shows that the TM tendency scale has an improved ability to discriminate between soluble sequences and TM helices relative to the GH scale (and thus also relative to all scales that Table 1 shows are not as accurate as the GH scale). A qualitatively similar improvement was found when the TM tendency scale was compared to the biological hydrophobicity scale, although the improvement was smaller, consistent with the higher accuracy of the biological hydrophobicity scale relative to the GH scale (data not shown).
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Another parameter used to compare the ability of different scales to distinguish TM from non-TM sequences is the ratio of the number of sequences with intermediate hydrophobicity (defined as sequences within the 10%–90% TM probability range) to the number of likely TM sequences (>90% TM probability). The better a scale resolves intermediate hydrophobicity sequences into TM and non-TM populations, the lower this ratio (R) should be. We tested this for the Yersinia pestis genome. As shown in Table 1, this R is lowest for the TM tendency scale, and the ability of different scales to resolve TM from non-TM sequences as judged from the R parameter generally parallels that reported by Pfalse TM and Pmissing TM.
Whether a sequence is the most hydrophobic in a protein can independently illustrate the accuracy of TM tendency scale
Additional information concerning scale accuracy can be obtained by considering whether a hydrophobic sequence is the most hydrophobic in that protein (= maxH sequence) (Boyd et al. 1998) or is not (a non-maxH sequence). Figure 5A shows that for both soluble sequences in known soluble proteins and TM sequences in known TM membrane proteins the hydrophobicity of the most hydrophobic sequence in a protein (filled symbols) is on the average significantly more hydrophobic than the other sequences within that protein (open symbols). This is shown by the fact that the peaks for non-maxH sequences are at lower hydrophobicities than for maxH sequences.
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This difference can be used to confirm the accuracy of the TM tendency scale. When the probability that a sequence is the most hydrophobic (maxH sequence) in a protein is assessed as a function of hydrophobicity using whole genomic data, and compared to the probability of being the most hydrophobic for soluble and TM sequences from databases of soluble and TM proteins, respectively (Fig. 5C,D), it is easy to discern the TM tendency values at which the sequences in a genome are exclusively soluble, exclusively TM, or a mixture of the two. Figure 5, C and D, shows that at low TM tendency (<0.3) the probability of a genomic sequence being the most hydrophobic fits expectations for soluble proteins. This is true because the low hydrophobicity region is dominated by sequences from soluble proteins. Similarly, at high TM tendency (>0.8), where TM sequences predominate, the probability of a sequence being the most hydrophobic in a protein fits expectations for TM sequences. The intermediate TM tendency range (0.3–0.8) does not fit the shape for either the soluble or TM sequence populations because it contains both TM and non-TM populations.
A key observation is that the intermediate hydrophobicity range defined by this method closely approximates the range in which soluble and TM sequences coexist as derived from fitting genomic data to soluble and TM protein databases (0.3–0.7 TM tendency values for 10% and 90% TM probabilities, respectively; see Table 1). In addition, the TM tendency value at which the percent TM probability is 50% in Figure 5, C and D (X-axis value 0.5–0.55, where the Y-axis value for the probability that a sequence is the most hydrophobic is halfway between the values for the soluble and TM curves) agrees with the 50% TM probability value for the TM tendency scale when independently derived from resolution of genomic data into soluble and TM sequence components (Table 1). This agreement is a further indication of the accuracy of the TM tendency scale.
It is noteworthy that both the overlap between (1) TM and non-TM maxH sequences and (2) the overlap between TM and non-TM non-maxH sequences (Fig. 5A) is less than the overlap between the combination of all sequences (i.e., maxH and non-maxH) (Fig. 1B). This implies that by separately calculating the percent TM probability for maxH and non-maxH sequences, it should be possible to improve prediction of TM sequences (see Table 1).
Comparison of amphiphilic, signal, and TM sequences using the TM tendency scale
Semihydrophobic helices are not equivalent to amphiphilic helices. Methods for identifying amphipathic helices (Eisenberg et al. 1984) indicate such helices represent a small fraction of the subhydrophobic sequences, and are not at all abundant in the semihydrophobic range, even when a low stringency hydrophobic moment is used (0.35). The lack of amphiphilic sequences in the semihydrophobic range is not surprising because they should have a relatively high content of polar and ionizable residues. Semihydrophobic sequences should also not be confused with signal sequences. Signal sequences (identified as described in Kall et al. 2004) were deleted from the genomic databases used in the preceding sections because they are generally not found in mature proteins, and thus would not be represented in the soluble and TMP structural databases used to resolve genomic data. When abundance versus hydrophobicity was calculated for the population of signal sequences from the E. coli and S. cerevisiae genomes, a Gaussian-like dependence of abundance on TM tendency value was found, with a peak at 0.6 units and a width at half-height of about 0.5 units. It should be noted that the omission of signal sequences does not have a major impact on the appearance of abundance versus hydrophobicity distributions for genomic data because signal sequences form only a small fraction of the total sequences (they were only 10% as abundant as sequences from mature proteins at the TM tendency value at which signal sequences show peak abundance). It should be noted that the hydrophobicity range containing signal sequences is influenced by the fact that signal sequences often have short hydrophobic cores of 7–15 residues in length (von Heijne 1985). Thus, our computations, which assigned hydrophobicities to sequences of 19 residues and longer, would tend to report the hydrophobicity of not only the signal sequence core, but also some of the surrounding hydrophilic residues.
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The possibility that there is a second, very different, scale that also fulfills the equal average composition criterion is unlikely because refinement of several different scales converged on the TM tendency scale, and because TM tendency scale values seem to correspond to consensus values for all previous hydrophobicity-type scales (see Results). However, we do not claim that the TM tendency scale is the ultimate method for predicting TM helices. Like other hydrophobicity-type, single-value scales, TM tendency ignores factors such as the identity of neighboring residues or the depth of a residue in the bilayer (Caputo and London 2004; Hessa et al. 2005). In this regard, an alternate approach to identify TM sequences involves Hidden-Markov methods. Such methods can use all differences in the pattern of sequences for TM and non-TM proteins to strengthen predictive accuracy. It is interesting to ask how TM tendency and Hidden-Markov methods compare. When we chose a sample of 36 membrane proteins of known structure that was recently used to compare scales (Chen et al. 2002), and compared the TM tendency scale to HMMTOP2, a Hidden-Markov method found to predict helices more accurately than previously reported single-value scales (Chen et al. 2002), we found little difference in the ability to correctly identify TM helices in terms of the sum of false positives, false negatives, or helix boundaries (data not shown).
Relationship of biological hydrophobicity and TM tendency scales
As noted in the Results, the biological hydrophobicity and TM tendency scales are highly correlated with each other, although they are derived from two very different approaches. The biological hydrophobicity scale was defined by a sophisticated experiment in which the ability of sequences to insert in membranes in TM form was assayed in a translocon-assisted process that mimics the insertion process in vivo (Hessa et al. 2005). It was derived for an Ala + Leu hydrophobic sequence containing at most one hydrophilic residue, and forming part of a chimera with two natural TM helices not likely to interact with the Ala + Leu sequence. Thus, biological hydrophobicity scale values are probably most accurate for an isolated TM helix in which helix–helix interactions and interactions between hydrophilic residues do not have a significant role.
In contrast, the TM tendency scale reports on the propensities of amino acid residues to participate in TM helices on a genome-wide basis, and represents a statistical average influenced by all of the parameters that complicate TM propensities. In this regard, it should be noted that the TM tendency scale does not directly measure hydrophobicity. A hydrophilic residue abundant in membrane proteins for functional reasons should have a higher TM tendency value than predicted based on hydrophobicity. Nevertheless, to some degree one can think of the TM tendency scale as the "statistical" version of biological hydrophobicity averaged over all possible sequences. Thus, the difference between these two scales is a measure of the degree to which all the interactions that occur in real membrane proteins can complicate the propensity of an isolated residue to participate in a TM helix. The relative agreement between the two scales is an indication that the effect of these interactions is relatively modest, but still significant. Once the experimental behavior of more complex sequences is defined, it will be interesting to see whether experimental data agree even more closely with the predictions of the TM tendency scale.
Relationship between TM tendency and thermodynamic tendency to form a TM state
It should be noted that the percent TM probabilities we describe in this study do not have an exact thermodynamic meaning. Percent TM probability values are statistical and are slightly dependent on the number of soluble and TM proteins in a genome (Fig. 3B). Nevertheless, percent TM probability values may not deviate far from true thermodynamic values. For the augmented Wimley-White hydrophobicity scale, the value at which there is a 50% TM probability for the sum of E. coli and S. cerevisiae sequences (0.114) is only slightly higher than the theoretical value of 0, which represents equal free energy for the TM and non-TM states (Jayasinghe et al. 2001). The slightly higher value from analysis of genomic sequences may mainly be a consequence of the fact that hydrophilic residues in natural sequences often will engage in interhelical interactions or place hydrophilic residues within the bilayer to carry out biological function. Thermodynamic scales usually evaluate the behavior of individual amino acids in a manner that does not include the effect of these factors.
In cases of post-translational membrane insertion, the folding of a protein in the soluble state can also affect the propensity to form a TM state. Equilibration of hydrophobic segments between TM and non-TM states in such cases should not be determined solely by the difference between the free energy of amino acid segments when exposed to aqueous solution relative to their free energy when part of a membrane-inserted TM helix. Instead, the appropriate equilibrium is that between the folded form of the protein in solution, and the membrane-inserted conformation. In aqueous solution, hydrophobic segments in a protein are often buried and have specific interactions with other segments that decrease free energy relative to that in the unfolded state.
Nevertheless, simple hydrophobicity is clearly the predominant factor determining membrane insertion as shown by the observation that a pure hydrophobicity scale (the augmented Wimley-White scale; Jayasinghe et al. 2001) is reasonably well correlated with the TM tendency scale (r = 0.91). It should be noted that although we normalized the TM tendency scale so that the range of values for different residues was roughly as large as the range of Go values for membrane insertion given by the augmented Wimley-White scale (see Materials and Methods), we did not calibrate it so that a 50% TM tendency value of zero would be equivalent to a Go of zero for TM insertion. The lack of such calibration would not affect the ability of the TM tendency scale to distinguish TM from non-TM sequences.
Semihydrophobic sequences and conversion between TM and non-TM states
One of the more important developments in this study is that there is a considerable population of sequences within the semihydrophobic range. Such sequences might form either TM or non-TM structures depending on the exact experimental or biological conditions to which they are exposed. Protein toxins such as diphtheria toxin and certain colicins, and some Bcl-class proteins (Qiu et al. 1996; Kienker et al. 1997; Ren et al. 1997; Jeong et al. 2004; Rosconi et al. 2004) have sequences already believed to switch between TM and non-TM states. These sequences often fall in the semihydrophobic (10%–90% TM probability) range. According to the TM tendency scale, helix 5, helices 6–7 (combined), helix 8, and helix 9 in the T domain of diphtheria toxin have percent TM probabilities of 15%, 35%, 63%, and 63%, respectively. In contrast, despite existing within both soluble and TM conformations, helices 8 and 9 in the channel-forming domain of colicin A have percent TM probabilities of 94% and 99.5%, respectively (calculation not shown). This suggests that if properly buried within the interior of a protein, even very hydrophobic sequences can exist in a soluble state.
The capacity to switch between TM and non-TM states is not limited to special proteins. This equilibrium has been observed for numerous simple hydrophobic helices in model membranes (Bechinger 1996; Ren et al. 1997, 1999; Lew et al. 2000; Vogt et al. 2000; Caputo and London 2004), and for simple sequences undergoing translocation via the translocon (Hessa et al. 2005). It is possible that there are additional cases in which predominantly soluble proteins have semihydrophobic sequences that allow formation of as-yet-undiscovered minor TM subpopulations, and vice versa. This could have tremendous biological significance. In some cases, the soluble and membrane-inserted conformations of such a protein might have very different tertiary structures and functions. The possibility of minor soluble or membrane-inserted populations moonlighting in this fashion is one of the more exciting possibilities revealed by this study.
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Footnotes |
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Article and publication are at http://www.proteinscience.org/cgi/doi/10.1110/ps.062286306.
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Acknowledgments |
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References |
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0.1 units greater than the hydrophobicity value shown on the X-axis. To resolve curves into soluble and TM sequences, hydrophobicity distributions were calculated for soluble proteins with known structures from a soluble protein database (squares) and for TM segments from a database (TMPDB) (
