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FOR THE RECORD

TM Finder: A prediction program for transmembrane protein segments using a combination of hydrophobicity and nonpolar phase helicity scales

¹ Division of Structural Biology and Biochemistry, Research Institute, Hospital for Sick Children, Toronto M5G 1X8, Ontario, Canada
² Bioinformatics Supercomputing Centre, Research Institute, Hospital for Sick Children, Toronto M5G 1X8, Ontario, Canada
³ Department of Biochemistry, University of Toronto, Toronto M5S 1A8, Ontario, Canada

Reprint requests to: Dr. Charles M. Deber, Structural Biology and Biochemistry, Research Institute, Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada; e-mail: deber{at}sickkids. on.ca; fax (416) 813-5005.

(RECEIVED July 18, 2000; FINAL REVISION October 2, 2000; ACCEPTED October 6, 2000)

Article and publication are at www.proteinscience.org/cgi/doi/10.1110/ps.30301.

	Abstract

TOP Abstract Introduction Construction of the TM... Results and discussion Conclusion References

 
Based on the principle of dual prediction by segment hydrophobicity and nonpolar phase helicity, in concert with imposed threshold values of these two parameters, we developed the automated prediction program TM Finder that can successfully locate most transmembrane (TM) segments in proteins. The program uses the results of experiments on a series of host-guest TM segment mimic peptides of prototypic sequence KK AAAXAAAAAXAAWAAXAAAKKKK-amide (where X = each of the 20 commonly occurring amino acids) through which an HPLC-derived hydropathy scale, a hydrophobicity threshold for spontaneous membrane insertion, and a nonpolar phase helical propensity scale were determined. Using these scales, the optimized prediction algorithm of TM Finder defines TM segments by first searching for competent core segments using the combination of hydrophobicity and helicity scales, and then performs a gap-joining operation, which minimizes prediction bias caused by local hydrophilic residues and/or the choice of window size. In addition, the hydrophobicity threshold requirement enables TM Finder to distinguish reliably between membrane proteins and globular proteins, thereby adding an important dimension to the program. A full web version of the TM Finder program can be accessed at http://www.bioinformatics-canada.org/TM/.

Keywords: TM Finder; transmembrane segments; threshold hydrophobicity; nonpolar phase helicity; membrane protein prediction

	Introduction

TOP Abstract Introduction Construction of the TM... Results and discussion Conclusion References

 
Advances in DNA cloning and sequencing techniques have allowed the rapid derivation of many protein sequences. However, the extreme hydrophobic nature of most membrane proteins have made them difficult targets for detailed structural analysis by techniques such as nuclear magnetic resonance (NMR) or X-ray crystallography. Among the nearly 10,000 entries in the current PDB database (http://pdb. pdb.bnl.gov), only a handful are membrane proteins solved at atomic resolution (e.g., Henderson et al. 1990; Deisenhofer et al. 1984; Tsukihara et al. 1996; Doyle et al. 1998). Because of the lack of generally suitable routes to high resolution analysis, model construction and computer simulation have become necessary tools for understanding various detailed interactions within the membrane domain. Consequently, as a first step toward model building, the accurate delineation of sequences and their secondary structures within membranes becomes a prerequisite.

In the absence of high-resolution structural data, the approximate positions of the membrane-spanning segments within a TM domain can, in principle, be proposed from the amino acid sequence alone with the aid of hydropathy plots (Kyte and Doolittle 1982), which select TM segments based on a moving average of segment hydrophobicity. The development of such hydrophobicity scales traditionally has been based on a combination of statistical analysis (Munoz and Serrano 1994), and the partitioning properties of individual amino acids both as monomers and in globular proteins (Kyte and Doolittle 1982), and in short hydrophilic peptides (Hodges et al. 1994). We have previously reported the experimental determination of apparent hydrophobicity using a series of TM-mimetic model peptides of prototypic sequence KKAAAXAAAAAXAAWAAXAAAKKKK-amide (the underlined region represents the hydrophobic core of sufficient length to span a lipid bilayer, and the guest residues X = each of the 20 common amino acids) (Liu and Deber 1998a). This work led to a hydrophobicity scale for the 20 X-residues based on the individual high-pressure liquid chromatography (HPLC) retention time measurements of the model peptides. When corresponding peptide secondary structures were characterized in aqueous versus micellar environments, peptides exhibited random or partially helical structures in water, but adopted X-residue–dependent full helical structure only upon integration into the micellar membrane. These experiments revealed the existence of a threshold hydrophobicity approximately equivalent to that of a strand of poly-alanine, which, once met or exceeded, dictates the spontaneous insertion of the peptides into micelles. When hydropobicities of natural protein sequences in SWISS-PROT and TMbase were examined using the HPLC-based scale, and the threshold applied, it was found that close to 97% of protein TM segments have hydrophobicity above the threshold value, while nearly 80% of non-TM helices (>19 residues, derived from soluble proteins) fail to meet the same minimum requirement (Liu and Deber 1998a). Therefore, threshold hydrophobicity is an attribute that can be used to distinguish TM segments from helices in soluble proteins.

While transmembrane segments generally are -helical, peptide helicity itself is influenced fundamentally by molecular environment, and accordingly, designations of particular residues as helix formers or breakers will differ significantly in membrane versus soluble protein domains. This circumstance is epitomized by the fact that ß-branched residues (Val, Ile, Thr) and Gly residues predicted to disfavor -helices by algorithms evolved from statistical analysis of residues in soluble proteins (Chou and Fasman 1978) among them can account for 40% of TM segment amino acid composition (Li and Deber 1994). Thus, a helicity scale developed from globular proteins cannot be applied to membrane proteins. We addressed this issue by using circular dichroism (CD) measurements to examine the series of TM-mimetic model peptides in a nonpolar organic phase (n-butanol), from which a scale of relative helical propensity for the 20 amino acids was constructed (Liu and Deber 1998b). When this scale was applied to protein databases, we observed a clear segregation between TM and non-TM helices; for example, a helicity value set at midscale selects correctly for 98% of the TM helices in a large database (Wang et al. 1999). These results suggested that in addition to the threshold hydrophobicity, there also exists a de facto minimum residue-dependent helicity requirement, which will promote and stabilize the folding of the nascent polypeptide chain within the membrane environment.

We have developed the program TM Finder, which automates and implements the concept of dual threshold hydrophobicity and nonpolar phase helicity requirements to locate protein TM segments from primary sequences alone. Here, we present the algorithm used for the TM Finder program, and discuss the refinements made to optimize the program for various protein sequences.

	Construction of the TM Finder program

TOP Abstract Introduction Construction of the TM... Results and discussion Conclusion References

 
Development of the hydrophobicity scale
A hydrophobicity value for each amino acid was assigned according to the HPLC retention time of each peptide KKAAAXAAAAAXAAWAAXAAA-KKKK-amide (Liu and Deber 1998a), which in practice ranged from 22.58 min (Phe) to 18.88 min (Lys). To obtain a dimensionless relative scale between 5 and -5, retention times were converted to hydropathy values by the equation,

x 10 - 5,where H and t represent hydrophobicity and HPLC retention time, respectively. An exception was made for Cys, which was synthesized with only one Cys in the middle ``X,'' and two Leu residues at the other two guest positions, to avoid excessive disulfide formation. the hydropathy of Cys then was calculated according to

which corresponds to the hydropathy of a peptide with three Cys substitutions. The Liu-Deber hydrophobicity scale, with relative residue rankings, is presented in Table 1, column 1. Comparative residue rankings for three established hydropathy scales, those developed by Kyte and DooLittle (1982), Engelman et al. (1986), and Eisenberg et al. (1984), are presented in columns 3, 4, and 5.

so that P = 1 corresponds to -30,000 deg cm²/dmol, a typical helicity value for a protein to adopt 50% -helical conformation in a nonpolar environment (Liu and Deber 1998b; Parker et al. 1992). This helicity scale, with relative residue rankings is given in Table 1, column 2.

TM Finder parameters
The original TM Finder program was prototyped using Microsoft Visual Basic and Excel. A web-enabled version of the program written in C and perl is available free to all users at http://www.bioinformatics-canada.org/TM/. Figure 1 illustrates the operation of the web version of the TM Finder program. The program accepts protein sequences in SWISS-PROT format, and then outputs TM segments based on the combined prediction from hydrophobicity and helicity. Hydrophobicity and helicity values are assigned to each amino acid, and then a sliding window is applied to calculate the moving average. TM segments are selected on the basis of residues, which exceed both the imposed threshold hydrophobicity and helicity levels. A gap joining operation (vide infra) was implemented to ligate segments within short breaks from the main segments. Users may adjust five parameters depending on the size and nature of the protein to produce the best prediction. These five parameters are: N-terminal window size, C-terminal window size, minimum core length, closed-gap length, and minimum segment length. The default settings of these parameters are values obtained from optimizing the five parameters against a collection of solved membrane protein structures (training sets) which are collected on the TM Finder website. It is emphasized that the program is designed to evaluate proteins that contain transmembrane -helices; membrane proteins such as the porins, which contain transmembrane ß-strands as components of ß-barrel structures, should be treated through algorithms developed specifically from this protein subset (Liu and Deber 1998a).

View larger version (73K): [in this window] [in a new window]   Fig. 1. TM Finder output taken directly from the website (http://www.bioinformatics-canada.org/TM/). The program accepts the primary sequence in FASTA format as input, and generates a dual hydropathy/helicity plot as a function of protein primary sequence. Hydrophobicity values (red line) and helicity values (blue line) are based on input from the scales given in columns 1 and 2 of Table 1. TM segments (yellow bars) are predicted based upon considerations of segment length and gap size, in conjunction with the combined requirements of threshold hydrophobicity (>0.4) and threshold helicity (>1.1); the dual threshold is shown as a horizontal line drawn through the prediction profile. The example presented is chain III of cytochrome C oxidase (SWISSPROT accession number: P00415). The sequence used to generate this plot is 1MTHQTHAYHM VNPSPWPLTG ALSALLMTSG LTMWFHFNSM TLLMIGLTTN 51MLTMYQWWRD VIRESTFQGH HTPAVQKGLR YGMILFIISE VLFFTGFFWA 101FYHS SLAPTP ELGGCWPPTG IHPLNPLEVP LLNTSVLLAS GVSITWAH HS 151LMEGDRKHML QALFITITLG VYFTLLQASE YYEAPFTISD GVYGSTFFVA 201TGFHGLHVII GSTFLIVCFF RQLKFHFTSN HHFGFEAGAW YWHFVDVVWL 251FLYVSIYWWGS261. The plot using default parameters is shown for illustration.

	Results and discussion

TOP Abstract Introduction Construction of the TM... Results and discussion Conclusion References

To a significant extent, hydrophobicity rankings of the 20 residues (Table 1, column 1) parallel helicity rankings (column 2). Inspection of the rankings reveals that five of the top six helix-promoting residues (F, L, I, M, and V) also are among the top six hydrophobes in the Liu-Deber scale. Yet a few residues do move drastically in their relative rankings, for example, the rankings of Gly and Trp in hydrophobicity (16th and 2nd, respectively) interchange versus their nonpolar phase helicity rankings (7th and 10th, respectively). As well, Ala is relatively less hydrophobic than it is a helix former in nonpolar phases. These circumstances suggest that hydrophobicity and helicity should be considered as uncoupled entities, and therefore the outcome of the prediction should improve when the two factors work in concert.

Gap-joining operation: Elimination of window size bias
Strict conformity to threshold hydrophobicity and helicity values can produce a lengthy list of predicted segments including many spikes. With the application of a small window size, we noted that the peaks tended to become narrow and break into several regions within one TM segment; such apparent shorter segments may arise in regions of local hydrophilicity (common to membrane transport proteins). In light of these observations, we modified TM Finder in the following manner. First, a variable m (usually set at 10) was set up, which allows the elimination of small segments with length m AA; and second, to resolve the problem of fragmentation when smaller window sizes are used, the program allows the fusion of neighboring smaller segments to a larger hydrophobic nucleus.

The importance of the gap-joining operation is demonstrated in Table 2. Cytochrome C oxidase had been chosen as the example because its crystal structure (Tsukihara et al. 1996) provides a good reference to determine the accuracy of the TM Finder prediction. As seen from Table 2, without the gap-joining operation (gap size = 0), two to three segments will be missed from the output. In addition, the prediction accuracy is highly dependent on the size of the sliding window chosen, i.e., the prediction accuracy is raised 7% simply by increasing the window size from 11 AA to 19 AA. With the application of gap size = 2 AA, all 12 segments of the protein are identified by the program, the prediction accuracy is improved by 6%–12%, and essentially the same level of accuracy is achieved regardless of the choice of window size. However, it must be noted that overuse of gap-joining operations can produce fused segments and result in reduced accuracy, as demonstrated in the case of gap size = 4 AA, and window size of 11 AA. The corrective action for fused segments is to reduce the gap size while at the same time increase the window size; when a larger window size is chosen, the resulting segments are more concentrated and have better separation. The eventual optimized choice (3 AA gap, 15 AA window) between gap size and window size that would allow the prediction of well-separated and reasonably sized segments is the choice that produces the best accuracy (81%).

View larger version (130K): [in this window] [in a new window]   Fig. 2. TM Finder prediction for M13 major coat protein, showing hydrophobicity (red trace) and helicity (blue trace). The sequence of the protein is 1AEGDDPAKAA 11FNSLQASATE 21YIGYAWAMVV 31VIVGATIGIK 41LFKKFTSKAS50. M13 major coat protein contains a single transmembrane segment that may extend maximally from 25–45 as deduced from nuclear magnetic resonance studies (Papavoine et al. 1998). Application of window size = 19 AA predicts the TM segment to occur at residues 19–38, while using a smaller window size = 7, the TM segment is identified at 23–42, closer to its experimentally determined location.

Globular protein database used to refine the prediction
In order to further codify the power of TM Finder to distinguish between membrane and nonmembrane proteins, we constructed a database composed of helices from globular proteins and from aqueous-based domains of membrane proteins, restricting the analysis to segments of comparable length to TM segments. Application of the threshold hydrophobicity criterion to this latter database of non-TM helices produced a few outliers containing 3–4 charged residues, but for which hydropathy is raised to just above the threshold value because of (1) an increased occurrence of aromatic residues (Tyr, Phe, Trp); and (2) an increased amount of Leu residues as compared to the majority of globular protein helices (i.e., those below the threshold). We have termed such helices -regions (Wang et al. 2000), which we speculate may occur as helices that function to bridge core-to-surface regions in globular protein structures. To eliminate false positives from this source, TM Finder has been programmed to flag potential TM segments, which contain three or more charged residues per 19 AA's.

Application of TM Finder to databases of membrane and nonmembrane proteins
To assess the generic ability of TM Finder to locate TM segments of membrane proteins, we constructed two databases. We first applied the program to a database of 21 protein subunits (containing 75 reported TM segments) in a set of membrane proteins considered to be solved to high resolution (Table 3) (see references herein and the TM Finder website). Recognizing that segment entry/exit points will vary from the specific positions reported from crystallography (data not shown), TM Finder correctly locates 67 of 75 TM segments, while mispredicting three segments, for a predictive value of a positive test of 67/70 = 96%. The segments underpredicted by TM Finder likely result from its stringency in that both hydrophobicity and helicity profiles must exceed the threshold for a positive prediction; thus, the program will occasionally bypass a Gly-rich and/or hydrophilic residue-rich (e.g., His) TM segment for which it computes insufficient hydrophobicity (example from Table 3: TM 3 of photosystem I [PsaA], residues 197–221: LNHHLAGLLGLGSLAWAGHQIHVSL). Comparison with three other hydropathy scales (Table 3) indicates roughly comparable performance for TM segment prediction in membrane proteins, viz., the KD scale correctly locates 70 segments while mispredicting eight (predictive value = 90%); the GES scale found 66 while mispredicting five (93%), and the Eisenberg scale found 73 while mispredicting 15 (83%).

View larger version (98K): [in this window] [in a new window]   Fig. 3. Transmembrane segment prediction profiles of human elastin (SWISSPROT accession number: P15502). (a) TM Finder profiles of hydrophobicity (red) and helicity (blue) (Liu and Deber 1998a,b), with the dual threshold shown as a horizontal line drawn through the prediction profiles. Hydrophobicity profiles for the same sequence are also shown for (b) the KD scale (Kyte and Doolittle 1982); (c) the GES scale (Engelman et al. 1986); and (d) the Eisenberg scale (Eisenberg et al. 1984). See text for a further discussion.

	Conclusion

TOP Abstract Introduction Construction of the TM... Results and discussion Conclusion References

	Acknowledgments

 
This work was supported, in part, by grants to C.M.D. from the Natural Sciences and Engineering Research Council of Canada, and the Canadian Institutes of Health Research. L.-P.L and C.W. acknowledge support from the Research Training Committee at the Hospital for Sick Children. We thank Dr. Raisa B. Deber for helpful discussions.

The publication costs of this article were defrayed in part by payment of page charges. This article must therefore be hereby marked ``advertisement'' in accordance with 18 USC section 1734 solely to indicate this fact.

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TOP Abstract Introduction Construction of the TM... Results and discussion Conclusion References

 
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