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Nestlé Research Center, CH-1000 Lausanne 26, Switzerland
Reprint requests to: Ziding Zhang, Nestlé Research Center, Vers-chez-les-Blanc, CH-1000 Lausanne 26, Switzerland; e-mail: Ziding.Zhang{at}rdls.nestle.com; fax: 41-21-785-9486.
(RECEIVED April 9, 2003; FINAL REVISION June 12, 2003; ACCEPTED June 17, 2003)
Supplemental material: See www.proteinscience.org
Article and publication are at http://www.proteinscience.org/cgi/doi/10.1110/ps.03131303.
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Abstract |
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 TOP Abstract IntroductionResults and DiscussionMaterials and methodsElectronic supplemental materialReferences |
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Keywords: Glycosyltransferase; fold recognition; sequence-similarity searching; protein structure prediction; structural genomics
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Introduction |
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TOPAbstractIntroductionResults and DiscussionMaterials and methodsElectronic supplemental materialReferences |
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At the sequence level, there are now a large number of open reading frames (ORFs) that correspond to GTFs. A database classifying GTF sequences into families based on sequence similarity and substrate/product stereochemistry is available and currently contains 56 potential families (see http://afmb.cnrs-mrs.fr/CAZY/; Campbell et al. 1997). At the 3D-structural level, currently only 13 GTF protein structures are available, which can be grouped into two folds: GTF A and GTF B. These two basic folds are shown in Figure 1
. The GTF A fold belongs to the 
/ß family, consisting of parallel ß-strands, flanked on both sides by -helices, and has been described as containing an N-terminal glyconucleotide donor-binding pocket and a C-terminal acceptor-binding domain (Unligil and Rini 2000). The GTF B fold is also a member of the /ß family. In contrast to the GTF A type of fold, the GTF B fold comprises two Rossmann-fold-like domains separated by a deep cleft. The glyconucleotide donor-binding pocket is located at the bottom of the cleft, where it interacts solely with the C-terminal domain, and the N-terminal domain is predicted to be responsible for acceptor binding (Unligil and Rini 2000). Undoubtedly, these structures provide a wealth of information about substrate binding, specificity, and possible catalytic mechanisms for most of the known GTFs (Gastinel et al. 2001; Persson et al. 2001; Tarbouriech et al. 2001).
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In addition to such sequence-based approaches for structural template identification, several fold-recognition techniques have been developed which incorporate structural information at a variety of levels. These fold-recognition methods are broadly classified into two categories based on the nature of the algorithm used. Profile-based methods operate by gathering both sequence and structural information (Rice and Eisenberg 1997; Kelley et al. 2000; Shi et al. 2001). Threading methods are based on mean force fields derived from databases of known structures (Godzik et al. 1992; Jones et al. 1992; Sippl 1995; Bryant 1996). These methods were developed to push fold recognition beyond the level of sequence-based similarity searches. The overall good performances of these techniques have been widely addressed in a series of Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiments (Levitt 1997; Murzin 1999; Sippl et al. 2001). In addition to providing the remotely homologous template to be used for comparative modeling in case-by-case studies, fold recognition has also been used in automatic prediction experiments. There, fold recognition was found to enhance genome annotation by suggesting 3D fold information for a number of genome sequences (Pawlowski et al. 2001).
In the field of comparative modeling of GTFs, fold recognition approaches have been used in case-by-case studies for identifying structural templates for the bovine -1,3-galactosyltransferase (Rao and Tvaroska 2001), porcine 3-galactosyltransferase (Imberty et al. 1999), and human 3-fucosyltransferase (de Vries et al. 2001). Studies have also adressed the occurence of specific and conserved peptidic motifs to identify remote homologs in the GTF family (Breton and Imberty 1999; Breton et al. 2002). However, to our knowledge, automatic fold assignment was not carried out on sequences from this protein family.
As stated earlier, the number of available GTF 3D structures is still quite limited, although the number of GTF sequences delivered by genome sequencing projects keeps increasing. Since remote homology is frequently found within the GTF family (i.e., different GTFs sharing the same fold at low sequence identity; Unligil and Rini 2000), fold recognition methods will be an important tool to direct and accelerate the mapping of GTF sequence landscape to protein structural space. To address the relationship between GTF sequences and structures, it is very natural and interesting to ask, "How many GTFs can find an adapted structural template among currently known protein structures?" And, "In addition to the GTF A and GTF B folds, should one expect some other GTF folds?" The present study attempted to answer these questions.
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Results and Discussion |
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TOPAbstractIntroductionResults and DiscussionMaterials and methodsElectronic supplemental materialReferences |
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The sizes of the clusters vary markedly. The largest cluster consists of 1043 sequences, containing almost 20% of all of the GTF sequences. The five largest clusters consist of 2866 sequences, amounting to 55% of all of the GTFs addressed in this paper. There are 132 singlet clusters.
Fold recognition
The 262 representative GTF sequences from the reduced data set were further processed by three different fold-recognition and two sequence-based searching methods. The results are summarized in Table 1. The fold recognition methods 3D-PSSM (Kelley et al. 2000), FUGUE (Shi et al. 2001), and GeneFold (Jaroszewski et al. 1998) provided confident fold assignments for 102, 138, and 74 representative sequences out of the 262 initial ones, respectively. When considering all GTFs represented by these seed sequences, we established that the three fold-recognition methods were able to confidently assign structure to 3695 (71.2%), 3774 (72.7%), and 3635 (70.1%) GTF sequences out of the initial 5188, respectively. The deduced success rates of the different fold-recognition methods were further compared to those obtained with sequence-based similarity searching methods (i.e., BLAST and PSI-BLAST). These were used to assign folds to the 262 GTF sequences by searching against a sequence database representing all known protein 3D structures (i.e., PDB sequence database; Berman et al. 2000). We found that the performance of every one of the three fold-recognition methods was significantly better than that of BLAST searching alone. Taking the results of 3D-PSSM as an example, one can confidently identify the folds adopted by the member sequences of 72 more clusters (about 1203 GTF sequences) compared to simple BLAST searching. In addition, fold recognition was also found to outperform PSI-BLAST-based searching. For example, 3D-PSSM was able to identify folds in 35 more clusters (about 373 GTF sequences) than PSI-BLAST did. In the present study, FUGUE confidently assigned folds to more cluster members than any other fold-recognition or sequence-based similarity searching method.
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. In most of the clusters where confident hits were found by BLAST and PSI-BLAST, fold-recognition methods assigned folds with high certainty. For example, in the 30 clusters where folds were identified by BLAST, 3D-PSSM and FUGUE assigned an identical significant top fold hit in 23 and 28 clusters, respectively. Furthermore, when considering the 67 clusters identified by PSI-BLAST, 3D-PSSM and FUGUE were able to identify an identical significant top fold hit in 52 and 62 clusters, respectively. In addition, largely consensual results were also found between 3D-PSSM and FUGUE. For those 102 folds identified by 3D-PSSM, FUGUE was able to provide an identical hit for 87 clusters. However, a poor performance for GeneFold was observed, as the results obtained with this method were quite different compared to the sequence-based searching and the other two fold-recognition methods. Indeed, GeneFold was able to identify the same top fold hit in only 32 and 28 clusters in comparison to the 67 and 102 folds assigned by PSI-BLAST and 3D-PSSM, respectively. The comparable performances of 3D-PSSM and FUGUE are certainly due to the similar fold-recognition algorithms used, as both fall in the category of the 1D/3D profile-based fold-recognition methods. More precisely, 3D-PSSM and FUGUE share the following three basic characteristics: (1) PSI-BLAST searching is included in both methods in order to make extensive use of sequence information; (2) both methods use structure-based profiles; and (3) the standards for identification of confident hits are well defined.
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A fully automated analysis was performed for query sequences with several significant hits identified by 3D-PSSM or FUGUE by considering all of these hits as well as their alignments. Of the 102 representative sequences confidently identified by 3D-PSSM, 48 sequences gave several confident hits, but after analysis we established that folds for two domains could be confidently assigned in the case of only four sequences. In the remaining 44 cases, we applied CE for structural similarity assessment, and found that 41 sequences share structurally related folds (ZCE > 4.2). In only three cases, structurally unrelated folds (ZCE 
4.2 for at least one pair of hits) are assigned to an identical region in the query sequence. Similarly, of the 138 sequences with confident top hits identified by FUGUE, several significant hits were assigned to 77 GTFs. In the case of only 15 sequences, folds for two unrelated domains were confidently assigned to the same query. In the remaining 62 cases, we applied CE for structural similarity assessment and found that 54 domains share structurally related folds. In only eight cases, structurally unrelated folds are assigned to an identical region in the query sequence. Compared with 3D-PSSM, FUGUE confidently identified more sequences with two domains, at the expense of an increased uncertainty in identifying structurally unrelated folds assigned to an identical region in the query sequence. Independently of the fold recognition methods being applied, we established that among the hits where two domains were confidently identified, in most of the cases the top-scoring fold belongs to the GTF A or GTF B class. Based on this observation, we took into account only the top hits in our further analysis.
Three types of contradictory results can be seen in Table 2
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The above results clearly demonstrate that structures can be successfully assigned to about 70% of the GTF sequences by the current fold-recognition methods, with a prediction rate higher than those obtained with sequence-based searching methods (i.e., BLAST and PSI-BLAST). Additionally, the results from 3D-PSSM and FUGUE are to a large extent in agreement, implying that the hits from these two methods are reliable. Finally, we have shown that a joint, jury-like prediction scheme combining the results of different fold-recognition methods enhances the confidence of fold assignment, and contributes to an increased detection rate of remote homologs (Lundström et al. 2001).
Structural clustering
To analyze and extract valuable information from the fold-recognition processing of the 262 representative sequences, we performed a structural clustering of all of the identified folds by again using CE structural alignment (Shindyalov and Bourne 1998). Consequently, a dissimilarity matrix was constructed, and the dimension was reduced by multidimensional scaling (MDS; Schiffman et al. 1981). We obtained in this way 2D maps of GTF structural space by using either 3D-PSSM or FUGUE. These maps are shown in Figure 2A and B, respectively. In Figure 2A, two main clusters can be found, with eight entries falling in the GTF A family and seven entries grouped in the GTF B class. The values of RMSDCE and ZCE between the two central, representative folds in the GTF A (1g8oA) and GTF B (1f6dA) clusters are 5.4 Å and 2.6 Å for 80 aligned residues, respectively. The numbers of GTFs sequences falling into the two basic clusters GTF A and GTF B are 2600 and 1057, respectively. Therefore, 3D-PSSM confidently identified nearly 70% (3657 of 5188) of GTFs sequences to adopt either the GTF A or the GTF B fold. However, one can see in Figure 2A the presence of five other points, representative of folds corresponding to 38 GTFs sequences. These points are significantly distant from the points forming the two main clusters GTF A and GTF B, and belong to the potential "new" GTF folds identified by 3D-PSSM.
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. Again two main clusters of points can be identified, which are representative of the GTF A and GTF B fold types, with seven and six structural entries, respectively. A total of 2587 GTFs sequences are affiliated with the seven structures falling in cluster GTF A, and 1113 sequences are related to the six structures in cluster GTF B. The values of RMSDCE and ZCE for the comparison between the central hits (1ll3A and 1iirA) in GTF A and GTF B clusters are 5.1 Å and 3.5 for 136 aligned residues, respectively. The remaining nine entries may be regarded as potential "new" GTF folds. Only 74 GTF sequences are related to these marginal structures.
"New" GTF folds
Our fold recognition studies demonstrated that unexpected folds could be assigned confidently to some of the GTF sequences, referred to as "new GTF folds." 3D-PSSM identified five such folds, and nine where found by FUGUE. It should be emphasized here that these "new" GTF folds share common folds with already known protein structures, and they are "new" only in that they differ from the GTF A and GTF B shapes. Taking into account the three clusters identified as "new" folds by both 3D-PSSM and FUGUE, the number of unique clusters with potential "new" folds is 11. Results from both methods are compared in detail in Table 3.
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+ß protein Ylr351C. This cluster contains only two sequences from the genome of Mycobacterium tuberculosis. The first sequence has a dolichol-phosphate-mannosyl transferase activity (Dpm1); the substrate specificity of the second sequence has not been identified. In this first cluster, we found that close to the common top hit generated by both 3D-PSSM and FUGUE were present hits with sufficiently high scores to be considered significant. It turned out that these were representative of the GTF A fold and are mapped in a region of the sequence different from the one generating the top hit (cf. Table 3). We therefore found a situation where a typical GTF fold is located on the same sequence together with a "new" fold of unknown function. More experimental work is necessary in this case to determine whether GTF function is retained when the GTF A domain is deleted. Only when this is verified can one conclude that a "new" GTF fold could be related to the sequences in this first cluster.
The second cluster contains 12 bacterial protein sequences, most of them exhibiting a cellobiose/cellodextrin phosphorylase activity. We found that a relevant template fold is 1h54A (maltose phosphorylase; MP), a dimeric enzyme that catalyzes the conversion of maltose and inorganic phosphate into ß-D-glucose-1-phosphate. Every monomer consists of an N-terminal complex ß-sandwich domain, a helical linker, an (/)6 barrel catalytic domain, and a C-terminal ß-sheet domain. In contrast to the first cluster, we found no indications that any part of this protein sequence will fit GTF A or GTF B templates. The top hit provided by 3D-PSSM or FUGUE maps in the same region of the sequence, and the alignment length matches on almost all of the template (cf. Table 3). Furthermore, it was established that the (/)6 barrel has an unexpectedly strong structural and functional analogy with the catalytic domain of glucoamylase from Aspergillus awamori. The only conserved glutamate of MP (Glu487) superposes onto the catalytic residue Glu179 of glucoamylase and likely represents the general acid catalyst. When we scrutinized the 3D-PSSM model generated for the representative protein sequence gi3172046 for this second cluster, we found that a homologous residue (Asp) maps close to MP Glu487 and glucoamylase Glu179. All of the described observations provide us good confidence to assign this type of fold to GTF sequences with cellobiose/cellodextrin phosphorylase activity.
Structural genomics target selection
As a key research topic in the postgenomic era, structural genomics aims to use high-throughput structure determination and computational analysis to provide a 3D structure for every known protein (Brenner 2000; Sali 2001). Currently exhaustive structural determination for all known proteins appears to be prohibitively expensive, and therefore the selection of a structurally nonredundant set of targets is of primary importance. The principal requirement for target selection is to define a relatively small set of proteins with new, currently unknown folds in an initial large collection of sequences (Portugaly and Linial 2000; Frishman 2002). The selection of such targets is a challenging task, because it is extremely difficult to predict whether a given sequence will point to a novel protein fold or not. However, there are encouraging indications that the total number of stable protein folds is limited (Chothia 1992; Portugaly and Linial 2000; McGuffin and Jones 2002).
We speculate that there are two possible situations when considering those GTFs with unassigned folds in our study. On one hand, some GTFs could share a common fold with proteins of known structure, but could not be detected by current fold-recognition methods. We expect that when applying different state-of-the-art fold-recognition methods on such GTF sequences, variable results would be obtained, with top hits situated somewhere in the lower limits of certainty. On the other hand, some GTFs could adopt novel, unknown protein folds. We expect that for such sequences, various state-of-the-art fold-recognition methods might provide consistently nonconfident hits (McGuffin and Jones 2002), although a systematic analysis of possible correlations between low statistical scores from fold-recognition methods (i.e., 3D-PSSM and FUGUE) and the likelihood of finding novel folds is still not available. Due to the poor performance of GeneFold, only the results from 3D-PSSM and FUGUE were jointly utilized for such a target selection. As pointed out by the authors of 3D-PSSM (Kelley et al. 2000), those hits with E-val3D-PSSM larger than 1.0 should be regarded as hits of low confidence. When applying 3D-PSSM, we identified 70 clusters with such low-confidence hits (Fig. S-1a in the Supplemental Material). Similarly, FUGUE identified 59 clusters with very weak hits (i.e., ZFUGUE < 3.0; Fig. S-1b). However, low-confidence and uncertain predictions were jointly provided by the two methods for only 30 of the 262 clusters. In this set of 30 unknown folds, 19 clusters include only one sequence (singlets), whereas the remaining 11 clusters account for 261 GTF sequences. We therefore ended up with these 261 sequences as the most promising targets for structural genomics studies of the GTF family. We took into account an argument by Frishman (2002), requiring the targets for a structural genomics study to represent not only novel folds, but also as much as possible of the sequences in the initial data set, and this mainly for reasons of cost-effectiveness. Details regarding these 11 clusters representative of the 261 target sequences are listed in Table S-2. Precise choices of candidates for structural determination should be further guided by feasibility studies of the expression, purification, and crystallization behavior of the targets.
Conclusions
The glycosyltransferase protein family is of particular interest for testing and validation of fold-recognition techniques because diverse amino acid sequences are known to adopt only two typical protein folds ensuring sugar synthesis. Three fold-recognition approaches (3D-PSSM, FUGUE, and GeneFold) were employed here to identify the folds of some 5188 GTF sequences. Taking the results from 3D-PSSM and FUGUE into account, the overall performance of fold recognition presented in this study is summarized in Tables 4 and 5. The results obtained indicate that current fold-recognition methods can identify confidently a fold for nearly 70% of all known GTF sequences with a confidence of at least 95%, improving on remote homolog identification by the most sophisticated sequence-based method (PSI-BLAST; Table 5). In most of the remaining 30% of sequences, we found a "hidden" relationship to GTF A or GTF B folds; that is, the top hits from fold recognition still point to GTF A/B but without a significant statistical score. We found that the FUGUE method performs slightly better than 3D-PSSM, which is evidenced by the consistently greater numbers appearing in the lower triangular part of Table 4. Generally, the results from 3D-PSSM and FUGUE are to a large extent in agreement, certainly due to the similar fold-recognition algorithms on which they are based. The high degree of degeneracy of GTF amino acid sequences in protein structural space was confirmed by 3D clustering of the significant hits. We were not able to confidently detect other currently known folds that could support glycosyltransferase function. However, an interesting evolutionary relationship has been identified among three folds exhibiting glucoamylase, maltose phosphorylase, and glycosyltransferase activities. In order to direct structural genomics efforts for GTFs structural determination, appropriate targets were selected from those GTFs for which the different fold-recognition methods in use were consistently unable to identify a fold type. The research strategy reported here would also be useful to map sequence space on the set of known folds (shapes) for other protein families.
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Materials and methods |
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TOPAbstractIntroductionResults and DiscussionMaterials and methodsElectronic supplemental materialReferences |
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The length of the sequences varies from 12 to 4573 amino acids, as illustrated in Figure 3. More precisely, 18 and 72 sequences have lengths shorter than 50 and 100 aa, respectively, and 245 are longer than 1000 aa. Sequences with chain lengths shorter than 50 aa were excluded, and in the final data set there were only 54 sequences with chain length between 50 and 100 aa. Our purpose was to find the compromise between a data set that will be adapted to fold-recognition studies, and one with maximized information content, containing even some fragments of GTF amino acid sequences. The cutoff of 50 aa was deduced from two different sources. First, we noted that the fold-recognition server validation LiveBench experiment (Bujnicki et al. 2001) automatically excludes from further analysis sequences shorter than 100 aa. Second, we performed some preliminary experiments with the FUGUE and 3D-PSSM methods, by sending to them several fragments derived from the N terminus of the GTF B fold adopted by the UDP-glucosyltransferase of Amycolatopsis orientalis (PDB entry 1iir
[PDB]
). Fragments were cut from the N terminus, spanning regions 1–30, 1–50, and 1–100, respectively. The FUGUE server recognized confidently all of the fragments and correctly identified structure 1iir
[PDB]
as the template. In contrast, 3D-PSSM was not able to identify confident hits for fragments 1–30 and 1–50, and picked 1iir
[PDB]
as a low-confidence template only for fragment 1–100. Based on these observations, we decided to apply the cutoff for chain length of 50 aa as a good compromise between the performance of current fold-recognition methods and the discovery spirit with which we tried to analyze sequence-structure relationships in the GTF protein family. On the other hand, sequences longer than 1000 aa often code for multidomain proteins, and cannot be processed by most of the current fold-recognition methods. In our study we also filtered out the sequences with lengths exceeding 1000 aa. Finally, 5188 GTF sequences (about 95% of the original 5451 GTFs) were kept for further study.
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The following algorithm was applied for clustering the GTF sequences: (1) A sequence is chosen at random as a seed for the current cluster. (2) A BLAST search is executed with this sequence as a query against all other GTFs. Sequences with E-value less than 1e-10 are assigned to the current cluster. (3) For those sequences newly assigned to the current cluster, a BLAST run is executed against the remaining GTF sequences to find possible new cluster members. In this step, we made an extensive use of similarity by transitivity in the sequence space (Yona et al. 2000). To prevent unrelated proteins from clustering together, a more strict standard was adopted at this step; that is, the qualified new member was required to have not only an E-value less than 1e-10, but additionally a similar sequence length [i.e., |L1-L2|/max(L1,L2) < 30%]. (4) The above step would be repeated until no sequence could be merged into the current cluster. (5) For each member in the newly built cluster, the Number of Directly Similar sequences within this cluster (NDS) was calculated by intracluster sequence-based cross-comparisons. The E-value for two directly similar sequences was again set to 1e-10. Then a representative sequence for the current cluster was selected, by choosing the one with a maximal value of NDS. (6) The same procedure is iterated for the remaining GTF sequences to build the other clusters. Eventually, 262 clusters were formed out of the original set of 5188 GTF sequences, and the 262 representative sequences were further processed by fold-recognition methods.
Fold recognition
3D-PSSM
3D-PSSM (Kelley et al. 2000) is a profile-based method relying on both multiple sequence alignments and multiple structural alignments. Central to the method is the so-called Three-Dimensional Position-Specific Scoring Matrix (3D-PSSM) that combines data from multiple-sequence profiles as implemented in PSI-BLAST with structure-based profiles, taking into account secondary structure and solvent accessibility. However, the truly innovative component of the approach resides in the use of structural alignments of remote homologs to generate sequence profiles that are accurately aligned yet more diverse than those generated through PSI-BLAST. The fold library for 3D-PSSM is based mainly on the SCOP database (Murzin et al. 1995) and included 7485 structures at the time that the present study was undertaken.
The 262 GTF sequences, forming the representative subset, were submitted automatically to the 3D-PSSM fold-recognition server (http://www.sbg.bio.ic.ac.uk/servers/3dpssm/) by running a Perl script. 3D-PSSM scans a submitted query sequence against its fold library, and potential homologs are suggested. Results were downloaded automatically for further analysis. According to the 3D-PSSM authors’ experience, all hits with E-val3D-PSSM less than 0.05 should be regarded as confident at the 95% certainty level.
FUGUE
FUGUE is a profile-based fold-recognition program, making extensive use of both multiple sequence and structural information (Shi et al. 2001). It is based on environment-specific substitution tables and structure-dependent gap penalties, where scores for amino acid matching and insertions/deletions are evaluated depending on the local environment of each amino acid residue in known structures (Shi et al. 2001). Given a query sequence, FUGUE scans its fold library, which is based on the HOMSTRAD database (Mizuguchi et al. 1998), calculates the sequence-structure compatibility scores, and produces a list of potential homologs and alignments. At the time the present study was performed, the FUGUE fold library contained 3914 templates.
By analogy to the protocol applied when using the 3D-PSSM fold-recognition server, the 262 sequences were sent to the FUGUE server (http://www-cryst.bioc.cam.ac.uk/~fugue/) automatically by running a dedicated Perl script. In addition, the results were automatically downloaded from the web site for further analysis. As pointed out by the authors Shi et al. (2001), hits with Z-scores larger than 6.0 should be considered confident at the 99% confidence level, and thus considered significant.
GeneFold
The third fold-recognition method we used is GeneFold (Godzik et al. 1992; Jaroszewski et al. 1998). Licensed by Tripos Inc., GeneFold is integrated into the SYBYL molecular modeling environment (SYBYL 6.8 2000). It uses both sequence and structural information to measure sequence-structure compatibility using three different scoring functions (Jaroszewski et al. 1998). The first scoring function evaluates sequence similarity only. The second scoring function evaluates a hybrid sequence/structure similarity score, where sequence, local conformational preferences, and burial terms are taken into account. The third, most elaborate scoring function derives a full hybrid score based on the compatibility of sequence, secondary structure, local conformational preferences, and burial terms between a query sequence and a structural template from the fold library. The results of sequence-structure matches using the above three functions are returned as a list of templates, ordered by decreasing scores, that are possible matches for the target sequence.
The original fold library distributed by Tripos Inc. consisted of 1824 entries representing all of the protein structures in the release of the PDB databank as of April 1998. In the past five years however, many protein structures with new folds have been deposited in PDB databank, and therefore the original GeneFold library was clearly outdated. For the purposes of our study, we updated the GeneFold library with all entries included in the 3D-PSSM fold library. At the time our study was preformed, 7485 protein structures were present in the 3D-PSSM fold library. However, as GeneFold supports a maximum size of 2500 structures per library, three new libraries were built up, with sizes of 2410, 2413, and 2414 structures, respectively. As can be seen, a total of 248 entries were not included in the libraries out of the 7485 initial ones, as GeneFold does not support structures with multiple conformations for the surface residues, with disordered chain terminals, or for which only the C coordinates are provided (Godzik et al. 1992).
The processing of the 262 sequences by GeneFold was executed on an SGI O2+ workstation by running a dedicated Perl script. For every one of the query sequences, GeneFold scanned the three libraries to find potential hits. Since GeneFold provides three different scores for a hit, we used a "jury" method to combine these three scores into a unique score (Lundström et al. 2001). Therefore, we did the following modifications:
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BLAST and PSI-BLAST searching
In a manner similar to that used for our fold-recognition studies, the subset of the 262 representative GTFs sequences was processed by the sequence-based similarity searching methods BLAST and PSI-BLAST. For this we used the standalone version of the BLAST program (Altschul et al. 1990, 1997). The nonredundant (NR) and structural (PDB) sequence databases were downloaded from (ftp://ncbi.nlm.nih.gov/blast/) in their updates dated 14 May 2002. The NR sequence database consists of all nonredundant GenBank CDS translation, PDB, SwissProt, PIR, and PRF entries (Altschul et al. 1990, 1997). The PDB database contains all of the sequences derived from protein structures deposited in the PDB Databank (Berman et al. 2000). BLAST searching was executed by using all 262 sequences as queries against the PDB sequence database. After an adjustment to the size of the NR database, all of the hits with an E-value less than 0.001 were considered confident.
PSI-BLAST is a sensitive sequence-similarity search method, performed in an iterative manner. First, an initial BLAST search is carried out, and the hits are ranked according to their alignment scores. Second, a profile in the form of a score matrix model is calculated from a certain number of the sequences taken from the top of the hit list. Third, an additional search is executed as a profile-sequence comparison using the generated score-matrix model to find a new set of hits. This search loop is repeated until no more new hits can be found or the maximum number of iterations is reached. To assign a fold to every GTF sequence, PSI-BLAST searching was executed in two stages. First, all 262 sequences were run against the NR sequence database by PSI-BLAST for three iterations. Based on the score matrix model built in this first search, we further searched with PSI-BLAST against the PDB sequence database for one round to find the potential structurally similar hits. The E-values for including sequences in the score matrix model and assessing the significant similar hits were both set to 0.001.
Confidence levels
The confidence levels provided for BLAST, PSI-BLAST, and 3D-PSSM are based on expectation values (E-values). By definition, the E-value is a parameter that describes the number of hits one can "expect" to see just by chance when searching a database of a particular size. It decreases exponentially with the score that is assigned to a match between two sequences. The lower the E-value, or the closer it is to zero, the more "significant" the match is. Currently, the most extensively studied E-value statistic is the one associated with BLAST. On the other hand, FUGUE uses an alternative scoring based on Z-scores, evaluated as the number of standard deviations above the mean score obtained by chance. Limited information is provided by the authors of both FUGUE and 3D-PSSM on the precise method of calculating confidence levels in general. However, an initiative such as LiveBench (Bujnicki et al. 2001) can provide some basis for the rationale of our results. The LiveBench project is a continuous benchmarking program for a number of participating fold-recognition servers. Every week the results are collected and evaluated using automated model assessment programs. The LiveBench experiment thus provides a simple evaluation of the sensitivity and specificity of the available servers and provides a way to assess the confidence of the obtained predictions. In the current LiveBench program, the 95% confidence levels for the 3D-PSSM and FUGUE servers are situated at cutoffs for E-values < 0.119 and for Z-scores > 4.8, respectively (cf. http://www.cs.bgu.ac.il/~dfischer/CAFASP3/summaries/thresholds.html). In our study, in order to declare a 3D-PSSM hit confident, we used an E-value cutoff of 0.05, as recommended by the authors of that method. Similarly, in order to declare a FUGUE hit confident, we applied a Z-score cutoff of 6.0, deduced by the FUGUE authors. In both cases, we used more restrictive cutoffs than the ones obtained in a real application, such as the LiveBench experiment. Therefore, we expect our assignments to be at least 95% correct in a CASP-like experiment.
Structural alignment
In order to rationalize the results from the fold-recognition studies and to establish the structural relationships among the identified hits, it is important to reliably assess protein structural similarity. More precisely, evaluation of protein structural similarity was needed mainly in the following two situations: (1) For the same query sequence, it was necessary to compare among them the hits obtained by the different fold-recognition methods. (2) In order to classify all the hits identified, a structural clustering was carried out based on an all-against-all comparison of the generated hits.
Several structural alignment methods have been developed (Taylor and Orengo 1989; Holm and Sander 1993; Shindyalov and Bourne 1998; Lu 2000). In our work we used CE, a structural alignment method proposed by Shindyalov and Bourne (1998). This algorithm involves a combinatorial extension (CE) of an alignment path defined by aligned fragment pairs, in contrast to the conventional techniques based on dynamic programming and Monte Carlo optimization. Two main parameters (RMSDCE and ZCE) for characterizing a given structural superposition are returned along with the resulting sequence alignment. The parameter RMSDCE is the root mean square deviation (Å) based on C positions in the two structures at the optimal superposition. ZCE is the z-score from the CE statistical model. Although the value of RMSDCE is intuitive to reveal structural similarity between two structures, it is not sufficient. For example, a structure alignment with a lower RMSDCE can be more significant than one with a higher RMSDCE if the number of aligned residues is greater in the first alignment. In the present study, ZCE was used to measure the structural similarity of the hits derived by fold-recognition methods. As pointed out by Shindyalov and Bourne, a family level similarity can be found for structures with ZCE 
4.5. In contrast, superfamily level similarity appears for structures with ZCE values between 4.0 and 4.5, whereas the similarity for those structures with ZCE 3.7 is usually very low. The source codes of the CE program were downloaded from http://cl.sdsc.edu/ce.html, and compiled for use in our local computer.
Structural clustering
Our study led us to the conclusion that an important structural degeneration is present among the otherwise diverse GTF amino acid sequences. For example, hits generated from some 102 of the 262 clusters map into only 20 different protein structures. As a matter of fact, this degeneration may be even higher, because some of these structures still share high similarity. To investigate the relationships between the hits produced by fold recognition, 3D clustering was undertaken by using the CE structural alignment method (Shindyalov and Bourne 1998). First, a structural dissimilarity function operating on two protein structures was defined as follows:
 | (2) |
where tanh is the hyperbolic tangent function, and the value of Dstr varies from 1.0 to 0.0 with the increase of the structure similarity (i.e., ZCE) between two hits. We used this type of sigmoid function to ensure smoothness properties for the dissimilarity function Dstr. First, we took the 20 hits generated by 3D-PSSM and we calculated Dstr between any pair of these, to obtain a 20 x 20 dissimilarity matrix. To provide a visual representation of the structural relationships among these 20 hits, we applied multidimensional scaling (MDS; Schiffman et al. 1981). In this way we reduced the dimension of the original 20 x 20 dissimilarity matrix to 20 x 2. Finally, structural similarity relationships were displayed as a 2D plot (see Fig. 2A). The structural relationship for the 22 different hits generated by FUGUE were derived similarly, and are displayed in Figure 2B.
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, GTF-A; 
, GTF-B; 
, potential new GTF folds).