BACKGROUND
proteins have complex three-dimensional  shapes, a fact well demonstrated by more than  <dig>  experimentally determined structures deposited in the current pdb database http://www.rcsb.org/pdb/home/home.do.
the number of unique protein folds  should be much smaller than the number of protein families defined by sequence similarity  <cit> .
as more structures are determined, it also becomes increasingly clear that the distribution of proteins between different folds is not even  <cit> .
although many folds have so far been observed for only a few proteins, some protein folds  occur frequently.
as reported by salem et al.
, the top ten superfolds could account for approximately one third of all proteins in the pdb database.
one of the top ten superfolds is the triosephosphate isomerase -barrel fold .
it was first observed in triosephosphate isomerase and consists of eight α-helices on the outside and eight parallel β-strands on the inside that alternate along the peptide backbone  <cit> .
in the past, many protein structures with the tim-barrel fold have been determined, which allow a more complete understanding of the fold space of the tim-barrel .
in the scop database   <cit> , the tim-barrel fold contains  <dig> superfamilies and  <dig> families .
as a common fold with multiple functions, tim-barrel proteins often function as enzymes.
they can catalyze five of the six categories of biochemical reactions  <cit> .
the evolution of the tim-barrel fold has also received considerable attention, and it has been established that the tim-barrel fold is one of the most ancestral folds  <cit> .
to identify the structural fold for a query protein sequence, classical sequence similarity searching methods  can be employed to scan the query protein sequence against others with known structures.
it is possible, however, that two structurally similar proteins may share weak sequence similarity .
marked improvements in detecting such remote homology relationships can be obtained using sensitive sequence-searching methods such as psi-blast  <cit>  and hidden markov models  <cit> .
in recent years, more powerful remote homology identification techniques called fold recognition or threading methods  have been elegantly developed as well.
the overall impressive performances of these algorithms, which combine different types of structural and sequence information, have been widely demonstrated in a series of casp experiments  <cit> , as well as in some real-time evaluation systems of structure prediction servers   <cit> .
the advantage of the above methods is that they are suitable for many protein fold types, but they may lack the specificity to recognize certain folds.
therefore, it is necessary to develop specialized computational tools for recognizing some important protein folds.
similar efforts have been successful in identifying some protein families, such as β-barrel membrane proteins  <cit> , g-protein coupled receptors  <cit>  and glycosyltransferases  <cit> .
to accelerate the exploration of the sequence-structure protein landscape in the tim-barrel fold, it is necessary to develop a specific and reliable method to detect tim-barrel proteins.
in this work, any measurement between two proteins can be regarded as a descriptor.
for instance, the e-value obtained from a blast search of protein a against protein b can be regarded as a descriptor between them.
based on such a broad definition, a great many descriptors have been developed in past decades, of which many can be used to measure the sequence similarity between two proteins.
because different descriptors may reflect different aspects of similarity between two proteins and can be complementary to a certain extent, the combination of well-performing descriptors can result in improved performance.
an example of such improvement is the generic fold recognition method developed in our previous work  <cit> .
based on a similar strategy, in this work we combined three descriptors into a prediction system with the assistance of support vector machine .
the three implemented descriptors are the sequence-alignment-based descriptor using psi-blast e-values and bit scores, the descriptor based on the alignment of secondary structural elements , and the descriptor based on the occurrence of prosite functional motifs  <cit> .
the proposed tim-barrel protein identification system, tim-finder, gives highly accurate results.
the details of the construction of the three descriptors and the svm-based predictor are reported.
the overall performance of tim-finder is also benchmarked against one of the state-of-the-art fold recognition methods, fugue, via a proteome-wide identification of tim-barrel proteins in the bacteria bacillus subtilis.
RESULTS
performance of the individual descriptors
in the present study, three descriptors were used to recognize tim-barrel proteins.
the three descriptors were individually benchmarked via a reference dataset called scop_10_mod, which contains  <dig> tim-barrel proteins and  <dig> structurally diverse non-tim-barrel proteins.
the details of the construction of the three descriptors, the compilation of the scop_10_mod dataset, and the evaluation procedures are outlined under methods.
the overall performance of the psi-blast-based descriptor was measured using receiver operator characteristic  analysis  <cit> , which plots true positive rate   as a function of false positive rate  .
the area under the roc curve  was also employed to assess the performance.
as shown in figure  <dig>  the psi-blast-based descriptor results in an auc value of  <dig> .
at a 5% fpr control, the psi-blast-based descriptor can correctly detect  <dig> % of tim-barrel proteins.
as a profile-based sequence searching algorithm, psi-blast has been widely applied in many aspects of protein structure and function prediction.
for instance, the psi-blast algorithm has been integrated into most state-of-the-art fold recognition methods  <cit> .
it also acts as a reference algorithm to benchmark any newly developed fold recognition method.
in this work, the psi-blast-based descriptor was used as a key component to construct our tim-barrel protein prediction system.
predicted secondary structure has long been proven to be helpful in protein fold classification and recognition  <cit> , and the ssea-based descriptor has been reported to be an effective way to consider the information of predicted secondary structure  <cit> .
as shown in figure  <dig>  the ssea-based descriptor performs the best, and it achieves an auc value of  <dig> .
at a fpr less than 5%, the ssea-based descriptor is able to successfully recognize  <dig> % of the tim-barrel proteins.
as reported in our previous study  <cit> , the psi-blast-based descriptor is much better than ssea at generic fold recognition.
interestingly, ssea is more powerful than the psi-blast-based descriptor in recognizing tim-barrel proteins.
generally, the tim-barrel fold has a well conserved 3d structure, which consists of eight β-strands and eight α-helices.
from n-terminus to c-terminus, the secondary structure of a typical tim-barrel fold is strictly arranged as β1-α1-β2-α2-β3-α3-β4-α4-β5-α5-β6-α6-β7-α7-β8-α <dig> , which may explain why the ssea descriptor is so powerful in recognizing tim-barrel proteins.
the performance of the ssea-based descriptor is further demonstrated in two tim-barrel proteins distant from one another in sequence space: 1vpqa  and 1i60a .
because the two proteins share a weak sequence similarity, the psi-blast-based descriptor fails to recognize their remote homologous relationship.
with a ssea score of  <dig> , however, the ssea-based descriptor is able to catch these two proteins' structural similarity.
the success of ssea should be ascribed to the overall conservation of secondary structure topology between these two proteins, which can be observed from their structural alignment derived from the ce algorithm  <cit>  .
the motif-based descriptor leads to an auc value of  <dig> , which is less impressive than the psi-blast- and ssea-based descriptors .
at a ≤ 5% fpr control, the motif-based descriptor only correctly recognizes  <dig> % of the tim-barrel proteins.
sequence motifs have been reported to correlate with protein folds  <cit> .
the central idea of the motif-based descriptor is to recognize tim-barrel proteins based on motif-fold compatibility.
in this work, we used the prosite database, because it is one of the most widely used and comprehensive sequence motif databases.
the prosite motifs are mainly defined as patterns  and profiles, which were derived from analysis of sequences of known function.
for each prosite motif, its compatibility with the tim-barrel fold was measured by a score called s. of the  <dig> motifs under investigation,  <dig> have s>  <dig> , including  <dig> patterns and  <dig> profiles.
as an illustrative example, we have provided the 3d model for a tim-barrel protein and the structural location of a prosite motif ps <dig> , which was analyzed as having the highest s score.
due to the functional diversity of tim-barrel proteins, the prosite motifs are obviously enriched in this fold.
therefore, the motif-based descriptor, which represents local sequence features of proteins, should be particularly suitable for recognizing tim-barrel proteins.
additionally, the motif-based descriptor is alignment independent, meaning that it should be complementary to the other two alignment related descriptors .
thus, it should be informative when combined with the other two descriptors, although the motif-based descriptor itself is not powerful.
performance of tim-finder
using svm, the psi-blast-, ssea- and motif-based descriptors were combined into a prediction system called tim-finder.
more details of the construction of tim-finder are available under methods.
the overall performance of tim-finder was further measured by the roc curve .
for the purpose of comparison, prediction based on the combination of psi-blast- and ssea-based descriptors was also carried out.
meanwhile, the result from the single psi-blast-based descriptor is also shown in figure  <dig> to provide a benchmark for tim-finder.
as shown in figure  <dig>  tim-finder results in a high auc value of  <dig> .
since the performance at low false positive rates is more important for real-world applications, the sensitivity values of tim-finder at 1%, 5% and 10% fprs are further listed in table  <dig>  with a 5% fpr rate control, tim-finder is able to correctly identify  <dig> % of the tim-barrel proteins, which is approximately  <dig> percentage points higher than the individual psi-blast-based descriptor and about  <dig> percentage points higher than the combination of the psi-blast- and ssea-based descriptors .
although the motif-based descriptor itself has an overall weak performance, it should be emphasized here that the motif-based descriptor does make an important contribution to the final performance of tim-finder , implying that it relies on quite different features from the psi-blast- and ssea-based descriptors.
generally, tim-finder has been benchmarked to have an excellent performance, implying it can be applied in practical use such as proteome-wide tim-barrel protein detection.
a the fprs at 1%, 5% and 10% mean that the corresponding specificity values are 99%, 95% and 90%, respectively.
comparison with the amino acid composition based svm model
as reported in the literature  <cit> , simple amino acid composition  based svm models have been widely employed for classification of proteins.
for comparison, a simple composition based method  was also developed to distinguish tim-barrel and non-tim-barrel proteins.
more details about the construction of aac_svm are available in methods.
due to the limited sequence information encoded by aac, the performance of aac_svm tends to be worse than tim-finder .
aac_svm achieves an auc value of  <dig> , which is much lower than that of tim-finder  .
at a 5% fpr control, aac_svm can correctly detect only  <dig> % of tim-barrel proteins, while the corresponding identification rate of tim-finder is up to  <dig> % .
comparison with the fugue fold recognition method
as mentioned, tim-barrel proteins can also be identified by state-of-the-art fold recognition methods.
therefore, it is also important to benchmark tim-finder against fold recognition methods.
in this work, tim-finder was benchmarked against the fugue fold recognition method, a profile-based fold-recognition program that makes extensive use of both sequence and structural information  <cit> , via a proteome-wide tim-barrel protein identification in b. subtilis.
for the purpose of comparison, tim-barrel protein identification based on a standard psi-blast search was also carried out.
more details about the proteome-wide computational experiments are available in methods.
of the  <dig>  b. subtilis protein sequences under investigation, tim-finder detects  <dig> tim-barrel proteins at a 1% frp control .
at the same confidence level, fugue and psi-blast recognize  <dig> and  <dig> tim-barrel proteins, respectively.
at a 5% fpr control, the performance of tim-finder is still better than that of fugue and psi-blast.
although fugue has been well benchmarked to be a leading fold recognition method  <cit> , tim-finder revealed an overall higher tim-barrel protein identification rate than fugue, confirming that it is really necessary to develop specific recognition methods for some important protein folds.
it is not surprising that both tim-finder and fugue can surpass the psi-blast search, because the psi-blast search was incorporated in tim-finder as well as in fugue.
it was also observed that the three methods are complementary to some extent .
considering the identification at a 1% fpr control, for instance, only approximately 80% of the tim-barrel proteins identified by tim-finder are also recognized by fugue.
to maximize a proteome-wide tim-barrel protein identification, a combination of different methods could therefore still be recommended.
aa 99% confidence level means a fpr rate ≤ 1% .
ba 95% confidence level means a fpr rate ≤ 5% .
a the comparison was based on a 99% confidence level.b the value outside the parentheses denotes the total number of b. subtilis sequences that were identified as tim-barrel proteins by both methods, whereas the value inside the parentheses represents the corresponding percentage, defined as the total number of consensual predictions from two methods divided by the total number of tim-barrel proteins identified by the single method appearing in the row.
however, the assessment of different methods based merely on the number of identified tim-barrel proteins in b. subtilis is still quite subjective.
in this work, the following efforts were made to allow a fair comparison.
first, the same nr database  was used in processing the above three methods.
second, all tim-barrel proteins in the fugue library  share sufficient sequence similarity with the tim-barrel proteins in the library of tim-finder , ensuring a fair comparison between tim-finder and fugue.
even with the above efforts, however, we are still not able to guarantee a fully unbiased assessment.
for instance, the fugue z-score threshold for different confidence levels was proposed by considering the recognition of all protein fold types, which may not be suitable for the recognition of tim-barrel proteins alone.
CONCLUSIONS
the proposed method tim-finder, incorporating the psi-blast-, ssea-, and motif-based descriptors, has been intensively benchmarked to have good performance, suggesting that it can serve as a powerful predictor to be practically applied in proteome-wide tim-barrel protein detection.
concerning future development, the following three aspects should be taken into account to obtain a more comprehensive prediction system.
1) from the viewpoint of structural biologists, it may be more interesting to target new tim-barrel superfamily proteins.
therefore, in the future version of tim-finder, we may consider including a prediction option to indicate whether a query sequence belongs to a new tim-barrel superfamily.
2) the current tim-finder is not able to provide a sequence alignment between the query sequence and the generated hit, which may limit its further application.
to solve this problem, a state-of-the-art profile-profile alignment algorithm  <cit>  can be employed.
3) the current tim-finder may lose some sensitivity in processing sequences with multiple domains.
therefore, a reasonable domain parser should be added as a preprocessing step in the future version of tim-finder.

 proteins have complex three-dimensional shapes, a fact well demonstrated by more than experimentally determined structures deposited in the current pdb database http://www.rcsb.org/pdb/home/home.do. the number of unique protein folds should be much smaller than the number of protein families defined by sequence similarity. as more structures are determined, it also becomes increasingly clear that the distribution of proteins between different folds is not even. although many folds have so far been observed for only a few proteins, some protein folds occur frequently. as reported by salem et al., the top ten superfolds could account for approximately one third of all proteins in the pdb database. one of the top ten superfolds is the triosephosphate isomerase -barrel fold. it was first observed in triosephosphate isomerase and consists of eight α-helices on the outside and eight parallel β-strands on the inside that alternate along the peptide backbone. in the past, many protein structures with the tim-barrel fold have been determined, which allow a more complete understanding of the fold space of the tim-barrel. in the scop database, the tim-barrel fold contains superfamilies and families. as a common fold with multiple functions, tim-barrel proteins often function as enzymes. they can catalyze five of the six categories of biochemical reactions. the evolution of the tim-barrel fold has also received considerable attention, and it has been established that the tim-barrel fold is one of the most ancestral folds. to identify the structural fold for a query protein sequence, classical sequence similarity searching methods can be employed to scan the query protein sequence against others with known structures. it is possible, however, that two structurally similar proteins may share weak sequence similarity. marked improvements in detecting such remote homology relationships can be obtained using sensitive sequence-searching methods such as psi-blast and hidden markov models. in recent years, more powerful remote homology identification techniques called fold recognition or threading methods have been elegantly developed as well. the overall impressive performances of these algorithms, which combine different types of structural and sequence information, have been widely demonstrated in a series of casp experiments, as well as in some real-time evaluation systems of structure prediction servers. the advantage of the above methods is that they are suitable for many protein fold types, but they may lack the specificity to recognize certain folds. therefore, it is necessary to develop specialized computational tools for recognizing some important protein folds. similar efforts have been successful in identifying some protein families, such as β-barrel membrane proteins, g-protein coupled receptors and glycosyltransferases. to accelerate the exploration of the sequence-structure protein landscape in the tim-barrel fold, it is necessary to develop a specific and reliable method to detect tim-barrel proteins. in this work, any measurement between two proteins can be regarded as a descriptor. for instance, the e-value obtained from a blast search of protein a against protein b can be regarded as a descriptor between them. based on such a broad definition, a great many descriptors have been developed in past decades, of which many can be used to measure the sequence similarity between two proteins. because different descriptors may reflect different aspects of similarity between two proteins and can be complementary to a certain extent, the combination of well-performing descriptors can result in improved performance. an example of such improvement is the generic fold recognition method developed in our previous work. based on a similar strategy, in this work we combined three descriptors into a prediction system with the assistance of support vector machine. the three implemented descriptors are the sequence-alignment-based descriptor using psi-blast e-values and bit scores, the descriptor based on the alignment of secondary structural elements, and the descriptor based on the occurrence of prosite functional motifs. the proposed tim-barrel protein identification system, tim-finder, gives highly accurate results. the details of the construction of the three descriptors and the svm-based predictor are reported. the overall performance of tim-finder is also benchmarked against one of the state-of-the-art fold recognition methods, fugue, via a proteome-wide identification of tim-barrel proteins in the bacteria bacillus subtilis.  performance of the individual descriptors in the present study, three descriptors were used to recognize tim-barrel proteins. the three descriptors were individually benchmarked via a reference dataset called scop_10_mod, which contains tim-barrel proteins and structurally diverse non-tim-barrel proteins. the details of the construction of the three descriptors, the compilation of the scop_10_mod dataset, and the evaluation procedures are outlined under methods. the overall performance of the psi-blast-based descriptor was measured using receiver operator characteristic analysis, which plots true positive rate as a function of false positive rate. the area under the roc curve was also employed to assess the performance. as shown in figure the psi-blast-based descriptor results in an auc value of. at a 5% fpr control, the psi-blast-based descriptor can correctly detect % of tim-barrel proteins. as a profile-based sequence searching algorithm, psi-blast has been widely applied in many aspects of protein structure and function prediction. for instance, the psi-blast algorithm has been integrated into most state-of-the-art fold recognition methods. it also acts as a reference algorithm to benchmark any newly developed fold recognition method. in this work, the psi-blast-based descriptor was used as a key component to construct our tim-barrel protein prediction system. predicted secondary structure has long been proven to be helpful in protein fold classification and recognition, and the ssea-based descriptor has been reported to be an effective way to consider the information of predicted secondary structure. as shown in figure the ssea-based descriptor performs the best, and it achieves an auc value of. at a fpr less than 5%, the ssea-based descriptor is able to successfully recognize % of the tim-barrel proteins. as reported in our previous study, the psi-blast-based descriptor is much better than ssea at generic fold recognition. interestingly, ssea is more powerful than the psi-blast-based descriptor in recognizing tim-barrel proteins. generally, the tim-barrel fold has a well conserved 3d structure, which consists of eight β-strands and eight α-helices. from n-terminus to c-terminus, the secondary structure of a typical tim-barrel fold is strictly arranged as β1-α1-β2-α2-β3-α3-β4-α4-β5-α5-β6-α6-β7-α7-β8-α, which may explain why the ssea descriptor is so powerful in recognizing tim-barrel proteins. the performance of the ssea-based descriptor is further demonstrated in two tim-barrel proteins distant from one another in sequence space: 1vpqa and 1i60a. because the two proteins share a weak sequence similarity, the psi-blast-based descriptor fails to recognize their remote homologous relationship. with a ssea score of, however, the ssea-based descriptor is able to catch these two proteins' structural similarity. the success of ssea should be ascribed to the overall conservation of secondary structure topology between these two proteins, which can be observed from their structural alignment derived from the ce algorithm. the motif-based descriptor leads to an auc value of, which is less impressive than the psi-blast- and ssea-based descriptors. at a ≤ 5% fpr control, the motif-based descriptor only correctly recognizes % of the tim-barrel proteins. sequence motifs have been reported to correlate with protein folds. the central idea of the motif-based descriptor is to recognize tim-barrel proteins based on motif-fold compatibility. in this work, we used the prosite database, because it is one of the most widely used and comprehensive sequence motif databases. the prosite motifs are mainly defined as patterns and profiles, which were derived from analysis of sequences of known function. for each prosite motif, its compatibility with the tim-barrel fold was measured by a score called s. of the motifs under investigation, have s>, including patterns and profiles. as an illustrative example, we have provided the 3d model for a tim-barrel protein and the structural location of a prosite motif ps, which was analyzed as having the highest s score. due to the functional diversity of tim-barrel proteins, the prosite motifs are obviously enriched in this fold. therefore, the motif-based descriptor, which represents local sequence features of proteins, should be particularly suitable for recognizing tim-barrel proteins. additionally, the motif-based descriptor is alignment independent, meaning that it should be complementary to the other two alignment related descriptors. thus, it should be informative when combined with the other two descriptors, although the motif-based descriptor itself is not powerful. performance of tim-finder using svm, the psi-blast-, ssea- and motif-based descriptors were combined into a prediction system called tim-finder. more details of the construction of tim-finder are available under methods. the overall performance of tim-finder was further measured by the roc curve. for the purpose of comparison, prediction based on the combination of psi-blast- and ssea-based descriptors was also carried out. meanwhile, the result from the single psi-blast-based descriptor is also shown in figure to provide a benchmark for tim-finder. as shown in figure tim-finder results in a high auc value of. since the performance at low false positive rates is more important for real-world applications, the sensitivity values of tim-finder at 1%, 5% and 10% fprs are further listed in table with a 5% fpr rate control, tim-finder is able to correctly identify % of the tim-barrel proteins, which is approximately percentage points higher than the individual psi-blast-based descriptor and about percentage points higher than the combination of the psi-blast- and ssea-based descriptors. although the motif-based descriptor itself has an overall weak performance, it should be emphasized here that the motif-based descriptor does make an important contribution to the final performance of tim-finder, implying that it relies on quite different features from the psi-blast- and ssea-based descriptors. generally, tim-finder has been benchmarked to have an excellent performance, implying it can be applied in practical use such as proteome-wide tim-barrel protein detection. a the fprs at 1%, 5% and 10% mean that the corresponding specificity values are 99%, 95% and 90%, respectively. comparison with the amino acid composition based svm model as reported in the literature, simple amino acid composition based svm models have been widely employed for classification of proteins. for comparison, a simple composition based method was also developed to distinguish tim-barrel and non-tim-barrel proteins. more details about the construction of aac_svm are available in methods. due to the limited sequence information encoded by aac, the performance of aac_svm tends to be worse than tim-finder. aac_svm achieves an auc value of, which is much lower than that of tim-finder. at a 5% fpr control, aac_svm can correctly detect only % of tim-barrel proteins, while the corresponding identification rate of tim-finder is up to %. comparison with the fugue fold recognition method as mentioned, tim-barrel proteins can also be identified by state-of-the-art fold recognition methods. therefore, it is also important to benchmark tim-finder against fold recognition methods. in this work, tim-finder was benchmarked against the fugue fold recognition method, a profile-based fold-recognition program that makes extensive use of both sequence and structural information, via a proteome-wide tim-barrel protein identification in b. subtilis. for the purpose of comparison, tim-barrel protein identification based on a standard psi-blast search was also carried out. more details about the proteome-wide computational experiments are available in methods. of the b. subtilis protein sequences under investigation, tim-finder detects tim-barrel proteins at a 1% frp control. at the same confidence level, fugue and psi-blast recognize and tim-barrel proteins, respectively. at a 5% fpr control, the performance of tim-finder is still better than that of fugue and psi-blast. although fugue has been well benchmarked to be a leading fold recognition method, tim-finder revealed an overall higher tim-barrel protein identification rate than fugue, confirming that it is really necessary to develop specific recognition methods for some important protein folds. it is not surprising that both tim-finder and fugue can surpass the psi-blast search, because the psi-blast search was incorporated in tim-finder as well as in fugue. it was also observed that the three methods are complementary to some extent. considering the identification at a 1% fpr control, for instance, only approximately 80% of the tim-barrel proteins identified by tim-finder are also recognized by fugue. to maximize a proteome-wide tim-barrel protein identification, a combination of different methods could therefore still be recommended. aa 99% confidence level means a fpr rate ≤ 1%. ba 95% confidence level means a fpr rate ≤ 5%. a the comparison was based on a 99% confidence level.b the value outside the parentheses denotes the total number of b. subtilis sequences that were identified as tim-barrel proteins by both methods, whereas the value inside the parentheses represents the corresponding percentage, defined as the total number of consensual predictions from two methods divided by the total number of tim-barrel proteins identified by the single method appearing in the row. however, the assessment of different methods based merely on the number of identified tim-barrel proteins in b. subtilis is still quite subjective. in this work, the following efforts were made to allow a fair comparison. first, the same nr database was used in processing the above three methods. second, all tim-barrel proteins in the fugue library share sufficient sequence similarity with the tim-barrel proteins in the library of tim-finder, ensuring a fair comparison between tim-finder and fugue. even with the above efforts, however, we are still not able to guarantee a fully unbiased assessment. for instance, the fugue z-score threshold for different confidence levels was proposed by considering the recognition of all protein fold types, which may not be suitable for the recognition of tim-barrel proteins alone.  the proposed method tim-finder, incorporating the psi-blast-, ssea-, and motif-based descriptors, has been intensively benchmarked to have good performance, suggesting that it can serve as a powerful predictor to be practically applied in proteome-wide tim-barrel protein detection. concerning future development, the following three aspects should be taken into account to obtain a more comprehensive prediction system. 1) from the viewpoint of structural biologists, it may be more interesting to target new tim-barrel superfamily proteins. therefore, in the future version of tim-finder, we may consider including a prediction option to indicate whether a query sequence belongs to a new tim-barrel superfamily. 2) the current tim-finder is not able to provide a sequence alignment between the query sequence and the generated hit, which may limit its further application. to solve this problem, a state-of-the-art profile-profile alignment algorithm can be employed. 3) the current tim-finder may lose some sensitivity in processing sequences with multiple domains. therefore, a reasonable domain parser should be added as a preprocessing step in the future version of tim-finder. 
the triosephosphate isomerase -barrel fold occurs frequently in the proteomes of different organisms, and the known tim-barrel proteins have been found to play diverse functional roles. to accelerate the exploration of the sequence-structure protein landscape in the tim-barrel fold, a computational tool that allows sensitive detection of tim-barrel proteins is required. when tested on the whole proteome of bacillus subtilis, tim-finder is able to detect tim-barrel proteins at a 99% confidence level, outperforming the psi-blast search as well as one existing fold recognition method. to develop a new tim-barrel protein identification method in this work, we consider three descriptors: a sequence-alignment-based descriptor using psi-blast e-values and bit scores, a descriptor based on secondary structure element alignment, and a descriptor based on the occurrence of prosite functional motifs. with the assistance of support vector machine, the three descriptors were combined to obtain a new method with improved performance, which we call tim-finder. tim-finder can serve as a competitive tool for proteome-wide tim-barrel protein identification.
 the triosephosphate isomerase -barrel fold occurs frequently in the proteomes of different organisms, and the known tim-barrel proteins have been found to play diverse functional roles. to accelerate the exploration of the sequence-structure protein landscape in the tim-barrel fold, a computational tool that allows sensitive detection of tim-barrel proteins is required.  to develop a new tim-barrel protein identification method in this work, we consider three descriptors: a sequence-alignment-based descriptor using psi-blast e-values and bit scores, a descriptor based on secondary structure element alignment, and a descriptor based on the occurrence of prosite functional motifs. with the assistance of support vector machine, the three descriptors were combined to obtain a new method with improved performance, which we call tim-finder. when tested on the whole proteome of bacillus subtilis, tim-finder is able to detect tim-barrel proteins at a 99% confidence level, outperforming the psi-blast search as well as one existing fold recognition method.  tim-finder can serve as a competitive tool for proteome-wide tim-barrel protein identification. the tim-finder web server is freely accessible at http://. /tim-finder/. 