. the prediction of domains from sequence information can improve tertiary structure prediction ( 1 ) , enhance protein function annotation ( 2 ) , aid structure determination ( 3 ) and guide protein engineering ( 4 ) and mutagenesis ( 5 ) .
a number of different methods have been developed to identify domains starting from primary sequences .
these methods can be roughly classified into four categories : template - based methods ( 610 ) , ab initio ( template - free ) methods ( 1122 ) , the hybrid approach combining template - based and ab initio methods ( 23 ) , and meta - domain prediction methods ( 24 ) . here
we describe an accurate , hybrid domain prediction server ( domac ) that integrates homology modeling , domain parsing and ab initio methods together . the preliminary implementation of the server [ under the name : foldpro ( 25 ) ] participated in the domain evaluation in the seventh edition of critical assessment of techniques for protein structure prediction ( casp7 ) ( 26,27 ) .
our hybrid approach uses the template - based method to predict domains for proteins having homologous template structures in protein data bank ( pdb ) ( 28 ) , and the ab initio method based on neural networks ( 29 ) to predict domains for de novo proteins .
first , it uses the psi - blast ( 30 ) to search the target sequence against ncbi non - redundant sequence database to construct a profile .
the profile is used to search a template structure library built from the proteins in pdb to identify templates , similarly as pdb - blast approach ( 31 ) .
second , if some significant templates are identified ( e - value 0.001 ) , it generates a structure model for the target using modeller ( 32 ) based on the template structures .
then it uses an accurate domain parsing tool pdp ( 33 ) to parse the model into domains .
if the parsed domains do not cover the whole target sequence , domac will assign uncovered regions to adjacent domains .
if no significant homologous template is found , domac will invoke the ab initio domain predictor dompro ( 29 ) to predict domains .
dompro uses neural networks in conjunction with sequence profile , predicted secondary structure , and relative solvent accessibility to predict domain boundary .
the secondary structure and relative solvent accessibility are predicted by sspro ( 34 ) and accpro ( 35 ) in the scratch suite ( 36 ) .
dompro tries to identify domain boundary positions based on the composition bias of sequence and structural features in domain linker regions .
the preliminary implementation of domac participated in casp7 and was ranked first among 13 domain prediction servers . since then , we have significantly speeded up the template identification process without sacrificing accuracy and added a module to update the template library weekly to incorporate the newly released proteins in pdb .
here we firstly describe the performance of the preliminary implementation of domac in casp7 ( under server name : foldpro ) . we compare it with 12 other server predictors in casp7 using two evaluation metrics : casp evaluation metric ( 37 ) and domain number accuracy .
casp metric ( ndo : normalized domain overlap score ) is to compute the overlapping score of domains without explicitly checking domain number and domain boundary ( 37 ) .
it computes the numbers of correctly and wrongly overlapped residues between true domains and predicted domains , respectively .
it summarizes the numbers of the overlapping residues into a single score to evaluate domain prediction .
the best score for a target is 1 and the worst score is 0 . the domain number accuracy is defined as the percentage of targets with correct domain number predictions .
the domain number accuracy is computed by comparing the domain number predictions with the official domain definitions released by casp7 . in terms of the two evaluation metrics ,
( % ) casp7 scorefoldpro ( domac)9593.70.963baker - rosettadom ( 23)9486.20.940ma - opus - dom9487.20.933robetta - ginzu ( 23)9484.00.932domssea ( 7)9478.70.910hhpred3 ( 38)9575.80.910meta - dp ( 24)9574.70.907hhpred1 ( 38)9375.30.902domfold9575.80.898dps(13)9375.30.889chop ( 22)8356.60.827distill ( 39)9570.50.819nn_put - lab9258.70.795the second column ( target num ) lists the number of targets for which a predictor made predictions . the performance of 13 domain prediction servers in casp7 the second column ( target num ) lists the number of targets for which a predictor made predictions .
we also evaluate domac on the three categories of casp7 targets : highly homologous , homologous and analogous / ab initio .
the domain number prediction accuracy of domac is 96% , 94% and 88% in the three categories , respectively .
however , because the majority ( 68 out of 95 ) of casp7 targets is single - domain proteins , the domain prediction accuracy is very likely over - estimated .
thus , we evaluate domac on a larger , balanced , high - quality dataset manually curated by holland et al .
the publicly released version of the holland 's benchmark2 dataset has 156 proteins consisting of 54 single - domain proteins , 69 two - domain proteins , 25 three - domain proteins , 4 four - domain proteins , 3 five - domain proteins and 1 six - domain protein .
we evaluate both template - based and ab initio methods on the whole dataset , respectively .
table 2 reports the specificity and sensitivity of each method in each category in terms of domain numbers .
the overall domain number prediction accuracy of the template - based and ab initio methods is 75% and 46% , respectively . 

table 2.the specificity and sensitivity of domain number prediction on the holland 's dataset using the template - based and ab initio methodsmethodacc .
( % ) 1-dom2-dom3-dom4-dom5-dom6-domtemplatesens.96.166.756.075.066.7spec.74.288.070.042.933.3ab initiosens.88.531.312.0spec.46.548.830.0 the specificity and sensitivity of domain number prediction on the holland 's dataset using the template - based and ab initio methods moreover , we assess the accuracy of the domain boundary prediction , which is important for generating hypotheses for crystallizing individual protein domains .
following the same convention ( 7,22 ) , a predicted boundary within 20 residues away from a true domain boundary is considered correct .
the domain boundary specificity and sensitivity is 50% and 76.5% for the template - based method , and 27% and 14% for the ab initio method .
thus , the accuracy are sufficient for guiding the crystallization experiment , whereas the ab initio method is not always reliable enough for the general , practical use .
since the reliability assessment of domain predictions is still an open issue , the user is advised to use the accuracy on the holland 's dataset to decide how to use these predictions .
the input form requires only three inputs : email address , target name , and protein sequence .
domac usually can make predictions within 15  min and send the results back to users through email .
domain prediction results include the user - defined target name , the protein sequence , the predicted domain number , the start and end positions of each domain and the method ( template - based or ab initio ) . for template - based prediction , it also reports the pdb codes of the templates .
domain 1 has two non - continuous segments , spanning from residues 1 to 16 and residues 82 to 208 , respectively .
the templates used to make the domain prediction are identified by pdb code + chain i d .
domain 1 has two non - continuous segments , spanning from residues 1 to 16 and residues 82 to 208 , respectively .
the templates used to make the domain prediction are identified by pdb code + chain i d .
we have developed a hybrid domain prediction web service integrating template - based and ab initio methods .
the template - based method is accurate enough for guiding protein structure prediction , structure determination , function annotation , mutagenesis analysis and protein engineering .
however , the ab initio method still needs to be improved for practical use . since protein domain architecture
is largely shaped by gene recombination events , such as gene fusion , fission , domain swapping and exon exchange , leveraging the evolutionary gene recombination signals embedded in the multiple sequence alignment of a protein family and exon boundaries ( or splicing sites ) in its gene structure , may help improve ab initio domain prediction significantly .
 protein domain prediction is important for protein structure prediction , structure determination , function annotation , mutagenesis analysis and protein engineering . 
 here we describe an accurate protein domain prediction server ( domac ) combining both template - based and ab initio methods . 
 the preliminary version of the server was ranked among the top domain prediction servers in the seventh edition of critical assessment of techniques for protein structure prediction ( casp7 ) , 2006 . 
 domac server and datasets are available at : http://www.bioinfotool.org/domac.html 
INTRODUCTION
IMPLEMENTATION
RESULTS
USE OF WEB SERVICE
CONCLUSION AND FUTURE WORK