integral membrane proteins account for 2030% of fully sequenced proteomes ( 1 ) . to date , two major structural architectures can be distinguished : the -helical and the -barrel membrane proteins .
the former are located primarily in cell membranes of eukaryotic cells and bacterial inner membranes , while the latter are found exclusively in the outer membranes of gram - negative bacteria and in the outer membranes of mitochondria and chloroplasts ( 24 ) .
the -barrel outer membrane proteins ( omps ) are crucial for the life of bacteria , serving a variety of diverse roles , such as passive nutrient uptake and active transport of large molecules , protein secretion , enzymatic activity or adhesion to host cells ( 58 ) .
the difficulty in obtaining crystals suitable for high - resolution studies of outer membrane proteins has resulted in their under - representation in the protein data bank ( 9 ) ( < 1% of all deposited proteins with known 3d structure ) . given these facts , and
because many -barrel omps nowadays attract an increased medical interest , several approaches have been made towards the development of predictive algorithms for this type of proteins .
these methods are based grossly on hydrophobicity ( 10 ) and statistical analysis ( 11,12 ) , remote homology detection ( 13 ) , hidden markov models ( hmms ) ( 1418 ) , feed - forward neural networks ( 1921 ) , radial basis function neural networks ( 22,23 ) and support vector machines ( 24 ) , whereas others like bomp ( 25 ) , tmb - hunt ( 26,27 ) and the tmb - finding pipeline ( 28 ) are oriented towards genome scale discrimination of -barrel membrane proteins .
a previously presented benchmark of several topology prediction methods indicated that hmms are the most reliable predictors ( 29 ) .
special purpose biological databases that include certain families of -barrel proteins , like tcdb ( 30 ) , pdbtm ( 31 ) , topdb ( 32 ) , psortdb ( 33 ) , tmbeta - genome ( 34 ) , opm ( 35 ) , mptopo ( 36 ) , membrane protein data bank ( 37 ) , prdns ( 38 ) , tmpdb ( 39 ) , tmfunction ( 40 ) and the hhomp database , as part of the hhomp webserver ( 13 ) have also been available to the public .
however , the annotation and classification of -barrels in the aforementioned databases is incomplete , the coverage is limited and there are also many false positives ( i.e. lipoproteins or peripheral proteins ) ; this urges the need for intensive studies and careful data collection regarding these proteins .
in order to create ompdb , we based our research along three main axes , namely the available 3d structures , the profile hmms ( phmms ) deposited in version 24.0 of the pfam database ( 41 ) that correspond to domains found solely in transmembrane ( tm ) -barrel omps , coupled with an extensive literature search for novel -barrel proteins that are reported as such but can not be retrieved from the databases otherwise .
the ultimate purpose was to build a classification system where each family ( i.e. a -barrel domain ) would be represented by a unique phmm .
we should emphasize here , that tm -barrel proteins could possess either a single - domain architecture ( where the whole sequence is composed of the -barrel domain ) , or a multi - domain architecture ( in which case the tm -barrel domain comprises only a portion of the sequence ) .
thus , following the philosophy of domain databases such as pfam , we identified only the respective domains that are responsible for the -barrel formation and subsequently we included in the database only proteins possessing these domains .
initially , we retrieved all proteins with a known 3d structure that are deposited in pdb and are listed at stephen white s laboratory page at uc irvine ( http://blanco.biomol.uci.edu/membrane_proteins_xtal.html ) .
we chose to exclude -barrels that do not originate from gram - negative bacteria , such as the mitochondrial voltage - dependent anion channel ( pdb i d : 2jk4 ) ( 42 ) , the mspa mycobacterial outer membrane channel ( pdb i d : 1uun ) ( 43 ) or the -hemolysin ( pdb i d : 7ahl ) ( 44 ) and lukf ( pdb id:3lkf ) ( 45 ) from staphylococcus aureus .
we studied the outer membrane -barrel protein superfamily clan ( mbb ; cl0193 ) of the pfam database which contains 36 members and noticed that there were several tm -barrel proteins with crystallographically solved structure whose respective pfam domains were not included in the clan .
such examples include scry ( pdb id:1a0s ) , ompla ( pdb id:1fw2 ) and pagp ( pdb id:1mm4 ) , which correspond to pfam domains pf02264 , pf02253 and pf07017 , respectively . salmonella typhimurium lpxr ( pdb id:3fid ) had a hit in a pfam domain which was neither included in the clan nor had any description for its function ( pf09982 ) . finally , two well - studied porins from rhodobacter capsulatus ( pdb id:2por ) and rhodopeudomonas blastica ( pdb id:1prn ) had no hits in the pfam domains .
apart from that , by searching in the literature , we came up with a number of proteins that were experimentally characterized as -barrel ( either multi- or single - domain ) omps using several techniques ( i.e. sub - cellular fractionization , electron microscopy , protease protection experiments , channel properties , chemical labeling , heat modifiable activity and so on ) .
many of them had either no hit in the phmms of pfam or matched any of the automatically generated alignments in the pfam - b subset .
for these proteins there was also no evidence concerning their sub - cellular location or structure in the uniprot database ( 46 ) .
in the first case , we performed a blast search ( 47 ) against uniprot using as query the -barrel domain of the experimentally verified protein(s ) reported in the respective published reports , we created multiple alignments with the best - scoring results using clustalw ( 48 ) and then , we built phmms for the particular domain using version 3.0b3 of the hmmer ( 49 ) software package . in the second case
, we constructed the phmms based on the domain alignments deposited in pfam - b after screening them in order to remove possible partial hits ( sequence fragments ) .
finally , we used the total set of phmms that was created for retrieving additional proteins from uniprot that scored high in these phmms , indicating that they could be classified in one of the available families .
these proteins were appended in the multiple alignments representing the respective -barrel domains and the procedure was repeated until no new members could be found .
the procedure for creating the phmm for the newly identified -barrel domains is illustrated graphically in figure 1 .
we have to notice , that some very short protein sequences ( i.e. less than100 amino acids ) that scored high against the phmms , were removed from the database since they could not possibly fold into a -barrel structure . 
 figure 1.schematic ilustration of the procedure used to create the phmms that represent the tm domains of -barrel omps .
either we were dealing with ( a ) single - domain omps or ( b ) multi - domain ones , following the literature we isolated the domain responsible for the -barrel formation .
afterwards , we performed a blast search ( 47 ) against uniprot using as query the -barrel domain of the experimentally verified protein reported in the respective published reports , we created multiple alignments with the best - scoring results using clustalw ( 48 ) and then , we built phmms for the particular domain using version 3.0b3 of the hmmer ( 49 ) software package .
the phmm was subsequently used to search for new family members and the procedure iterated until no new family members are found . for families that were represented in pfam - b subset ,
schematic ilustration of the procedure used to create the phmms that represent the tm domains of -barrel omps .
either we were dealing with ( a ) single - domain omps or ( b ) multi - domain ones , following the literature we isolated the domain responsible for the -barrel formation .
afterwards , we performed a blast search ( 47 ) against uniprot using as query the -barrel domain of the experimentally verified protein reported in the respective published reports , we created multiple alignments with the best - scoring results using clustalw ( 48 ) and then , we built phmms for the particular domain using version 3.0b3 of the hmmer ( 49 ) software package .
the phmm was subsequently used to search for new family members and the procedure iterated until no new family members are found . for families that were represented in pfam - b subset
the majority of proteins that we collected would not be possible to be gathered by just searching in uniprot using a combination of keywords , because they were mostly listed as having 
the result of the aforementioned procedure was a total of 85 families and 84 phmms ( one family has only one representative , thus no multiple alignment and , consequently , no phmm model could be created ) . at the end , we performed once again a literature search in order to find additional references for all the -barrel families ( i.e. domains ) that we included in the database .
another main innovation of ompdb is the annotation of the tm segments . for proteins with known 3d structure
, we used the information provided in the pdbtm database regarding the annotation of -barrels along with the number and location of -strands . for proteins that share structural homology with a protein with determined 3d structure (
i.e. they belong to the same family ) , we mapped the boundaries of the tm segments of the member with the crystallographically solved structure to their sequences , using the alignment of the family s sequences .
some obvious errors , such as a porin with 17 tm strands , were manually corrected . for families
for which there was no evidence for tm topology , we offer the user the opportunity to run the pred - tmbb algorithm , which is one of the top - scoring algorithms concerning -barrel omps prediction ( 14,15,29 ) .
ompdb , in its first version , contains 69  354 -barrel outer membrane proteins , originating from 2712 gram - negative bacterial species and strains , which are classified into 85 families .
figure 2 summarizes the annotation regarding the families classification system we propose in the pfam database , which is the largest collection of protein families in the literature .
however , if we just relied on the mbb clan , we would end up with only 36 profile hmm models that are identified by the curators of the database as being characteristic for -barrels . a more detailed analysis , initiated by literature findings , revealed another 22 models deposited in pfam - a , the high quality , manually curated component of pfam , which represent -barrels .
four out of them were actually assigned as domains of unknown function ( dufs ) , which lack further characterization in pfam .
we also used nine multiple sequence alignments ( msas ) from the automatically generated pfam - b subset , because some of the proteins they contained were found to be -barrels during the literature search .
both the 22 pfam - a models and the nine msas from pfam - b are not straightforward to be retrieved from uniprot or pfam just by using some kind of keywords combination in a text search query , since in most cases such keywords do not exist in the respective entries . more importantly ,
for 17 of ompdb s families , there was no information in the pfam database at all .
figure 3 shows a summary of the database s families and proteins entries classified according to their function . 

figure 2.annotation of ompdb s families in the latest version ( v.24october 2009 ) of the pfam database .
the majority of the families ( 36 ) is reported in the mbb clan part of the pfam database . however , many phmm models correspond to domains that represent families not included in the clan ( 18  +  4  =  22 families ) or they can only be found in the non - annotated pfam - b subset of pfam ( nine phmms )
. finally , out of the 84 phmm models that ompdb contains , 17 were not reported in pfam at all and these correspond to novel families . 

figure 3.a demonstration of the total numbers of families and protein entries in ompdb that belong to each of the eight functional categories that we created .
there are still a lot of families with -barrel proteins whose function remains unclear , whereas specific and non - specific channels , together with biogenesis / secretion proteins seem to play a vital role in the bacterial life .
receptors are under - represented in terms of the number of families , but they constitute the second largest category of -barrel omps in absolute numbers of proteins . in general ,
the largest proportion of outer membrane proteins in gram - negative bacteria serve as receptors or play a role in biogenesis / secretion .
the category of proteins with unknown function , though it contains a lot of families , has a relatively small number of protein members , which may be due to the progress in whole genome annotation that is being made regarding gram - negative bacteria .
annotation of ompdb s families in the latest version ( v.24october 2009 ) of the pfam database .
the majority of the families ( 36 ) is reported in the mbb clan part of the pfam database .
however , many phmm models correspond to domains that represent families not included in the clan ( 18  +  4  =  22 families ) or they can only be found in the non - annotated pfam - b subset of pfam ( nine phmms ) . finally , out of the 84 phmm models that ompdb contains , 17 were not reported in pfam at all and these correspond to novel families .
a demonstration of the total numbers of families and protein entries in ompdb that belong to each of the eight functional categories that we created .
there are still a lot of families with -barrel proteins whose function remains unclear , whereas specific and non - specific channels , together with biogenesis / secretion proteins seem to play a vital role in the bacterial life .
receptors are under - represented in terms of the number of families , but they constitute the second largest category of -barrel omps in absolute numbers of proteins . in general ,
the largest proportion of outer membrane proteins in gram - negative bacteria serve as receptors or play a role in biogenesis / secretion .
the category of proteins with unknown function , though it contains a lot of families , has a relatively small number of protein members , which may be due to the progress in whole genome annotation that is being made regarding gram - negative bacteria .
the database possesses a user - friendly environment , through which the user may retrieve all the necessary information or find available resources and cross - references .
the web application is based on the combination of two layers : the underlying level is a mysql database system , which contains all protein data and the upper layer is an apache - php applications server that receives user queries and fetches populated html data to the web browser client . from the welcome page of the web site , the user can view a brief description of ompdb , along with the current holdings .
a selection of a family entry from the drop down menu in the same page redirects to the respective family page .
there , a short description of the family is presented along with the literature references , the initial ( seed ) and full alignments of the family s members ( figure 4 )
the user can read a short description for the given family together with a number of respective literature references . by clicking on the respective web links
, he / she can view all proteins that belong to the family or download the seed and full alignments of the family s protein members .
the user can read a short description for the given family together with a number of respective literature references . by clicking on the respective web links
, he / she can view all proteins that belong to the family or download the seed and full alignments of the family s protein members . from the navigation bar in every web page ,
there is a text search page , in which several fields are presented to the user , allowing the performance of advanced text search queries .
there is also the opportunity to submit blast searches against the database s protein members using the blast search tool , where an e - value cut - off level can be specified accordingly .
additionally , through the domain search tool , one or more fasta - formatted sequences may be submitted and searched against the collection of profile hmms that was constructed and is proven to be characteristic for -barrel omps .
this search can rely on either an e - value cut - off level or the manually specified trusted cut - off scores of each model .
the results in all cases are presented in a tabular way , which facilitates easy view of the main fields of each result entry and can be selected and downloaded locally for further investigation .
each database entry contains the following fields : ompdb name , ompdb i d , uniprot accession number , protein description and classification , sequence , species , organism name , taxonomy , links to other databases , accompanied with annotation for tm segments and signal peptides ( figure 5 ) .
there is also an extensive user s manual page , describing in detail the available tools . 

the user may observe the classification of the protein , the available cross - references , the amino acid sequence , along with information about the presence of a signal peptide and the annotation of the tm segments .
all this information can be downloaded in easy - to - use formats as well ( fasta , xml or text files )
the user may observe the classification of the protein , the available cross - references , the amino acid sequence , along with information about the presence of a signal peptide and the annotation of the tm segments .
all this information can be downloaded in easy - to - use formats as well ( fasta , xml or text files ) .
we have constructed a relational protein database , which contains -barrel outer membrane proteins from gram - negative bacteria . to our knowledge , ompdb has some innovative and unique features not available in any other publicly accessible resource .
when compared to general protein or domain databases , like uniprot and pfam ( which were actually the main sources of information ) , ompdb presents a more complete classification and accurate annotation concerning -barrel proteins , due to the added value of the manual annotation that we performed and the detailed literature references that we provide . on the other hand , a comparison of ompdb against other specific databases that contain -barrel proteins , reveals that it clearly excels at all aspects , because it features the largest number of protein and family entries , it possesses the most complete and exclusive data for -barrels and offers the most complete interconnection to other public databases , literature references , prediction tools and sequence annotation .
table 1 shows a summary of the comparison of the main characteristics of ompdb as opposed to other related databases that are available to the scientific community . from the table
, it is evident that related databases fall into two main categories : there are those with a very limited number of protein entries and either no classification system for them or families with just few representative proteins .
the reason for this is that they contain only proteins that have a crystallographically ( 3d ) determined structured ( like tcdb , pdb_tm , topdb , opm , mpdb and tmpdb ) . on the other hand , there are databases like tmbeta - genome , prnds and psortdb that contain a larger number of proteins , but they offer no classification system and additionally , the proteins are gathered using automated techniques ( i.e. prediction algorithms ) .
we should note here , that prediction methods produce a large number of false positives and thus , the content of these databases need additional filtering ( i.e. they contain several outer membrane lipoproteins or peripheral proteins ) .
it is clear thus , that the semi - automated procedure used in the development of ompdb , combines the advantages of both approaches offering currently the most advanced resource for omps . 

table 1.a comparison of ompdb to other databases that include -barrel outer membrane proteins , in terms of total entries , method of data retrieval , existence of literature references , annotation of important sequence features ( such as the signal peptide and the tm segments ) , interconnection to other public databases related to -barrel proteins and availability of prediction / search toolsdatabase featuresompdb ( current work)pfam ( mbb clan ) ( 41)tcdb ( 30)pdb_tm ( nr set ) ( 31)topdb ( 32)epsortdb ( 33)tmbeta genome ( 34)opm ( 35)mpdb ( 37)prnds ( 38)tmpdb ( 39)hhompdb ( 13)total entries69  35438  595348494548036  0957314831821754  184total families8536572625data retrievalsemi-
automatedsemi- automatedmanualsemi- automatedsemi- automatedmanualautomatedsemi- automatedmanualautomatedmanualautomatedliterature references+++++++signal peptide/ tm segments annotation+/+///++/+///+//+/+/number of databases cross - referenced13511223223prediction / search
toolsblast/ hmmer/ pred - tmbb/ text searchtext search/ hmmertext search/ blasttext searchtext searchpsortb/ blastsearch by organismtext searchtext searchtext search/ bss / bbftext searchhhsearch a comparison of ompdb to other databases that include -barrel outer membrane proteins , in terms of total entries , method of data retrieval , existence of literature references , annotation of important sequence features ( such as the signal peptide and the tm segments ) , interconnection to other public databases related to -barrel proteins and availability of prediction / search tools ompdb aims to fill a gap in the literature , because as already mentioned , the annotation of tm -barrels in the existing databases is rather inadequate
. experimentalists that are involved with the biochemical and functional characterization of outer membrane proteins of gram - negative bacteria will probably benefit the most from our database .
for instance , a blast or domain search query against ompdb , which would follow the identification of one or more -barrel protein sequences , is expected to be much more informative than a usual query against uniprot , the non - redundant ( nr ) sequences of ncbi or pfam . moreover , even in the case that a similarity ( even a remote one ) is not found , the existing prediction methods that have been developed in our lab ( pred - tmbb and conbbpred ) , will provide the necessary tools towards the clarification of the structural and functional nature of these proteins .
another aspect in which ompdb could be very useful is the computational and theoretical analyses concerning -barrels .
the classification into families might be used in studies regarding the attributes of their members , in phylogenetic analyses and/or comparative genomics .
the use of the phmms that are deposited in the database could also be used for designing effective strategies for whole - genome analyses .
finally , the existence of such a large and reliable data set of -barrels can be used for large - scale analyses concerning the classification accuracy of existing predictors , for training new prediction methods or for comparative modeling approaches .
we permit the users to download the entire database , in various easy - to - use formats , for those bioinformaticians and/or experimentalists who would like to access a frequently updated , high - quality annotationed data set of -barrel outer membrane proteins .
we also used the cd - hit program ( 50 ) in order to provide nr subsets of the database at various levels , i.e. 30% ( 4225 proteins ) , 50% ( 15  029 proteins ) , 70% ( 23  110 proteins ) and 90% ( 31  327 proteins ) sequence similarity .
the database may also be useful for microbiologists whose aim is to identify potential targets in bacterial genomes for medical diagnostics , vaccines , antimicrobials and other uses . our long - term goal is to keep ompdb as up - to - date as possible , following the regular updates of uniprot and searching in the literature at the same time , for novel , experimentally verified -barrel proteins , in order to include them in the database or appoint them to a new family if necessary .
similar to other databases , ompdb is an ongoing project and interaction with the user community is vital for its development and refinement .
we encourage the submission of data , correction of errors , and suggestions for making ompdb of greater use to the scientific community .
 we describe here ompdb , which is currently the most complete and comprehensive collection of integral -barrel outer membrane proteins from gram - negative bacteria . 
 the database currently contains 69  354 proteins , which are classified into 85 families , based mainly on structural and functional criteria . 
 although ompdb follows the annotation scheme of pfam , many of the families included in the database were not previously described or annotated in other publicly available databases . 
 there are also cross - references to other databases , references to the literature and annotation for sequence features , like transmembrane segments and signal peptides . 
 furthermore , via the web interface , the user can not only browse the available data , but submit advanced text searches and run blast queries against the database protein sequences or domain searches against the collection of profile hidden markov models that represent each family s domain organization as well . 
 the database is freely accessible for academic users at http://bioinformatics.biol.uoa.gr/ompdb and we expect it to be useful for genome - wide analyses , comparative genomics as well as for providing training and test sets for predictive algorithms regarding transmembrane -barrels . 