such proteins perform many basic functions including cell signalling , transcription regulation , energy conservation and transformation , and ion exchange .
membrane proteins are difficult to study , since they are inserted into lipid bilayers and expose to the polar outer and inner environments portions of different sizes .
it is therefore difficult to purify them in the native , functional form and even more difficult to crystallize them . for such technical reasons , only a small fraction of the protein data bank structures
are membrane proteins ( < 1% of the total number of structures and far less than their estimated abundance in cells ) ( 1 ) .
there are a number of computational methods available to predict the topology of membrane proteins , which consists of two basic features : ( i ) the location of transmembrane domains along the protein chain and ( ii ) the location of the n- and c - termini with respect to the lipid membrane .
topological models are sufficient in many instances to design simple experiments in order to prove the location of the n- and c - protein termini , that of the inner and outer loops with respect to the membrane plane , and concomitantly the number of transmembrane segments in the chain
. however , the best - performing methods are offered at different servers and are endowed with different graphical interfaces .
this hampers the direct comparison of predictions , especially for experimentalists interested in comparing their results with computational methods .
currently , two other web servers of which we are aware ( 2,3 ) are available and separately comprise two of the predictors that we implement tmhmm2.0 ( 4 ) and memsat ( 5 ) .
the novelty of our web server is to include in the same framework ensemble ( 6 ) and prodiv ( 7 ) , two powerful methods that became available only recently .
furthermore the available web servers render only the consensus prediction , without allowing a critical discrimination among different predictions that may be useful , considering that different predictors may highlight different properties , depending on the different implementation .
automatic topology annotation for membrane proteins has been included among the efforts of the biosapiens european network of excellence ( ) with the specific aim of taking into consideration different predictors for annotating membrane proteins in the human genome .
the common platform for these efforts is the biosapiens distributed annotation servers ( das ) ( ) . in this context
, the pongo - das server ( ) provides topology annotation for the all - alpha membrane proteins of the human genome .
this is done using the das protocol ( ) to answer at das queries that can be seen using specific visualizers such as dasty ( 8) or ensembl ( 9 ) . in order to allow users to browse directly the pre - computed transmembrane annotations
the annotation is carried out using four selected predictors , namely ensemble1.0 ( 6 ) and prodiv ( 7 ) , in order to allow a direct comparison of the topology prediction for the sequence at hand .
the server has also been set up to make it possible to predict the topology of any putative membrane proteins of interest regardless of provenance .
the topological models computed by the different predictors can be directly obtained simply by pasting in a box the sequence of interest and looking at the results .
recently developed web technologies ( e.g. ajax ) are used to improve the user interface .
the website implements the following predictors : memsat is a new version of the memsat predictor of transmembrane helices in proteins ( 4 ) .
tmhmm2.0 which is a predictor of transmembrane helices in proteins based on hidden markov models ( 5 ) .
it has been shown that it performs quite well taking into account that it uses only single sequence information .
ensemble1.0 is an ensemble of two hidden markov models and one neural network ( 6 ) .
ensemble takes also advantage of the evolutionary information derived by multiple sequence alignment , both for the neural network and the hidden markov model systems .
prodiv_tmhmm_0.91 is a recent predictor of transmembrane helices in proteins ( 7 ) which uses a hidden markov model similar to tmhmm , but exploits the evolutionary information derived by multiple sequence alignment .
spep is a signal peptide predictor based on combination of neural networks ( 10 ) .
this predictor has performance similar to the most widely used signalp ( 11 ) , and it has been included since it is quite common that signal peptides are mispredicted as transmembrane helices . in the context of the biosapiens project , we downloaded the uniprot dataset ( september 22 , 2004 ) which consists of 33  135 human protein sequences , and for future use also the ipi dataset ( august 5 , 2004 ) which consists of 46  782 human proteins .
the union of the two datasets comprises 50  600 sequences from the human genome .
it is worth noticing that these 50 600 sequences do not include the known splice variants , since those variants are not presently included as unique uniprot codes .
the choice of annotating uniprot sequences is well justified from the fact that ensembl gene products ( in contrast to the uniprot entries ) are not stable ; for instance , only 60% of the sequences are common ( and conserved ) between the ensembl releases 34 and 35 , respectively .
we then use four state - of - the - art predictors already described in the literature in order to identify the most probable integral membrane proteins . in this way
the union of the predictions obtained with the four methods gives a set of likely membrane proteins , while the intersection contains those chains on whose annotation as membrane proteins all the predictors agree upon .
the pre - computed annotations are filtered with spep before being processed by the transmembrane predictors .
this is done since it is quite common that signal peptides are mispredicted as transmembrane helices by all the predictors implemented in our web server . in the case of a positive spep answer
, the system cuts the corresponding predicted segment and processes the remaining part of the sequence with the four transmembrane predictors .
the website implements the following predictors : memsat is a new version of the memsat predictor of transmembrane helices in proteins ( 4 ) .
tmhmm2.0 which is a predictor of transmembrane helices in proteins based on hidden markov models ( 5 ) .
it has been shown that it performs quite well taking into account that it uses only single sequence information .
ensemble1.0 is an ensemble of two hidden markov models and one neural network ( 6 ) .
ensemble takes also advantage of the evolutionary information derived by multiple sequence alignment , both for the neural network and the hidden markov model systems .
prodiv_tmhmm_0.91 is a recent predictor of transmembrane helices in proteins ( 7 ) which uses a hidden markov model similar to tmhmm , but exploits the evolutionary information derived by multiple sequence alignment .
spep is a signal peptide predictor based on combination of neural networks ( 10 ) .
this predictor has performance similar to the most widely used signalp ( 11 ) , and it has been included since it is quite common that signal peptides are mispredicted as transmembrane helices .
in the context of the biosapiens project , we downloaded the uniprot dataset ( september 22 , 2004 ) which consists of 33  135 human protein sequences , and for future use also the ipi dataset ( august 5 , 2004 ) which consists of 46  782 human proteins .
the union of the two datasets comprises 50  600 sequences from the human genome .
it is worth noticing that these 50 600 sequences do not include the known splice variants , since those variants are not presently included as unique uniprot codes .
the choice of annotating uniprot sequences is well justified from the fact that ensembl gene products ( in contrast to the uniprot entries ) are not stable ; for instance , only 60% of the sequences are common ( and conserved ) between the ensembl releases 34 and 35 , respectively .
we then use four state - of - the - art predictors already described in the literature in order to identify the most probable integral membrane proteins . in this way
the union of the predictions obtained with the four methods gives a set of likely membrane proteins , while the intersection contains those chains on whose annotation as membrane proteins all the predictors agree upon .
the pre - computed annotations are filtered with spep before being processed by the transmembrane predictors .
this is done since it is quite common that signal peptides are mispredicted as transmembrane helices by all the predictors implemented in our web server . in the case of a positive spep answer ,
the system cuts the corresponding predicted segment and processes the remaining part of the sequence with the four transmembrane predictors .
pongo has two different usage options : 
 pongo - das accessing the pre - computed annotations using keywords or das queries;pongo - pred that is an enhanced and modern version of a standard  through - the - web server application .
pongo - das accessing the pre - computed annotations using keywords or das queries ; pongo - pred that is an enhanced and modern version of a standard  through - the - web server application . in the case of pongo - das we implemented two types of result visualization : 
 by means of a das , which is a client  server system in which a single client integrates information from multiple servers .
it allows a single machine to gather up genome annotation information from multiple distant web sites , collate the information , and display it to the user in a single view .
little coordination is needed among the various information providers , anda user - friendly interface that allows the search for a specific prediction.through the das protocol a web client like ensembl can obtain the list of the annotations for each human protein using its uniprot code as requested for the das queries ( ) ; in order to check this url the client needs a das client such as the one that will be embedded in ensembl pages at ebi and a uniprot code for the sequence .
a user can directly query the database , without using the das infrastructure , using the web interface available at   ( figure 1 ) the user can then use a crc64 ( the sequence hash code ) , or the sequence uniprot or ipi code to get the predictions in a graphical view .
an example of a sequence filtered with the different predictors is presented together with the detailed sequence annotation ( figure 2 ) .
a colour code is used to quickly identify the transmembrane segments . by means of a das , which is a client  server system in which a single client integrates information from multiple servers .
it allows a single machine to gather up genome annotation information from multiple distant web sites , collate the information , and display it to the user in a single view .
little coordination is needed among the various information providers , and a user - friendly interface that allows the search for a specific prediction .
conversely , pongo - pred provides the user with a unique framework for membrane protein topology and signal peptide predictions .
another interesting feature of pongo - pred is the javascript - enabled portlet that in real time refreshes the queue status ( without reloading of the submission page and with very lightweight xml http requests ) . in particular , on the left - side of the page the user can realise whether her / his submission is a new call , whether it is running ( in this case the starting date and an absolute link is provided for bookmarking ) , or whether it has been processed .
the pongo homepage . on the left - side it is possible to follow the status of the different queries and the starting of a new action .
 the annotation efforts of the biosapiens european network of excellence have generated several distributed annotation systems ( das ) with the aim of integrating bioinformatics resources and annotating metazoan genomes ( ) . in this context 
 , the pongo das server ( ) provides the annotation on predictive basis for the all - alpha membrane proteins in the human genome , not only through das queries , but also directly using a simple web interface . in order to produce a more comprehensive analysis of the sequence at hand , 
 this annotation is carried out with four selected and high scoring predictors : tmhmm2.0 , memsat , prodiv and ensemble1.0 . 
 the stored and pre - computed predictions for the human proteins can be searched and displayed in a graphical view . 
 however the web service allows the prediction of the topology of any kind of putative membrane proteins , regardless of the organism and more importantly with the same sequence profile for a given sequence when required . here 
 we present a new web server that incorporates the state - of - the - art topology predictors in a single framework , so that putative users can interactively compare and evaluate four predictions simultaneously for a given sequence . 
 together with the predicted topology , the server also displays a signal peptide prediction determined with spep . 
 the pongo web server is available at . 
INTRODUCTION
MATERIALS AND METHODS
The predictors
Pre-computed transmembrane annotations for the human proteome
RESULTS
Figures and Tables