t - cells of the human immune system recognize antigens as short peptide fragments ( t - cell epitopes ) derived from the degradation of proteins . major histocompatibility complex ( mhc ) proteins play a vital role in the initiation and regulation of immune responses ( 14 ) . their primary function is to bind and subsequently present antigenic peptides on the cell surface for recognition by t - cells of the immune system . the recognition of t - cell epitopes is critical for the immune response to infectious , autoimmune , allergic and neoplastic disease . t - cell epitopes are important for the development of peptide - based vaccines ( 5 ) . there is a great diversity of human leukocyte antigens ( hlas ; human mhc ) genes with some 2000 known variants characterized to date ( 6 ) . hla proteins share 3d structure with main differences observed in residues that form the peptide - binding groove . hla proteins that have small differences in their peptide - binding grooves and share similar peptide - binding specificities are grouped into hla supertypes ( 7,8 ) . promiscuous peptides  those that bind more than one hla variant  are prime targets for vaccine and immunotherapy development because they are relevant to higher proportions of the human population . because of the large number of hla proteins , experimental approaches for identifying t - cell epitopes ( from overlapping peptides that span the length of protein antigens ) are time - consuming and costly , and thus not applicable for large - scale screening . computer modeling methods can help to simulate the biological process of antigen presentation , minimize the number of experiments required , enable a systematic scanning for candidate mhc - binding peptides and thus speed up vaccine development ( 9 ) . multipred is a web - based system for the prediction of peptides that bind multiple hla alleles . current implementation can predict peptides that bind hla proteins belonging to supertypes a2 and a3 ( hla class i ) as well as dr ( hla class ii ) and in future will be extended to other supertypes . the predictive engines implemented in multipred are hidden markov models ( hmms ) and artificial neural networks ( anns ) . a novel data representation method enables multipred to predict peptides that bind to multiple hla alleles belonging to one hla supertype by a single prediction model per supertype . the predominant length of peptides that bind hla - a2 and - a3 ( class i ) proteins is nine amino acids ( 10 ) . hla - dr ( class ii ) proteins bind longer peptides through the core binding region , which is nine amino acids long ( 11 ) . the training data comprise 3050 9mer peptide sequences ( 664 binders and 2386 non - binders ) related to 15 variants of the hla - a2 supertype , 2216 9mer peptide sequences ( 680 binders and 1536 non - binders ) related to eight variants of the hla - a3 supertype and 2396 9mer peptides ( 448 binders and 1948 non - binders ) related to six hla - dr variants . these data are mainly from three sources , the mhcpep database ( 12 ) , published articles and a set of hla non - binding peptides ( v. brusic , unpublished data ) . for both training and prediction the data representation includes both the peptide and its binding environment ( hla contact residues ) . this virtual peptide representation comprises both peptide residues and the environment for each residue of the 9mer peptides ( 13,14 ) . to simplify the data representation and eliminate redundant information , for each hla supertype , we considered only those contact residues that vary across various hla variants and discarded the residues , which are conserved . in multipred , a three - layer backpropagation network with sigmoid activation functions was built for hla - a2 and -a3 supertype and a four - layer backpropagation network with a hyperbolic tangent sigmoid activation function between the two hidden layers and a sigmoid activation function between the second hidden layer and the output for hla - dr supertype . various techniques , including optimization of ann architecture and balancing datasets , were explored to improve the prediction accuracy of the ann models ( 14 ) . multipred also has a first - order hmm as an alternative prediction engine ( 13 ) . the user can select either the ann or the hmm model for prediction  both methods have been optimized and show similar performance . the aroc is > 0.8 in all cases , indicating good prediction capability [ see ( 13,14 ) for details on hla - a2 models , ( 15 ) for hla -a3 models , and v. brusic , a. sette , g. l. zhang , k. n. srinivasan , j. t. august and v. brusic , manuscript in preparation for hla - dr models . in addition to individual 9mer predictions , multipred also predicts immunological hotspots ( regions of high concentration of 9mer promiscuous binders ) . we have developed two scoring schemes to identify immunological hotspots within antigens for hla classes i and ii supertype . the scheme for hla class i supertype is based on high - scoring individual 9mers within a window of 30 amino acids ( 15 ) and the scheme for hla class ii supertype is based on average scores of individual 9mers within a window of 15 amino acids . the selection of window lengths was based on a trial - and - error process . window lengths of 15 , 20 , 25 and 30 , were explored and the results were compared with the representative experimental results . the window length 30 was found to suit class i predictions and window length 15 to class ii predictions . the lengths outside these ranges are considered too short or too long as targets for experimental validation . the prediction performance of multipred for hla - a2 and -a3 hotspots was validated using experimental results from a systematic study of human papillomavirus type 16 e6 ( p03126 ) and e7 ( p03129 ) proteins ( 16 ) . the prediction performance of multipred for hla - dr hotspots was validated using experimental results from systematic binding studies of overlapping peptides from myelin oligodendrocyte glycoprotein ( mog ) ( caa88109 ) , bee venom protein ( 1poc ) and hepatitis c virus 1b protein ( aab00216 ) . the web interface of multipred uses a set of graphical user interface forms with a combination of perl , cgi and c background programs . the functions provided by multipred include ( i ) running predictions , ( ii ) model building , ( iii ) prediction accuracy evaluation and ( iv ) identifying consensus predictions among up to three sets of predictions on the same input protein sequence . run prediction. the required input is the selection of supertype and prediction method ( pre - defined ann or hmm ) . alternatively , users can select a pre - defined model ( built by model building function ) . by selecting the submit button users get to a sequence input page where the required input is a protein sequence and its name . the length of the input sequence must be between 9 and 2000 amino acids . if the input sequence contains symbols other than amino acids ( space and carriage returns are allowed ) or if the sequence is outside the length limits , an error message will be displayed . protein sequence , which means the input sequence is treated as one single protein sequence and carriage returns are ignored . if users changed the sequence type to  a list of peptide sequences , then sequences divided by carriage returns are treated as separate peptides . the detailed description on processing steps involved when the input sequence is a protein sequence or a list of peptides are available at   and , respectively . the 9mer binding scores range from 1 to 9 ( figure 1a ) , with scores 49 referring to predicted binders ( 8 or 9 referring to high , 6 or 7 to moderate , and 4 or 5 to low confidence of peptide binding ) . multipred saves the prediction result and the users may note down the i d number of the saved jobs for the comparison of prediction results generated by different prediction models ( figure 1a ) . two scoring schemes to identify immunological hotspots within antigens were developed for hla classes i and ii supertype . the scheme for hla class i supertypes is based on high - scoring individual 9mers within a window of 30 amino acids ( 15 ) . in the result table ( figure 1a ) , sum is the sum total of the individual binding scores of a peptide to the mhc proteins , score 1 is the top 1  sum in a 30mer window ( a 30mer window comprises 22 consecutive 9mer peptides ) . score 2 is the average of the top 2  sum in a 30mer window . score 5 are the average of the top 3 , 4 and 5  sum , respectively , in a 30mer window . to show the user a clear view of the binding capacity of an input protein , scores 15 of all 30mer peptides of the input protein can be displayed as graphs , in which x - axis represents the starting position of a 30mer window and the y - axis represents score 1 ( 2/3/4/5 ) of the 30mer window . for example , in figure 1b , which is the graph of score 4 of the protein e6 , the first three 30mer windows ( starting at positions 1 , 2 or 3 ) are 36.82 and the next two windows ( starting at positions 4 or 5 ) have scores 39.50 . the following 13 30mer windows ( starting at positions 619 ) have scores > 42 , the recommended threshold for score 4 for hla - a2 ann models ( figure 1a ) , indicating a predicted hotspot , which corresponds to an experimentally determined hla - a2 hotspot in e6 protein ( 16 ) . to locate the individual 9mers with top binding scores in each 30mer window , the  align function can be used . figure 1c shows an example of the alignment view of the top four 9mers in each 30mer window . the default values on the web page are the recommended thresholds for score 4 . in a hla - dr prediction result table , average was calculated as the average of the  sum within a 15mer window ( seven consecutive 9mers make a 15mer window ) . when users select the input sequence as  a list of peptide sequences , the input sequences separated by carriage returns or line breaks are treated as different peptides . all overlapping 9mers in each peptide are submitted for prediction . in the result tables , predicted binding scores are represented by the highest individual binding score of each input peptide . the predicted binding scores of individual 9mers in each peptide in the list are data not shown ( figure 2a ) . to display the input peptides in the order of their binding scores , the user can use the function  sort the result. in the result page ( figure 2b ) , the input peptides are listed in descending order of their binding scores . to display the predicted 9mer binders from each input peptide , the user can use the function alignment view. in the result page ( figure 2c ) , the 9mers with binding scores 4 are aligned with the input peptides . the predicted 9mer binders are displayed with the names of the hla alleles , which produced binding scores above the selected threshold . if the user has 9mer peptides with known binding affinities to proteins belonging to hla - a2 , -a3 or -dr supertypes and wants to build his own prediction models , the user can use the  model build function in multipred . the users have the option to use their data only , or combine their data with the existing multipred data and build the model on the server . currently , users can expect to train an hmm model within 1 min while training of ann models may take up to 50 min ( depending on the size of the training dataset)there are actually four anns trained in the background . the ann models trained by the same dataset are usually slightly different because the initial weights of networks are assigned randomly ( 14 ) . to make the trained models more stable , the training is repeated four times , and four sets of weights are trained  the predictions are the averages of these four predictions . when the model building request is submitted , an intermediate page ( figure 3 ) will be displayed providing the result url can be bookmarked for later model retrieval . if the user has 9mer peptides with known binding affinities and would like to evaluate the prediction accuracy of a model with these peptides , the user can use the  accuracy evaluation function of multipred . the system predicts the binding affinities of the input 9mers and calculates aroc of the predictions . for each supertype the comparison of predictions helps identify the most promising peptides picked up as predicted binders by multiple models . the user needs to input the individual result ids ( up to three ) to the system . the result ids must be predictions of the same protein and to the same hla supertype , if the result ids belong to predictions on different proteins , an error message will be displayed . the user can select the analysis of top 5 or 10% of the predicted binders . in the output table , top 5 or 10% predictions are displayed in the descending order of their binding scores . several web - based systems have been developed and widely used for the prediction of mhc binders , such as syfpeithi ( 17 ) , bimas ( 18 ) , smm ( 19 ) , mhcpred ( 20 ) , rankpep ( 21 ) , tepitope ( 22 ) , netmhc ( 23 ) and svmhc ( 24 ) . although multipred is similar to them in its overall goal of predicting mhc - binding peptides , there are significant differences in both functionality and methodology . bimas , mhcpred , rankpep and tepitope use quantitative matrices , and smm is based on an improved matrix - based algorithm called stabilized matrix method . each of these methods uses one prediction model per mhc proteins , making them difficult to maintain and assess accuracy . tepitope allows prediction of peptides to many different class ii proteins ( using multiple prediction models ) , but it is not available through the web . since hla proteins are highly polymorphic , promiscuous peptides that bind more than one hla protein are prime targets for vaccine and immunotherapy development because they are relevant to higher proportions of the human population . t - cell epitope hotspots are highly promising regions as targets of t - cell immune responses , which are of interest for experimental validation as potential vaccine targets . in addition , multipred provides several functions which are not available in other prediction systems , such as model building by user function , accuracy evaluation function and consensus prediction function . the pathway from epitopes to vaccine development is lengthy and cost - intensive , involving exhaustive experiments . the main utility of multipred is in the selection of key antigenic regions to minimize the number of experiments required for mapping of promiscuous t - cell epitopes and t - cell epitope hotspots . an example of the output pages of multipred when the input is a single protein sequence . the input protein sequence is a human papillomavirus type 16 e6 , the prediction method used is ann and the hla supertype of interest is hla - a2 . ( a ) the main result page . the input sequence is truncated into overlapping 9mers for the prediction of binding scores to multiple hla - a2 variants , 0201 , 0202 , 0203 , 0204 , 0205 , 0206 , 0207 and 0209 . an example of the output pages of multipred when input is a list of peptides . the input protein peptides are from hepatitis c virus , the prediction method used is ann and the hla supertype of interest is hla - a3 . ( as can be seen here , the input sequence is truncated into overlapping 9mers for the prediction of binding scores to multiple hla - a3 variants , 0301 , 0302 , 1101 , 1102 , 3101 , 3301 and 6801 . when the model building request is submitted , an intermediate page will be displayed providing the result url that can be bookmarked for later model retrieval .
multipred is a web - based computational system for the prediction of peptide binding to multiple molecules ( proteins ) belonging to human leukocyte antigens ( hla ) class i a2 , a3 and class ii dr supertypes . 
 it uses hidden markov models and artificial neural network methods as predictive engines . 
 a novel data representation method enables multipred to predict peptides that promiscuously bind multiple hla alleles within one hla supertype . 
 extensive testing was performed for validation of the prediction models . 
 testing results show that multipred is both sensitive and specific and it has good predictive ability ( area under the receiver operating characteristic curve aroc > 0.80 ) . 
 multipred can be used for the mapping of promiscuous t - cell epitopes as well as the regions of high concentration of these targets  termed t - cell epitope hotspots . 
 multipred is available at .