1. Enter the protein sequence for running prediction.The sequence can be loaded in the provided text box by cutting or pasting. The user can also upload the sequence file through browsing.Then select the format of sequence. The server accept both the formatted and unformatted sequence.The formatted sequence are accepted in almost every standard format like EMBL,PIR,etc.The server uses the ReadSeq program for reading the input sequences.
   Note: All the gaps, non standard characters except the standard protein sequence are ignored from the sequence. The sequence is accepted only in the single amino acid code.
   

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2. Single or multiple alleles can be selected from the list of 36 class I and 51 class II alleles. The user can also select all class I and Class II alleles through "Both" option in selection box.An example of selection of multiple alleles from both class I & II alleles is shown below.The user can select multiple MHC alleles by using the ALT or Meta Key.
   
   
   

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3. The server provides option of identification and predicting binder in antigen sequence. The user can perform prediction and identification separately (selecting either prediction or identification) or together by (selecting the "Both" option).
   Identification of experimental binders-
   

   The data of HLA binders for mapping was obtained from MHCBN version 3.1. The databases have more than 20,000 MHC binding peptides.

   
   Prediction of MHC binders
   

   The prediction of MHC binders is achieved by implementing the quantitative matrices.he matrices for class I alleles are obtained from Propred1 server. These matrices are originally derived from BIMAS server and literature. The matrices for class II alleles are obtained from ProPred1 servers. These matrices are originally derived from the virtual matrices of Sturniolo et al., 1999.

   
   
   

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   Parameters for Prediction

4. Threshold is an important parameter for prediction
During prediction, user can choose a cutoff threshold, which is defined as the 'percentage of best scoring natural peptides'. For example, a threshold of 1% would predict peptides in any given protein sequence that belongs to the 1% best scoring natural peptides. The threshold parameter allows user to modulate the stringency level in prediction results. A lower threshold (1%) corresponds to a high stringency prediction, i.e. to a lower rate of false positives and to a higher rate of false negatives. In contrast, a higher threshold value (low stringency) corresponds to a higher rate of false positives and a lower rate of false negatives. Authors suggest that for first round of screening, threshold values higher than 3% are not desirable, since the rate of false positives can increase the size of the predicted repertoire to an amount unacceptable for later experimental testing.The value of threshold can vary from 1% to 10%.
All peptide achieving score more then cutoff score at selected threshold will be assigned binders.
DisplayThe server displays the results in two formats i) HTML Mapping ii)Tabular. These reults display formats will be described in result display section of help.
Users can limit the number of top peptides to be displayed in tabular display.


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Data for Identification of Experimentally proven Binders

Prediction of HLA Binders:

All the quantitative matrices for the prediction are obtained from ProPred and ProPred1 servers developed by our group. The HLA class I matrices were orginally derived from BIMAS and literatute. The Matrices for HLA class II were orginally obtained from the work of Sturniolo et al., 1999.These matrices are either multiplication or addition type. The listing of allele and their type is provided below.

*Where allele mean the name of HLA allele, type specify the type of matrices used in prediction,"++" denotes addition matrices and "××" denotes the multiplication matrices.

Prediction