General information:-
For a peptide to be a T-cell epitope, it is necessary that it must be a MHC binder. In th epast much wor has been done in developing methods for prediction of MHC binders and non-binders. But along with this information, one is always anxious to find out the binding affinity of these peptides with the MHC molecules. So keeping this demand of the immunologists in mind we got inspired to develop a SVM based method to predict the binding affinity (natural logarithm of the binding affinities) of the peptides.
This method uses either the binary pattern and or the Physico-chemical properties of the amino acids for making the predictions.
Stepwise help:-
Name of protein: This is an optional field.The name of the protein may have letters and numbers with the "-" or "_". All other characters are non-permissible. The field is assigned a default name "Protein".
The sequence name is just used only for your information. It may be a problem with ? , for example or an empty space within the name of the sequence, which is not allowed for reasons of security.
Protein sequence:- This server allows the submission of the sequence in any of the standard formats. The user can paste plain sequence in the provided inbox. The server also has the
facility for uploading the local sequence files. Amino acid sequences must be entered in the one-letter code. All the non standard characters will be ignored from the sequence.
Sequence format: The server can accept both the formatted or unformatted raw antigenic sequences. The server uses ReadSeq routine to parse the input. The user should choose whether
the sequence uploaded or pasted is plain or formatted before running prediction. The result of the prediction will be wrong if the format chosen is wrong.
Prediction Approach:-
Binary pattern of amino acids:- A SVM was developed on the basis of binary code of of amino acids of the protein. The SVM was provided with a 20 dimensional vector. The binary patternof each amino acid is a unique code of compsed of 0s & 1s only. The overall accuracy of composition based method for prediction of affinity of HLA-A2 binders was evaluated to be.The performance of the method is evaluated using leave-one-out validation.
Physico-chemical properties:- A SVM was based on the basis of average physico-chemical properties of peptide sequence. the properties of peptide was determined by considering 36 physico-chemical properties.
The overall accuracy of the prediction method was found to be.
Prediction result:- After the analysis the result having sequence of the protein, and its predicted affinity can be illustrated both in graphical or in tabular form. A sample of these is shown below.
1. Display in graphical form
2. display in tabular form
