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 Tools    >> Predicting Antigenic Peptides


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Antibodies (Abs) finds multiple applications in a variety of areas including biotechnology, medicine and diagnosis, and indeed they are one of the most powerful tools for life science research.
Abs directed against protein antigens can recognize either linear or native three-dimensional (3D) epitopes. The obtention of Abs that recognize 3D epitopes require the use of whole native protein as immunogens. Unfortunately, this not always a choice due to various technical reasons: for example the native protein is just not available, or the protein is toxic. In such cases, immunization with peptides is the alternative. Of course, Abs generated in this manner will recognize linear epitopes, and they might or might not recognize the source native protein, but yet they will be useful for standard laboratory applications such as western blots. The question of what are the best peptides to use as immunogens is reviewed below.
Directions for the prediction of Antigenic peptides
Despite there is not infallible method to predict antigenic peptides, there are several rules that can be followed to determine what are the peptide fragments from a protein that are likely to be antigenic. These rules are also dictated to increase the odds of an Ab recognizing the native protein.
  • 1. Antigenic peptides should be located in solvent accessible regions and contain both hydrophobic and hydrophilic residues.
    • For proteins of known 3D structure solvent accessibility can be determined using a variety of programs such as DSSP, NACESS or WHATIF, among others. A web server to calculate solvent accessibility using Whatif is also available following this link
    • If the 3D structure is not known, use any of the following web servers to predict accessibilities: PHD, JPRED, PredAcc (c), ACCpro
  • 2. Preferably select peptides lying in long loops connecting Secondary Structure (SS) motifs, avoiding peptides located in helical regions. This will increase the odds that the Ab recognizes the native protein.
    • For protein with known 3D coordinates, SS can be obtained from the sequence link of the relevant entry at the Brookhaven data bank. The PDBsum server also offer SS analysis of pdb records.
    • When no structure is available secondary structure predictions can be obtained from any of the following servers: PHD, JPRED, PSI-PRED, NNSP, etc
  • 3. When possible, choose peptides that are in the N- and C-terminal region of the protein. Because the N- and C- terminal regions of proteins are usually solvent accessible and unstructured, Abs against those regions are also likely to recognize the native protein.
  • 4. For cell surface glycoproteins eliminate from initial peptides those containing consesus sites for N-glycosylation.
Software for the the detection of antigenic peptides
Several methods based on various physio-chemical properties of experimental determined epitopes (flexibility, hydrophibility, accessibility) have published for the prediction of antigenic determinants, of which The antigenic Index and Preditop are good examples.

Perhaps the simplest method for the prediction of antigenic determinants is that of Kolaskar and Tongaonkar (1990) , which is based on the occurrence of amino acid residues in experimentally determined epitopes. Prediction of antigen determinants using the method of Kolaskar and Tongaonkar method is available from our antigenic site.

The prediction algorithm work as follows:
  • 1. Calculate the average propensity for each overlapping 7-mer and assign the result to the central residue (i+3) of the 7-mer.
  • 2. Calculate the average for the whole protein.
  • 3. (a) If the average for the whole protein is above 1.0 then all residues having above 1.0 are potentially antigenic.
  • 3. (b) If the average for the whole protein is below 1.0 then all residues having above the average for the whole protein are potentially antigenic.
  • 4. Find 8-mers where all residues are selected by step 3 above (6-mers in the original paper)
The Kolaskar and Tongaonkar method is also available from the GCG package, and it runs using the command egcg.
For comments and suggestions about this site please contact Pedro Reche
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