Epitopes: Difference between revisions

Line 17:

==Epitope Prediction Servers==

In the absence of the crystal structure of an antibody:antigen complex, a common way to identify the epitope recognized by a particular antibody is to display random peptides (for example, using [http://en.wikipedia.org/wiki/Phage_display phage display libraries]), and then to identify the sequences of the peptides with the highest affinity for the antibody. These sequences can then be used to predict where the epitope lies on the native protein, taking into account that the epitope on the native protein may be discontinuous. In this strategy, the 3D structure of the protein antigen must be known.

In the absence of the crystal structure of an antibody:antigen complex, a common way to identify the epitope recognized by a particular antibody is to display random peptides (for example, using [http://en.wikipedia.org/wiki/Phage_display phage display libraries]), and then to identify the sequences of the peptides with the highest affinity for the antibody. These sequences can then be used to predict where the epitope lies on the native protein, taking into account that the epitope on the native protein may be discontinuous. In order to use this strategy, the 3D structure of the protein antigen must be known.

===EpiSearch===

Line 23:

===Epitopia Server===

The [http://epitopia.tau.ac.il/ Epitopia Server] predicts immunogenic regions in general. It will accept either a protein sequence, or a 3D protein structure. It "implements a machine learning scheme to rank individual amino acids in the protein, according to their potential of eliciting a humoral immune response". (Thus, it does not require a list of peptides that bind to an antibody of interest.) When a 3D model is submitted, it can be visualized in Jmol colored by predicted immunogenicity. The methods are published<ref>PMID:18947876</ref>.

The [http://epitopia.tau.ac.il/ Epitopia Server] predicts immunogenic regions in general. It will accept either a protein sequence, or a 3D protein structure. It "implements a machine learning scheme to rank individual amino acids in the protein, according to their potential of eliciting a humoral immune response". (Thus, it does not require a list of peptides that bind to an antibody of interest.) When a 3D model is submitted, it can be visualized in [[FirstGlance in Jmol]] colored by predicted immunogenicity. The methods are published<ref>PMID:18947876</ref>.

===Pepitope Server===

The [http://pepitope.tau.ac.il/ Pepitope Server] predicts epitopes on the surface of a 3D protein antigen model, based on a list of peptides that bind to the antibody. The 3D model can be a [[PDB code]] or uploaded. Results can be visualized interactively in [[FirstGlance in Jmol]]. The algorithm is also available as a stand-alone program called ''PepSurf'' (with C++ source), licensed for non-commercial use by Tel-Aviv University. The methods are published<ref>PMID: 17977889</ref>.

==References and Notes==

Navigation menu

Page actions

Page actions

Personal tools

Navigation

Search

Tools

@@ Line 17: / Line 17: @@
 ==Epitope Prediction Servers==
-In the absence of the crystal structure of an antibody:antigen complex, a common way to identify the epitope recognized by a particular antibody is to display random peptides (for example, using [http://en.wikipedia.org/wiki/Phage_display phage display libraries]), and then to identify the sequences of the peptides with the highest affinity for the antibody. These sequences can then be used to predict where the epitope lies on the native protein, taking into account that the epitope on the native protein may be discontinuous. In this strategy, the 3D structure of the protein antigen must be known.
+In the absence of the crystal structure of an antibody:antigen complex, a common way to identify the epitope recognized by a particular antibody is to display random peptides (for example, using [http://en.wikipedia.org/wiki/Phage_display phage display libraries]), and then to identify the sequences of the peptides with the highest affinity for the antibody. These sequences can then be used to predict where the epitope lies on the native protein, taking into account that the epitope on the native protein may be discontinuous. In order to use this strategy, the 3D structure of the protein antigen must be known.
 ===EpiSearch===
@@ Line 23: / Line 23: @@
 ===Epitopia Server===
-The [http://epitopia.tau.ac.il/ Epitopia Server] predicts immunogenic regions in general. It will accept either a protein sequence, or a 3D protein structure. It "implements a machine learning scheme to rank individual amino acids in the protein, according to their potential of eliciting a humoral immune response". (Thus, it does not require a list of peptides that bind to an antibody of interest.) When a 3D model is submitted, it can be visualized in Jmol colored by predicted immunogenicity. The methods are published<ref>PMID:18947876</ref>.
+The [http://epitopia.tau.ac.il/ Epitopia Server] predicts immunogenic regions in general. It will accept either a protein sequence, or a 3D protein structure. It "implements a machine learning scheme to rank individual amino acids in the protein, according to their potential of eliciting a humoral immune response". (Thus, it does not require a list of peptides that bind to an antibody of interest.) When a 3D model is submitted, it can be visualized in [[FirstGlance in Jmol]] colored by predicted immunogenicity. The methods are published<ref>PMID:18947876</ref>.
+===Pepitope Server===
+The [http://pepitope.tau.ac.il/ Pepitope Server] predicts epitopes on the surface of a 3D protein antigen model, based on a list of peptides that bind to the antibody. The 3D model can be a [[PDB code]] or uploaded. Results can be visualized interactively in [[FirstGlance in Jmol]]. The algorithm is also available as a stand-alone program called ''PepSurf'' (with C++ source), licensed for non-commercial use by Tel-Aviv University. The methods are published<ref>PMID: 17977889</ref>.
 ==References and Notes==
 <references />