Epitopia Overview
 


Aim

Epitopia is a server for studying the immunogenic nature of a protein. Epitopia implements a machine learning scheme to rank individual amino acids in the protein, according to their potential of eliciting a humoral immune response.

Introduction

The interaction between an antibody and its antigen is at the heart of the humoral immune response. Specific regions of an antigen, termed epitopes, elicit a humoral immune respone, and are thus recognized by antibodies. Based on large datasets of antigen 3D structures and sequences, for which a validated epitope is known, several physico-chemical and structural-gemoetrical properties that significantly distinguish epitopes from the remaining antigen surface have been derived (Rubinstein et al., 2008). A machine learning scheme was then implemented in order to train a Naive Bayes classifier on these data for the purpose of detecting protein regions that manifest epitope-like characteristics.

Methodology

Epitopia may either be used to detect immunogenic regions in a given protein structure or in a given protein sequence. The input is analyzed with regards to its phyisco-chemical and structural-geometrical properties. Following that, the Naive Bayes classifier, which was trained on a dataset of epitope and non-epitope examples, computes for each property in each residue of the antigen its probability of being an epitope based on the the region it is embedded in. In other words, each epitope-sized region of the protein is given a score that reflects the joint probability of each one of its phyisco-chemical and structural-geometrical properties being an epitope based on validated epitope examples. The joint probability is expressed in sum of log of probabilities and is assigned to the amino-acid in the center of that epitope-sized region to enable inference of the immunogenic potential at the single amino-acid site resolution. Given an immunogenicity score of a residue, the probability that it was drawn from a population of epitope residues is thus computed as the fraction of validated epitope residues among all residues of the training data with an immunogenicity score in that range.

Inputs

Preprocess

After the the "Submit" button has been pressed a preprocess stage is performed in order to prepare the input and run several stand-alone executables that extract some of the physico-chemical and structural-geometrical properties required for the Epitopia prediction.

Outputs

For each Epitopia run a "job status page is created" and updated every 30 seconds. When the computation is complete links to the different results appear, and an email is sent to the user, if he entered an email address.

References

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