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Assume that i have a 3d-protein structure in a PDB file on a computer. Is there any bioinformatic method to predict if that protein is recognized by all known antibodies of human and cause an immune response or not? If the answer is yes, how accurate of these method?

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First of all: It is important to appreciate tha antibodies do not recognize antigens, they recognize epitopes borne on antigens.

There are several software (online) that allow to predict these epitopes with high accuracy. I´m going to describe some of them next:

Prediction of B-cell epitopes: You have to be in mind that B-cell epitopes can be continuous and conformationals.

Continuous epitopes can be predicted using Bcepred. This prediction methods is based on physico-chemical properties on a non-redundant dataset. The dataset consists of 1029 B-cell epitopes obtained from Bcipep database and equally number of non-epitopes obtained randomly from Swiss-Prot database. The prediction accuracy for models based of various properties varies from 52.92% and 57.53%. Also It achived highest accuracy of 58.70% at threshold 2.38, when combined four amino acid properties( hydrophilicity, flexibility, polarity and exposed surface).

Conformational epitopes can be predicted using DiscoTope 2.0. This method predicts discontinuous B cell epitopes from protein three dimensional structures. The method utilizes calculation of surface accessibility (estimated in terms of contact numbers) and a novel epitope propensity amino acid score. The final scores are calculated by combining the propensity scores of residues in spatial proximity and the contact numbers. Besides, Using the benchmark data set from the original DiscoTope paper, has been demonstrated that the updated method has a significantly increased predictive performance.

Important: Discotope allows to get a 3D structure of this epitopes.

I propose to use DiscoTope for prediction of discontinuous epitope residues for several reasons. First, I have shown on a data set of discontinuous epitopes that the average predictive performance of the DiscoTope is significantly higher than the Parker propensity scale and marginally higher than the surface localization score defined by the NACCESS RSA score. Second, several authors have shown that DiscoTope correctly predicts residues in epitopes that have been identified using different techniques such as phage-display, point mutation, and sequence analysis. Third, the DiscoTope prediction method is publicly available on www.cbs.dtu.dk/services/DiscoTope, and the output of the method is easily interpreted.

I would recommend you to check if this epitopes borne on the protein have possible allergenic sites that can contain experimentally proven IgE epitope. For this you can use Algpred

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    $\begingroup$ Very interesting answer, but please make it clear when you are directly quoting a source. $\endgroup$ – canadianer Jun 7 '17 at 6:23
  • $\begingroup$ Also note that, while you are (excellently) covering the 'how' of epitope prediction, this will not allow the OP to predict if the immune system can make antibodies against a certain protein or not (see my answer) $\endgroup$ – Nicolai Jun 7 '17 at 6:53
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The answer of polonio210 is an excellent summary of bioinformatic tools, that allow comparisons with databases of known antibodies - which is helpful to find a specific antibody that could bind your protein.

It's important to note, that this approach will not allow you, to discern whether or not the human immune system will be able to make an antibody against this protein. Based on the answers & comments to this question the number of possible antibodies a single human can make ranges from $10^{12}$ to $10^{16}$. These numbers are so high, that it's likely that not database will ever be able to cover all possible antibodies and allow you to predict if the immune system could make an antibody against a specific protein.

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