In this work, we developed a general perturbation theory and machine learning method for data mining of proteomes to discover new B-cell epitopes useful for vaccine design. The method predicts the epitope activity ε(c) of one query peptide (q-peptide) under a set of experimental query conditions (c). The method uses as input the sequence of the q-peptide. The method also uses as input information about the sequence and epitope activity ε(c) of a peptide of reference (r-peptide) assayed under similar experimental conditions (c). The model proposed here is able to classify 1 048 190 pairs of query and reference peptide sequences from the proteome of many organisms reported on IEDB database. These pairs have variations (perturbations) under sequence or assay conditions. The model has accuracy, sensitivity, and specificity between 71 and 80% for training and external validation series. The retrieved information contains structural changes in 83 683 peptides sequences (Seq) determined in experimental assays with boundary conditions involving 1448 epitope organisms (Org), 323 host organisms (Host), 15 types of in vivo process (Proc), 28 experimental techniques (Tech), and 505 adjuvant additives (Adj). Afterward, we reported the experimental sampling, isolation, and sequencing of 15 complete sequences of Bm86 gene from state of Colima, Mexico. Last, we used the model to predict the epitope immunogenic scores under different experimental conditions for the 26 112 peptides obtained from these sequences. The model may become a useful tool for epitope selection toward vaccine design. The theoretical-experimental results on Bm86 protein may help the future design of a new vaccine based on this protein.

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