Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given Perturbations in Peptide Sequence, In Vivo Process, Experimental Techniques, and Source or Host Organisms
Perturbation methods add variation terms to a known experimental solution of one problem to approach a solution for a related problem without known exact solution. One problem of this type in immunology is the prediction of the possible action of epitope of one peptide after a perturbation or variation in the structure of a known peptide and/or other boundary conditions (host organism, biological process, and experimental assay). However, to the best of our knowledge, there are no reports of general-purpose perturbation models to solve this problem. In a recent work, we introduced a new quantitative structure-property relationship theory for the study of perturbations in complex biomolecular systems. In this work, we developed the first model able to classify more than 200,000 cases of perturbations with accuracy, sensitivity, and specificity >90% both in training and validation series. The perturbations include structural changes in >50000 peptides determined in experimental assays with boundary conditions involving >500 source organisms, >50 host organisms, >10 biological process, and >30 experimental techniques. The model may be useful for the prediction of new epitopes or the optimization of known peptides towards computational vaccine design. 1. Introduction National Institute of Allergy and Infectious Diseases (NIAID) supported the launch, in 2004, of the Immune Epitope Database (IEDB), http://www.iedb.org/ [1–4]. The IEDB system withdrew information from approximately 99% of all papers published to date that describe immune epitopes. In doing so, IEDB system analyses over 22 million PubMed abstracts and subsequently curated ≈13？K references, including ≈7？K manuscripts about infectious diseases, ≈1？K about allergy topics, ≈4？K about autoimmunity, and 1？K about transplant/alloantigen topics [5]. IEDB lists a huge amount of information about the molecular structure as well as the experimental conditions ( ) in which different th molecules were determined to be immune epitopes or not. This explosion of information makes necessary both query/display functions for retrieval of known data from IEDB as well predictive tools for new epitopes. Salimi et al. [5] reviewed advances in epitope analysis and predictive tools available in the IEDB. In fact, IEDB analysis resource (IEDB-AR: http://tools.iedb.org/) is a collection of tools for prediction of molecular targets of T- and B-cell immune responses (i.e., epitopes) [6, 7]. On the other hand, Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) techniques are useful tool to predict new drugs,
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