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

Abstract

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.

QSPR-perturbation models for the prediction of B-epitopes from immune epitope database: an interesting route for predicting<em> &ldquo;in silico</em>&rdquo; new optimal peptide sequences and/or boundary conditions for vaccine development.

“…On the other side, the first input term λ The models obtained here are very stable and robust, yielding values of accuracy, sensitivity and specificity > 90% for both training and cross-validation series. These models are not able to improve the model developed by Gonzá lez-Dí az et al (2). However, the results obtained are very similar and the values of different statistical parameters demonstrate the high significance of the models, validating the consistency of the method.…”

“…In these equations, N is the number of cases used to train the models, RC is the canonical correlation coefficient, and U is the Wilk's lambda or Ustatistic. In line with Gonzá lez-Dí az et al (2), the output of the models λ(εij)new is a real value function that scores the propensity with which a new peptide obtained after perturbation of the initial conditions acts as B-epitope. On the other side, the first input term λ The models obtained here are very stable and robust, yielding values of accuracy, sensitivity and specificity > 90% for both training and cross-validation series.…”

“…The same database recently utilized by Gonzá lez-Dí az et al (2) was used in the present study. The calculation of the molecular descriptors was implemented in the in-house program MARCH-INSIDE (4), which makes use of a Markov Chain method to calculate the k-th mean values of different physicochemical molecular properties k λ(mi) for i-th molecules (mi) (5).…”

“…In a recent work, Gonzá lez-Dí az et al (2) have developed an electronegativity QSPRperturbation model for B-epitopes reported in IEBD able to predict the probability of occurrence of an epitope after a perturbation in the peptide sequence (mi), source organism (so), host organism (ho), immunological process (ip), and experimental technique (tq) used. In principle, there are more than 1,600 different molecular descriptors (λi) that may be generalized and used to solve QSPR problems in chemical structures (3).…”

Experimental and computational studies of fatty acid distribution networks

“…In another recent work, the method for the prediction of peptide epitopes was adapted using the perturbation theory 40 In another recent work, the method for the prediction of peptide epitopes was adapted using the perturbation theory 40 …”