# 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

Authors: Humberto González-Díaz; Lázaro G. Pérez-Montoto; Florencio M. Ubeira
Journal: Journal of Immunology Research
 (2014)

---

## 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.


---

## Body

## 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 (c
i
j) in which different ith 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, RNA, drug-protein complexes, and protein-protein complexes. In general, QSAR/QSPR-like methods transform molecular structures into numeric molecular descriptors (λ
i) in a first stage and later fit a model to predict the biological process. For example, DRAGON [8–10], CODESSA [11, 12], MOE [13], TOPS-MODE [14–17], TOMOCOMD [18, 19], and MARCH-INSIDE [20] are among the most used softwares to calculate molecular descriptors based on quantum mechanics (QM) and/or graph theory [21–27]. The software STATISTICA [28] and WEKA [29] are often used to perform multivariate statistics and/or machine learning (ML) analysis in order to preprocess data and later fit the final QSAR/QSPR model using techniques like principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM), or artificial neural networks (ANN) [28].QSAR/QSPR models are also important in immunoinformatics to predict the propensity of different molecular structures to play different roles in immunological processes. They include skin vaccine adjuvants and sensitizers [30–38], drugs and their activity/toxicity protein targets in the immune system [39], and epitopes [40–49]. Moreover, Reche and Reinherz [50] implemented PEPVAC (promiscuous epitope-based vaccine), a web server for the formulation of multiepitope vaccines that predict peptides binding to five distinct HLA class I supertypes (A2, A3, B7, A24, and B15). PEPVAC can also identify conserved MHC ligands, as well as those with a C-terminus resulting from proteasomal cleavage. The Dana-Farber Cancer Institute hosted the PEPVAC server at the site http://immunax.dfci.harvard.edu/PEPVAC/. To close with a last example, Lafuente and Reche [51] reviewed the available methods for predicting MHC-peptide binding and discussed their most relevant advantages and drawbacks.In many complex QSPR-like problems in immunoinformatics, like in other areas, we know the exact experimental result (known solution) of the problem, but we are interested in the possible result obtained after a change (perturbation) on one or multiple values of the initial conditions of the experiment (new solution). For instance, we often know, for large collections ofith molecules (
m
i
), organic compounds, drugs, xenobiotics, and/or peptide sequences, the efficiency of the compound ε
(
c
i
j
) as adjuvant, action as epitope, immunotoxicity, and/or the interaction (affinity, inhibition, etc.) with immunological targets. In addition, we often known for each molecule the exact conditions (c
i
j) of assay for the initial experiment including structure of the molecule m
i (drug, adjuvant, and sequence of the peptide), source organism (so), host organism (ho), immunological process (ip), experimental technique (tq), concentration, temperature, time, solvents, and coadjuvants. This is the case of big data retrieved from very large databases like IEDB [1–4] and CHEMBL [52]. However, we do not know the possible result of the experiment if we change at least one of these conditions (perturbation). We refer to small changes or perturbations in both structure and condition for input or output variables. It means that we include changes in ho, so, ip, and tq, changes of the compound by one analogue compound with similar structure, changes in the sequence of the epitope (artificial by organic synthesis or natural mutations), and polarity of the solvent or coadjuvants. In these cases, we could use a perturbation theory model to solve the QSAR/QSPR problem. Perturbation theory includes methods that add “small” terms to a known solution of a problem in order to approach a solution to a related problem without known solution. Perturbation models have been widely used in all branches of science from QM to astronomy and life sciences including chaos or “butterfly effect,” Bohr’s atomic theory, Heisenberg’s mechanics, Zeeman’s and Stark’s effects, and other models with applications in like protein spectroscopy and others [53–57]. In a very recent work Gonzalez-Diaz et al. [58] formulated a general-purpose perturbation theory or model for multiple-boundary QSPR/QSAR problems. However, there is not report in the immunoinformatics literature of a general QSPR perturbation model for IEDB B-epitopes. Here we report the first example of QSPR-perturbation model for B-epitopes reported in IEDB able to predict the probability of occurrence of an epitope after a perturbation in the sequence, the experimental technique, the exposition process, and/or the source or host organisms.


## 2. Materials and Methods

### 2.1. Molecular Descriptors for Peptides

We calculated the molecular descriptors of the structure of peptides using the software MARCH-INSIDE (MI) based on the algorithm with the same name [59]. The MI approach uses a Markov Chain method to calculate the kth mean values of different physicochemical molecular properties λ
(
m
i
) for ith molecules (
m
). These λ
(
m
i
) values are calculated as an average of λ
k
(
m
i
) values for all atoms placed at topological distance d
≤
k; which are in turn the means of atomic properties (
λ
j
) for all atoms in the molecule and its neighbors placed at d
=
k. For instance, it is possible to derive average estimations of molecular refractivities k
MR
(
m
i
), partition coefficients P
k
(
m
i
), and hardness k
η
(
m
i
) for atoms placed at different topological distances d
≤
k. In this first work, we calculated only one type of λ
(
m
i
) values. We calculated for all peptides the average value χ
(
m
i
) of all the atomic electronegativities χ
i for all δ
i atoms connected to the ith atom (
i
→
j
) and their neighbors placed at a distance d
≤
5 [59]:
(1)
χ
(
m
i
)
=
1
6
∑
k
=
0
5
χ
j
k
=
1
6
∑
k
=
0
5
∑
i
→
j
δ
i
p
k
(
χ
j
)
·
χ
j
.We calculate the probabilitiesp
k
(
λ
j
) for any atomic property including p
k
(
χ
j
) using a Markov Chain model for the gradual effects of the neighboring atoms at different distances in the molecular backbone. This method has been explained in detail in many previous works so we omit the details here [59].


### 2.2. Electronegativity Perturbation Model for Prediction of B-Epitopes

Very recently Gonzalez-Diaz et al. [58] formulated a general-purpose perturbation theory or model for multiple-boundary QSPR/QSAR problems. We adapted here this new theory or modeling method to approach to the peptide prediction problem from the point of view of perturbation theory. Let be a set of ith peptide molecules denoted as m
i with a value of efficiency ε
i
j as epitopes experimentally determined under a set of boundary conditions c
j
≡
(
c
0
,
c
1
,
c
2
,
c
3
,
…
,
c
n
). We put the main emphasis here on peptides reported in the database IEDB. In this sense, the boundary conditions c
j used here are the same reported in this database, c
0
=
is the specific peptide, c
1
=
s
o
j, c
2
=
h
o
j, c
3
=
i
p
j, and c
4
=
t
q
j. In general, so is the organism that expresses the peptide (but it can include also artificial peptides, cellular lines, etc.), ho is the host organism exposed to the peptide by means of the bp detected with tq. As our analysis, based on the data reported by IEDB we are unable to work with continuous values of epitope activity ε
i
j. Consequently, we have to predict the discrete function of B-epitope efficiency λ
(
ε
i
j
)
=
1 for epitopes reported in the conditions c
j and λ
(
ε
i
j
)
=
0, otherwise. Our main aim is to predict the shift or change in a function of the output efficiency Δ
λ
(
ε
i
j
)
=
λ
(
ε
i
j
)
ref
-
λ
(
ε
i
j
)
new that takes place after a change, variation, or perturbation (
Δ
V
) in the structure and/or boundary conditions of a peptide of reference. But we know the efficiency of the process of reference λ
(
ε
i
j
)
ref in addition to the molecular structure and the set of conditions c
j for initial (reference) and final processes (new). Consequently, to predict Δ
λ
(
ε
i
j
) we have to predict only λ
(
ε
i
j
)
new the efficiency function of the new state obtained by a change in the structure of the peptide and/or the boundary conditions. Let Δ
V be a perturbation in a function λ; we can define V
i
j as the state information function for the reference and new states. According to our recent model [58], we can write V
i
j as a function of the conditions and structure of the peptide m
i as follows. In fact, the variational state functions V
i
j have to be written in pairs in order to describe the initial (reference) and final (new) states of a perturbation, as follow:
(2)
V
i
j
=
λ
(
ε
i
j
)
new
-
∑
j
=
1
4
(
λ
(
m
i
)
-
λ
(
c
i
j
)
avg
)
,
V
q
r
=
λ
(
ε
q
r
)
ref
-
∑
r
=
1
4
(
λ
(
m
q
)
-
λ
(
c
q
r
)
avg
)
.The state functionV
n
i
j is for the ith peptide measured under a set of c
i
j boundary conditions in output, final, or new state. The conjugated state function V
r
q
r is for the qth peptide measured under a set of c
q
r boundary conditions for the input, initial, or reference state. The difference Δ
V between the new (output) state and the reference (input) state is the additive perturbation [58]. Consider
(3)
Δ
V
=
V
i
j
-
V
q
r
=
[
λ
(
ε
i
j
)
new
-
∑
j
=
1
4
(
λ
(
m
i
)
-
λ
(
c
i
j
)
avg
)
]
-
[
λ
(
ε
q
r
)
ref
-
∑
r
=
1
4
(
λ
(
m
q
)
-
λ
(
c
q
r
)
avg
)
]
.Equation (3) described before opens the door to test different hypothesis. A simple hypotheses is H0: existence of one small and constant value of the perturbation function Δ
V
=
e
0 for all the pairs of peptides and a linear relationship between perturbations of input/output boundary conditions with coefficients a
i
j, b
i
j, c
q
r, and d
i
j. Consider
(4)
e
0
=
Δ
V
=
[
a
i
j
·
λ
(
ε
i
j
)
new
-
∑
j
=
1
4
b
i
j
·
(
λ
(
m
i
)
-
λ
(
c
j
)
avg
)
]
-
[
c
q
r
·
λ
(
ε
q
r
)
ref
-
∑
r
=
1
4
d
q
r
·
(
λ
(
m
q
)
-
λ
(
c
r
)
avg
)
]
.We can use elemental algebraic operations to obtain from these equations an expression for efficiency as epitope of the peptideλ
(
ε
i
j
)
new. In this case, considering b
i
j
≈
d
q
r, we can obtain the different expressions; the last may be very useful to solve the QSRR problem for the large datasets formed by IEDB B-epitopes. Consider
(5)
λ
(
ε
i
j
)
new
=
(
c
q
r
a
i
j
)
·
λ
(
ε
q
r
)
ref
+
[
∑
j
=
1
4
(
b
q
r
a
i
j
)
·
(
λ
(
m
i
)
-
λ
(
c
j
)
avg
)
new
]
-
[
∑
r
=
1
4
(
d
q
r
a
i
j
)
·
(
λ
(
m
q
)
-
λ
(
c
r
)
avg
)
ref
]
+
(
e
0
a
i
j
)
,
λ
(
ε
i
j
)
new
=
c
0
′
·
λ
(
ε
q
r
)
ref
+
∑
j
=
1
4
d
i
j
′
·
Δ
(
λ
(
m
i
)
-
λ
(
c
j
)
avg
)
+
e
0
′
,
λ
(
ε
i
j
)
new
=
c
0
′
·
λ
(
ε
q
r
)
ref
+
∑
j
=
1
4
d
i
j
′
·
Δ
Δ
λ
i
j
q
r
+
e
0
′
,
λ
(
ε
i
j
)
new
=
c
0
′
·
λ
(
ε
q
r
)
ref
+
∑
j
=
1
4
d
i
j
′
·
Δ
Δ
χ
i
j
q
r
+
e
0
′
.


## 2.1. Molecular Descriptors for Peptides

We calculated the molecular descriptors of the structure of peptides using the software MARCH-INSIDE (MI) based on the algorithm with the same name [59]. The MI approach uses a Markov Chain method to calculate the kth mean values of different physicochemical molecular properties λ
(
m
i
) for ith molecules (
m
). These λ
(
m
i
) values are calculated as an average of λ
k
(
m
i
) values for all atoms placed at topological distance d
≤
k; which are in turn the means of atomic properties (
λ
j
) for all atoms in the molecule and its neighbors placed at d
=
k. For instance, it is possible to derive average estimations of molecular refractivities k
MR
(
m
i
), partition coefficients P
k
(
m
i
), and hardness k
η
(
m
i
) for atoms placed at different topological distances d
≤
k. In this first work, we calculated only one type of λ
(
m
i
) values. We calculated for all peptides the average value χ
(
m
i
) of all the atomic electronegativities χ
i for all δ
i atoms connected to the ith atom (
i
→
j
) and their neighbors placed at a distance d
≤
5 [59]:
(1)
χ
(
m
i
)
=
1
6
∑
k
=
0
5
χ
j
k
=
1
6
∑
k
=
0
5
∑
i
→
j
δ
i
p
k
(
χ
j
)
·
χ
j
.We calculate the probabilitiesp
k
(
λ
j
) for any atomic property including p
k
(
χ
j
) using a Markov Chain model for the gradual effects of the neighboring atoms at different distances in the molecular backbone. This method has been explained in detail in many previous works so we omit the details here [59].


## 2.2. Electronegativity Perturbation Model for Prediction of B-Epitopes

Very recently Gonzalez-Diaz et al. [58] formulated a general-purpose perturbation theory or model for multiple-boundary QSPR/QSAR problems. We adapted here this new theory or modeling method to approach to the peptide prediction problem from the point of view of perturbation theory. Let be a set of ith peptide molecules denoted as m
i with a value of efficiency ε
i
j as epitopes experimentally determined under a set of boundary conditions c
j
≡
(
c
0
,
c
1
,
c
2
,
c
3
,
…
,
c
n
). We put the main emphasis here on peptides reported in the database IEDB. In this sense, the boundary conditions c
j used here are the same reported in this database, c
0
=
is the specific peptide, c
1
=
s
o
j, c
2
=
h
o
j, c
3
=
i
p
j, and c
4
=
t
q
j. In general, so is the organism that expresses the peptide (but it can include also artificial peptides, cellular lines, etc.), ho is the host organism exposed to the peptide by means of the bp detected with tq. As our analysis, based on the data reported by IEDB we are unable to work with continuous values of epitope activity ε
i
j. Consequently, we have to predict the discrete function of B-epitope efficiency λ
(
ε
i
j
)
=
1 for epitopes reported in the conditions c
j and λ
(
ε
i
j
)
=
0, otherwise. Our main aim is to predict the shift or change in a function of the output efficiency Δ
λ
(
ε
i
j
)
=
λ
(
ε
i
j
)
ref
-
λ
(
ε
i
j
)
new that takes place after a change, variation, or perturbation (
Δ
V
) in the structure and/or boundary conditions of a peptide of reference. But we know the efficiency of the process of reference λ
(
ε
i
j
)
ref in addition to the molecular structure and the set of conditions c
j for initial (reference) and final processes (new). Consequently, to predict Δ
λ
(
ε
i
j
) we have to predict only λ
(
ε
i
j
)
new the efficiency function of the new state obtained by a change in the structure of the peptide and/or the boundary conditions. Let Δ
V be a perturbation in a function λ; we can define V
i
j as the state information function for the reference and new states. According to our recent model [58], we can write V
i
j as a function of the conditions and structure of the peptide m
i as follows. In fact, the variational state functions V
i
j have to be written in pairs in order to describe the initial (reference) and final (new) states of a perturbation, as follow:
(2)
V
i
j
=
λ
(
ε
i
j
)
new
-
∑
j
=
1
4
(
λ
(
m
i
)
-
λ
(
c
i
j
)
avg
)
,
V
q
r
=
λ
(
ε
q
r
)
ref
-
∑
r
=
1
4
(
λ
(
m
q
)
-
λ
(
c
q
r
)
avg
)
.The state functionV
n
i
j is for the ith peptide measured under a set of c
i
j boundary conditions in output, final, or new state. The conjugated state function V
r
q
r is for the qth peptide measured under a set of c
q
r boundary conditions for the input, initial, or reference state. The difference Δ
V between the new (output) state and the reference (input) state is the additive perturbation [58]. Consider
(3)
Δ
V
=
V
i
j
-
V
q
r
=
[
λ
(
ε
i
j
)
new
-
∑
j
=
1
4
(
λ
(
m
i
)
-
λ
(
c
i
j
)
avg
)
]
-
[
λ
(
ε
q
r
)
ref
-
∑
r
=
1
4
(
λ
(
m
q
)
-
λ
(
c
q
r
)
avg
)
]
.Equation (3) described before opens the door to test different hypothesis. A simple hypotheses is H0: existence of one small and constant value of the perturbation function Δ
V
=
e
0 for all the pairs of peptides and a linear relationship between perturbations of input/output boundary conditions with coefficients a
i
j, b
i
j, c
q
r, and d
i
j. Consider
(4)
e
0
=
Δ
V
=
[
a
i
j
·
λ
(
ε
i
j
)
new
-
∑
j
=
1
4
b
i
j
·
(
λ
(
m
i
)
-
λ
(
c
j
)
avg
)
]
-
[
c
q
r
·
λ
(
ε
q
r
)
ref
-
∑
r
=
1
4
d
q
r
·
(
λ
(
m
q
)
-
λ
(
c
r
)
avg
)
]
.We can use elemental algebraic operations to obtain from these equations an expression for efficiency as epitope of the peptideλ
(
ε
i
j
)
new. In this case, considering b
i
j
≈
d
q
r, we can obtain the different expressions; the last may be very useful to solve the QSRR problem for the large datasets formed by IEDB B-epitopes. Consider
(5)
λ
(
ε
i
j
)
new
=
(
c
q
r
a
i
j
)
·
λ
(
ε
q
r
)
ref
+
[
∑
j
=
1
4
(
b
q
r
a
i
j
)
·
(
λ
(
m
i
)
-
λ
(
c
j
)
avg
)
new
]
-
[
∑
r
=
1
4
(
d
q
r
a
i
j
)
·
(
λ
(
m
q
)
-
λ
(
c
r
)
avg
)
ref
]
+
(
e
0
a
i
j
)
,
λ
(
ε
i
j
)
new
=
c
0
′
·
λ
(
ε
q
r
)
ref
+
∑
j
=
1
4
d
i
j
′
·
Δ
(
λ
(
m
i
)
-
λ
(
c
j
)
avg
)
+
e
0
′
,
λ
(
ε
i
j
)
new
=
c
0
′
·
λ
(
ε
q
r
)
ref
+
∑
j
=
1
4
d
i
j
′
·
Δ
Δ
λ
i
j
q
r
+
e
0
′
,
λ
(
ε
i
j
)
new
=
c
0
′
·
λ
(
ε
q
r
)
ref
+
∑
j
=
1
4
d
i
j
′
·
Δ
Δ
χ
i
j
q
r
+
e
0
′
.


## 3. Results and Discussion

We propose herein, for the first time, a QSRR-perturbation model able to predict variations in the propensity of a peptide to act as B-epitope taking into consideration the propensity of a peptide of reference and the changes in peptide sequence, immunological process, host organism, source organisms, and the experimental technique used. The best QSPR-perturbation model found here with LDA was(6)
λ
(
ε
i
j
)
new
=
4.979
·
λ
(
ε
i
j
)
ref
-
221.642
·
Δ
χ
seq
+
8.770
·
Δ
Δ
χ
ho
+
63.572
·
Δ
Δ
χ
so
-
55.387
·
Δ
Δ
χ
ip
+
201.919
·
Δ
Δ
χ
tq
-
2.149
,
N
=
155169
,
R
c
=
0.92
,
U
=
0.15
,
p
<
0.01
.The first input term is the valueλ
(
ε
i
j
)
ref is the scoring function λ of the efficiency of the initial process ε
i
j (known solution). The function λ
(
ε
i
j
)
ref
=
1 if the ith peptide could experimentally be demonstrated to be a B-epitope in the assay of reference (reference) carried out in the conditions c
j, λ
(
ε
i
j
)
ref
=
0 otherwise. The variational-perturbation terms Δ
Δ
χ
c
j are at the same time terms typical of perturbation theory and moving average (MA) functions used in Box-Jenkin models in time series [60]. These new types of terms account both for the deviation of the electronegativity of all amino acids in the sequence of the new peptide with respect to the peptide of reference and with respect to all boundary conditions. In Table 1, we give the overall classification results obtained with this model. Speck-Planche et al. [61–63] introduced different multitarget/multiplexing QSAR models that incorporate this type of information based on MAs. The results obtained with the present model are excellent compared with other similar models in the literature useful for other problems including moving average models [64, 65] or perturbation models [58]. Notably, this is also the first model combining both perturbation theory and MAs in a QSPR context.Table 1
Results of QSPR-perturbation model for IEDB B-Epitopes.
Data
Stat.
Pred.
Predicted epitope perturbations
subset
param.
%
λ
(
ε
i
j
)
=
1
λ
(
ε
i
j
)
=
0
λ
(
ε
i
j
)
=
1
Sp
97.0
84607
2660
λ
(
ε
i
j
)
=
0
Sn
93.6
4354
63548
Total train
Ac
95.5
λ
(
ε
i
j
)
=
1
Sp
97.1
28060
840
λ
(
ε
i
j
)
=
0
Sn
93.3
1485
20641
Total cv
Ac
95.4
Bold font is used to highlight the number of cases correctly classified by the model.The other input terms are the following. The firstΔ
χ
seq
=
χ
(
m
q
)
ref
-
χ
(
m
i
)
new is the perturbation term for the variation or in the mean value of electronegativity for all amino acids in the sequence of the peptide of reference. The remnant input variables of the model Δ
Δ
χ
c
j
=
Δ
χ
c
j
-ref
-
Δ
χ
c
j
-new
=
[
χ
(
m
q
)
ref
-
χ
*
(
c
q
r
)
ref
]
-
[
χ
(
m
i
)
new
-
χ
*
(
c
i
j
)
new
] quantify values of the conditions of the new assay c
j-new that represent perturbations with respect to the initial conditions c
i
j-ref of the assay of reference. The quantities χ
*
(
c
i
j
) and χ
*
(
c
q
r
) are the average values of the mean electronegativity values χ
(
m
i
) and χ
(
m
q
) for all new and reference peptides in IEDB that are epitopes under the jth or rth boundary condition. The values of these terms have been tabulated for >500 source organisms, >50 host organisms, >10 biological process, and >30 experimental techniques. We must substitute the values of χ
(
m
i
) and χ
(
m
q
) of the new and reference peptides and the tabulated values of χ
*
(
c
i
j
) and χ
*
(
c
q
r
) for all combinations of boundary conditions to predict the perturbations of the action as epitope of peptides. In doing so we can found the optimal sequence and boundary conditions towards the use of the peptide in the development of a vaccine. In Table 2 we give some of these values of χ
*
(
c
i
j
) and χ
*
(
c
q
r
).Table 2
Average values and count of input-output cases for different organisms, process, and techniques.
Source organism (so)
N
in
N
out
*χ
Homo sapiens
38920
39274
2.685
Plasmodium falciparum
10669
9446
2.704
Hepatitis C virus
9935
10239
2.683
Bos taurus
5671
5780
2.690
Canine parvovirus
5655
5637
2.693
Foot-mouth disease virus
4062
4176
2.676
Triticum aestivum
3769
3887
2.703
Bacillus anthracis
3602
3600
2.699
Human papillomavirus
3316
3414
2.693
Human herpesvirus
3026
3132
2.684
Gallus gallus
2850
2829
2.689
Arachis hypogaea
2648
2670
2.687
Mycobacterium tuberculosis
2637
2593
2.688
Clostridium botulinum
2588
2722
2.685
SARS coronavirus
2550
2704
2.686
Mus musculus
2334
2287
2.682
Hepatitis B virus
2007
2066
2.680
Helicobacter pylori
1958
1796
2.695
Hevea brasiliensis
1938
1958
2.697
Hepatitis E virus
1928
1941
2.685
Shigella flexneri
1878
1701
2.699
Dengue virus 2
1767
1828
2.679
Staphylococcus aureus
1757
1661
2.694
Treponema pallidum
1739
1755
2.691
Escherichia coli
1721
1678
2.689
Murine hepatitis virus
1575
1603
2.692
Haemophilus influenzae
1545
1587
2.695
Streptococcus mutans
1523
1537
2.697
Puumala virus (strain)
1505
1574
2.689
Chlamydia trachomatis
1402
1546
2.704
Human respiratory  virus
1347
1398
2.682
Borrelia burgdorferi
1228
1237
2.698
Hepatitis delta virus
1182
1199
2.690
Streptococcus pyogenes
1181
1251
2.697
Porphyromonas gingivalis
1143
1085
2.688
Human enterovirus
1106
1132
2.689
Influenza A virus
1085
1086
2.687
Mycoplasma hyopneumoniae
1044
1024
2.695
Rattus norvegicus
1025
1039
2.689
Bordetella pertussis
1011
960
2.685
Human T-lymphotropic virus
996
1031
2.680
Anaplasma marginale
977
857
2.707
Measles virus strain
804
810
2.688
Fasciola hepatica
803
857
2.685
Neisseria meningitidis
789
853
2.696
Human poliovirus
766
780
2.690
Tityus serrulatus
764
775
2.680
Torpedo californica
752
788
2.687
Cryptomeria japonica
719
794
2.680
Mycobacterium bovis
717
733
2.688
Trypanosoma cruzi
691
777
2.704
Andes virus CHI-7913
679
687
2.690
Bovine papillomavirus
672
665
2.692
Human hepatitis
670
696
2.688
Leishmania infantum
659
735
2.688
Human parvovirus
649
691
2.683
Poa pratensis
648
664
2.692
Aspergillus fumigatus
642
709
2.677
Duck hepatitis
587
603
2.688
Olea europaea
571
577
2.692
Porcine reproductive
515
514
2.681
Fagopyrum esculentum
509
497
2.685
Juniperus ashei
505
568
2.672
Mycobacterium leprae
489
542
2.690
Glycine max
477
509
2.685
D. pteronyssinus
455
464
2.680
Plasmodium vivax
453
446
2.690
Chlamydophila pneumoniae
446
462
2.690
Pseudomonas aeruginosa
443
454
2.691
Vibrio cholera
427
426
2.694
Streptococcus sp.
426
425
2.691
Mycobacterium avium
425
415
2.689
Dermatophagoides farinae
410
390
2.693
Human coxsackievirus
406
392
2.694
Equine infectious  virus
404
419
2.688
Babesia equi
383
371
2.696
Prunus dulcis
383
379
2.708
Human adenovirus
375
405
2.686
Theileria parva
366
371
2.713
Candida albicans
365
370
2.690
Porcine endogenous
355
351
2.692
Ovis aries
352
350
2.683
Chironomus thummi
347
338
2.691
Sus scrofa
343
362
2.686
Bovine leukemia virus
333
329
2.676
Ricinus communis
329
314
2.692
Androctonus australis
322
357
2.685
Renibacterium salmoninarum
319
350
2.690
Orientia tsutsugamushi
309
372
2.705
Anacardium occidentale
293
306
2.693
Conus geographus
289
295
2.660
Host organism (ho)
N
in
N
out
*χ
Homo sapiens
257293
91093
2.6856
Mus musculus
107867
51466
2.6873
Oryctolagus cuniculus
65053
31433
2.6900
Bos taurus
15333
2072
2.6909
Rattus norvegicus
9450
3562
2.6876
Aotus sp.
9044
3933
2.6879
Sus scrofa
7725
3464
2.6873
Gallus gallus
7507
997
2.6790
Canis lupus
6604
3334
2.6906
Macaca mulatta
5261
2569
2.6993
Ovis aries
3953
1653
2.6836
Equus caballus
3943
2099
2.6842
Cavia porcellus
3458
1688
2.6833
Capra hircus
2182
1127
2.6830
Aotus nancymaae
1659
852
2.6837
Pan troglodytes
1614
732
2.6757
Marmota monax
1100
509
2.7011
Felis catus
901
279
2.6838
Myodes glareolus
814
388
2.6863
Anas platyrhynchos
688
342
2.6880
Homo sapiens  (human)
508
270
2.6851
Trichosurus vulpecula
456
126
2.6921
Mesocricetus auratus
438
104
2.6909
Macaca cyclopis
382
193
2.6871
O. tshawytscha
333
159
2.6929
Macaca fuscata
188
100
2.6667
Cricetulus migratorius
171
142
2.7008
Camelus dromedarius
171
89
2.6886
Dicentrarchus labrax
121
55
2.6759
Macaca fascicularis
96
52
2.6793
Saimiri sciureus
92
44
2.6900
Canis familiaris
77
42
2.6850
Rattus rattus
72
31
2.6760
Callithrix pygmaea
67
30
2.6920
Chinchilla lanigera
41
24
2.6729
Aotus lemurinus
30
19
2.6860
Papio cynocephalus
27
13
2.7267
Aotus griseimembra
26
12
2.7000
Mustela vison
18
10
2.7000
Chlorocebus aethiops
15
10
2.6875
Bos indicus
13
4
2.6925
Oncorhynchus mykiss
10
4
2.6700
M. macquariensis
9
6
2.6600
Cricetulus griseus
8
4
2.6900
Aotus trivirgatus
7
4
2.7000
Process type (pt)
N
in
N
out
*χ
AID
111197
108536
2.6876
OOID
32419
32617
2.6868
OAID
19210
18954
2.6801
OOA
15863
16303
2.6902
NI
13430
15206
2.6845
EWEIR
4818
4864
2.6843
EEE
3113
3546
2.6906
OOD
2806
2799
2.6887
AICD
1077
1095
2.6812
EWED
696
686
2.6879
DEWED
280
337
2.6804
TT
260
215
2.6806
OOC
153
137
2.6800
Technique (tq)
N
in
N
out
*χ
ELISA
133458
135109
2.6871
WI
33627
33292
2.6887
ACAbB
7780
9068
2.6862
PhDIP
7450
4496
2.6771
RIA
5241
5218
2.6858
IFAIH
4454
4581
2.6879
NIAA
4222
4316
2.6892
FIA
2255
2276
2.6897
PAC
1312
1219
2.6837
IP
1127
1089
2.6886
SPR
758
639
2.6860
FACS
608
647
2.6907
Other
502
495
2.6813
SAC
484
393
2.6878
ELISPOT
396
412
2.6979
RDAT
366
323
2.6859
EDAT
284
330
2.6800
XRC
231
227
2.6880
MS
209
179
2.6849
PFF
171
153
2.6820
AbDPO
162
295
2.6968
CdC
146
205
2.6895
IAbBA
144
183
2.6940
IOT
124
106
2.6835
HAGGI
115
122
2.6834
IgMHR
89
90
2.6929
EAAA
84
139
2.6922
HS
82
67
2.6791
AbdCC
73
118
2.6897
AGG
50
60
2.6980
CM
50
57
2.6863
The∗indicates that quantities like ∗
χ is the average value of the mean electronegativity (m
i) for all the peptides in IEDB that are epitopes for the same boundary condition.In Table3 we depict the sequences and input-output boundary conditions for top perturbations present in IEDB. All these perturbations have observed value of λ
(
ε
i
j
)
new
=
1 and predicted value also equal to 1 with a high probability. See Supplementary Material available online at http://dx.doi.org/10.1155/2014/768515 file contains a full list of >200,000 cases of perturbations.Table 3
Top100 values of p1 for positive perturbations in training series.
New experiment
Experiment of reference
Input perturbation terms
IDE
Sequence
ho
so
ip
tq
IDE
Sequence
ho
so
ip
tq
Δ
χ
Δ
Δ
χ
ho
Δ
Δ
χ
so
Δ
Δ
χ
ip
Δ
Δ
χ
tq
115153
MKGVVCTRIYEKV
Homo sapiens
Homo sapiens
OAID
ELISA
115155
NNQRKKAKNTPFNMLKRERN
Mus musculus
Dengue virus 2
AID
WI
0.01
0.012
0.004
0.017
0.012
52124
QQQPP
Homo sapiens
Triticum aestivum
OOA
ELISA
52128
QQQQGGSQSQKGKQQ
Homo sapiens
Glycine max
OOA
WI
0
0
−0.018
0
0.002
3639
APLGVT
Homo sapiens
Hepatitis E virus
EWEIR
ELISA
3652
APLTRGSCRKRNRSPER
Homo sapiens
Human herpesvirus
OOID
ELISA
0.04
0.04
0.039
0.043
0.04
135959
LTRAYAKDVKFG
Homo sapiens
Homo sapiens
OAID
ELISA
135963
NGQEEKAGVVSTGLIGGG
Mus musculus
MD
AID
ELISA
−0.04
−0.038
−0.047
−0.033
−0.04
108075
PREPQVY
Homo sapiens
Homo sapiens
OAID
ELISA
108078
PTSPSGVEEWIVTQVVPGVA
Oryctolagus cuniculus
Homo sapiens
AID
ACAbB
−0.01
−0.006
−0.01
−0.003
−0.011
25113
HVVDLP
Homo sapiens
Hepatitis E virus
EWEIR
ELISA
25126
HWGNHSKSHPQR
Mus musculus
MD
AID
ELISA
0.02
0.022
0.013
0.023
0.02
48780
PPFSPQ
Homo sapiens
Hepatitis delta virus
OOID
ELISA
48782
PPFTSAVGGVDHRS
Mus musculus
MD
AID
SAC
0.02
0.022
0.008
0.021
0.021
40988
LYVVAYQA
Mus musculus
Viscum album
AID
ELISA
41004
MAARLCCQLDPARDV
Homo sapiens
Hepatitis B virus
OOID
ELISA
0.02
0.018
0.006
0.019
0.02
50439
QDAYNAAG
Mus musculus
Mycobacterium scrofulaceum
AID
ELISA
50445
QDCNCSIYPGHASGHRMAWD
Homo sapiens
Hepatitis C virus
OOID
ELISA
0.04
0.038
0.028
0.039
0.04
98849
KIPAVFKIDA
Homo sapiens
Bos taurus
DEWED
WI
98850
KKGSEEEGDITNPIN
Homo sapiens
Arachis hypogaea
OOA
IFAIH
−0.05
−0.05
−0.053
−0.04
−0.051
116171
TQDQDPBBHFFKNIVTPR
Homo sapiens
Homo sapiens
OAID
ACAbB
116286
CGKGLSATVTGGQKGRGSR
Oryctolagus cuniculus
Mus musculus
AID
MS
0.01
0.014
0.008
0.017
0.009
123442
LLKDLRKN
Homo sapiens
Borna disease virus
EWEIR
WI
123443
LLTEHRMTWDPAQPPRDLTE
Homo sapiens
Homo sapiens
OOID
ELISA
−0.02
−0.02
−0.025
−0.017
−0.022
47858
PHVVDL
Homo sapiens
Hepatitis E virus
EWEIR
ELISA
47860
PHWIKKPNRQGLGYYS
Capra hircus
Human T-lymphotropic virus
AID
ACAbB
0.01
0.007
0.005
0.013
0.009
61783
STNKAVVSLS
Bos taurus
Bovine respiratory
AID
ELISA
61792
STNPKPQRKTKRNTNRRPQD
Homo sapiens
Hepatitis C virus
OOID
ELISA
0.03
0.025
0.019
0.029
0.03
118210
VMLYQISEE
Homo sapiens
Homo sapiens
OAID
WI
118217
VTKYITKGWKEVH
Oryctolagus cuniculus
Homo sapiens
AID
ELISA
0.05
0.054
0.05
0.057
0.048
130944
LFKHS
Oryctolagus cuniculus
Rattus norvegicus
AID
ELISA
130956
LPPRVTPKWSLDAWSTWR
Homo sapiens
MD
OOD
WI
0.01
0.006
−0.002
0.011
0.012
23028
GVKYA
Homo sapiens
MD
OAID
WI
23032
GVLAKDVRFSQV
Homo sapiens
MD
OOID
ELISA
0
0
0
0.007
−0.002
51199
QKKAIE
Oryctolagus cuniculus
Vibrio cholerae
AID
ELISA
51204
QKKNKRNTNRRPQDV
Homo sapiens
Hepatitis C virus
OOID
ELISA
0.03
0.026
0.018
0.029
0.03
144783
SHVVTMLDNF
Homo sapiens
Homo sapiens
NI
ELISA
144786
SMNRGRGTHPSLIWM
Mus musculus
MD
AID
ACAbB
0.03
0.032
0.023
0.033
0.029
134343
DLYIK
Mus musculus
Human papillomavirus
AID
NIAA
134344
DMAQVTVGPGLLGVSTL
Mus musculus
Homo sapiens
AID
WI
0
0
−0.009
0
0
38321
LNQLAGRM
Anas platyrhynchos
Duck hepatitis
AID
ELISA
38323
LNQTARAFPDCAICWEPSPP
Oryctolagus cuniculus
Bovine leukemia virus
AID
ACAbB
−0.01
−0.008
−0.022
−0.01
−0.011
144657
GQITVDMMYG
Homo sapiens
Homo sapiens
OAID
ELISA
144661
GREGYPADGGCAWPACYC
Oryctolagus cuniculus
MD
AID
WI
0.02
0.024
0.013
0.027
0.022
21084
GLQN
Mus musculus
Chlamydia trachomatis
AID
ELISA
21093
GLRAQDDFSGWDINTPAFEW
Mus musculus
Mycobacterium tuberculosis
AID
WI
0.03
0.03
0.013
0.03
0.032
98453
SGFSGSVQFV
Oryctolagus cuniculus
Neisseria meningitidis
AID
ELISA
98456
SICSNNPTCWAICKRIPNKK
Mus musculus
Human respiratory virus
AID
IFAIH
0.04
0.037
0.027
0.04
0.041
98453
SGFSGSVQFV
Mus musculus
Neisseria meningitidis
AID
ELISA
98456
SICSNNPTCWAICKRIPNKK
Mus musculus
Human respiratory virus
AID
IFAIH
0.04
0.04
0.027
0.04
0.041
107107
EAIQP
Rattus norvegicus
Homo sapiens
AID
ELISA
107110
EKERRPSPIGTATLL
Homo sapiens
MD
OOA
ELISA
0.05
0.048
0.043
0.053
0.05
110857
FTGEAYSYWSAK
Homo sapiens
Mycoplasma penetrans
EWEIR
ELISA
110859
GEESRISLPLPNFSSLNLRE
Mus musculus
Homo sapiens
AID
FIA
0
0.002
−0.017
0.003
0.003
36315
LGSAYP
Mus musculus
Mycobacterium leprae
MD
ELISA
36317
LGSGAFGTIYKG
Mus musculus
Avian erythroblastosis virus
AID
ACAbB
0.01
0.01
0
0.011
0.009
122034
WNPAD
Rattus norvegicus
Torpedo californica
AID
ELISA
122035
WNPADYGGIKWNPADYGGIK
Rattus norvegicus
MD
AID
RIA
0.01
0.01
0.001
0.01
0.009
25013
HVADIDKLID
Mus musculus
Puumala virus Kazan
AID
ELISA
25021
HVAPTHYVTESDASQRVTQL
Homo sapiens
Hepatitis C virus
OOID
ELISA
0
−0.002
−0.014
−0.001
0
36162
LGIHE
Oryctolagus cuniculus
Candida albicans
AID
ELISA
36166
LGIMGEYRGTPRNQDLYDAA
Mus musculus
Human respiratory  virus
AID
RIA
0
−0.003
−0.007
0
−0.001
67253
TWEVLH
Mus musculus
Plasmodium vivax
AID
ELISA
67257
TWGENETDVLLLNNTRPPQ
Homo sapiens
Hepatitis C virus
OOID
ACAbB
−0.02
−0.022
−0.027
−0.021
−0.021
50990
QGYRVSSYLP
Homo sapiens
Hevea brasiliensis
OOA
WI
50998
QHEQDRPTPSPAPSRPFSVL
Homo sapiens
Hepatitis E virus
OOID
ELISA
0.01
0.01
−0.002
0.007
0.008
100458
RDVLQLYAPE
Mus musculus
Bacillus anthracis
AID
ELISA
100462
RFSTRYGNQNGRIRVLQRFD
Homo sapiens
Arachis hypogaea
EWED
ELISA
0.03
0.028
0.018
0.03
0.03
111036
TESTFTGEAYSV
Homo sapiens
Mycoplasma penetrans
EWEIR
ELISA
111039
TGVPIDPAVPDSSIVPLLES
Bos taurus
Bovine papillomavirus
AID
ELISA
0.03
0.035
0.02
0.033
0.03
117919
IFIEME
Homo sapiens
Homo sapiens
OAID
WI
117921
IGIIDLIEKRKFNQ
Mus musculus
Homo sapiens
AID
WI
0.03
0.032
0.03
0.037
0.03
7127
CTDTDKLF
Oryctolagus cuniculus
Shigella flexneri
AID
ELISA
7128
CTDVSTAIHADQLTPAW
Homo sapiens
SARS coronavirus
OOID
ELISA
0.01
0.006
−0.003
0.009
0.01
112253
PGQSPKL
Homo sapiens
Homo sapiens
OAID
ELISA
112255
PIRALVGDEVELPCRISPGK
Mus musculus
Homo sapiens
AID
ELISA
0.01
0.012
0.01
0.017
0.01
122034
WNPAD
Rattus norvegicus
Torpedo californica
AID
ELISA
122038
WNPDDYGGVKWNPDDYGGVK
Rattus norvegicus
MD
AID
RIA
0
0
−0.009
0
−0.001
131878
FLMLVGGSTL
Homo sapiens
Homo sapiens
OAID
ACAbB
131879
FLVAHTRARAPSAGERARRS
Mus musculus
Mus musculus
AID
NIAA
0.03
0.032
0.028
0.037
0.033
70664
VQVVYDYQ
Homo sapiens
Treponema pallidum
OOID
ELISA
70667
VQWMNRLIAFAFAGNHVSP
Homo sapiens
Hepatitis C virus
OOID
ELISA
0.05
0.05
0.041
0.05
0.05
71545
VTV
Homo sapiens
Helicobacter pylori
OOID
ELISA
71559
VTVRGGLRILSPDRK
Homo sapiens
Arachis hypogaea
OOA
WI
0.04
0.04
0.032
0.043
0.042
127856
TDVRYKD
Mus musculus
Mus musculus
AID
ACAbB
127857
TDVRYKDDMYHFFCPAIQAQ
Mus musculus
Mus musculus
AID
PFF
0.01
0.01
0.01
0.01
0.006
112149
GVGWIRQ
Homo sapiens
Homo sapiens
OAID
ELISA
112152
HHPARTAHYGSLPQKSHGRT
Homo sapiens
Homo sapiens
AID
ELISA
0
0
0
0.007
0
119581
FSCSVMHE
Homo sapiens
Homo sapiens
OAID
ELISA
119582
GLQLIQLINVDEVNQI
Mus musculus
Homo sapiens
AID
RIA
−0.01
−0.008
−0.01
−0.003
−0.011
144657
GQITVDMMYG
Homo sapiens
Homo sapiens
OAID
ELISA
144659
GREGYPADGGAAGYCNTE
Oryctolagus cuniculus
MD
AID
WI
−0.01
−0.006
−0.017
−0.003
−0.008
25013
HVADIDKLID
Mus musculus
Puumala virus Kazan
AID
ELISA
25022
HVAPTHYVVESDASQRVTQV
Homo sapiens
Hepatitis C virus
OOID
ELISA
0
−0.002
−0.014
−0.001
0
144652
GMRGMKGLVY
Homo sapiens
Homo sapiens
OAID
ELISA
144654
GPHPTLEVVPMGRGS
Mus musculus
MD
AID
ELISA
−0.02
−0.018
−0.027
−0.013
−0.02
104515
HDCRPKKI
Mus musculus
La Crosse virus
AID
IFAIH
104521
IGTLKKILDETVKDKIAKEQ
Rattus norvegicus
Streptococcus pyogenes
AID
ELISA
−0.04
−0.04
−0.053
−0.04
−0.041
7367
CYGDWA
Homo sapiens
Triticum aestivum
OOA
ELISA
7374
CYGLPDSEPTKTNGK
Mus musculus
Tityus serrulatus
AID
WI
−0.02
−0.018
−0.043
−0.023
−0.018
7367
CYGDWA
Homo sapiens
Triticum aestivum
OOA
ELISA
7374
CYGLPDSEPTKTNGK
Mus musculus
Tityus serrulatus
AID
WI
−0.02
−0.018
−0.043
−0.023
−0.018
144610
DFFTYK
Mus musculus
Porcine transmissible
AID
WI
144611
DFNGSFDMNGTITA
Oryctolagus cuniculus
Escherichia coli
AID
ELISA
−0.01
−0.007
−0.018
−0.01
−0.012
112047
ASTRESG
Homo sapiens
Homo sapiens
OAID
ELISA
112048
ATASTMDHARHGFLPRHRDT
Homo sapiens
Homo sapiens
AID
ELISA
0.05
0.05
0.05
0.057
0.05
36136
LGGVFT
Homo sapiens
Dengue virus 2
OOID
ELISA
36137
LGGWKLQSDPRAYAL
Homo sapiens
Ambrosia artemisiifolia
OOA
RIA
0.01
0.01
0.007
0.013
0.009
115256
FRELKDLKGY
Homo sapiens
Bos taurus
DEWED
WI
115261
GDLEILLQKWENGECAQKKI
Homo sapiens
Bos taurus
OOA
FIA
−0.01
−0.01
−0.01
0
−0.009
129024
KADQLYK
Homo sapiens
Homo sapiens
OAID
ELISA
129026
KAKKPAAAAGAKKAKS
Oryctolagus cuniculus
Homo sapiens
AID
ELISA
0.03
0.034
0.03
0.037
0.03
148481
YTRDLVYK
Rattus norvegicus
Homo sapiens
AID
WI
148483
YVPIVTFYSEISMHSSRAIP
Oryctolagus cuniculus
MD
AID
ELISA
0
0.002
−0.007
0
−0.002
150850
GY
Mus musculus
Human papillomavirus
AID
ELISA
150853
HIGGLSILDPIFGVL
Homo sapiens
Dermatophagoides farinae
OOA
ACAbB
0.04
0.038
0.04
0.043
0.039
107366
FPPKPKD
Homo sapiens
Homo sapiens
OAID
ELISA
107376
GDRSGYSSPGSPG
Mus musculus
Homo sapiens
AID
ACAbB
−0.03
−0.028
−0.03
−0.023
−0.031
114859
ICGTDGVTYT
Homo sapiens
Gallus gallus
OOA
WI
114865
IVERETRGQSENPLWHALRR
Rattus norvegicus
Human herpesvirus
AID
ELISA
0.04
0.042
0.035
0.037
0.038
62149
SVHLF
Homo sapiens
MD
OAID
WI
62150
SVIALGSQEGALHQALAGAI
Equus caballus
West Nile virus
AID
IFAIH
−0.02
−0.021
−0.012
−0.013
−0.021
98455
SGSVQFVPIQ
Mus musculus
Neisseria meningitidis
AID
ELISA
98456
SICSNNPTCWAICKRIPNKK
Mus musculus
Human respiratory  virus
AID
IFAIH
0.04
0.04
0.027
0.04
0.041
61783
STNKAVVSLS
Bos taurus
Bovine respiratory
AID
ELISA
61791
STNPKPQRKTKRNTNRRPQ
Homo sapiens
Hepatitis C virus
EWEIR
ACAbB
0.04
0.035
0.029
0.037
0.039
100318
NAPKTFQFIN
Mus musculus
Bacillus anthracis
AID
ELISA
100319
NASSELHLLGFGINAENNHR
Homo sapiens
Arachis hypogaea
EWED
ELISA
0
−0.002
−0.012
0
0
118947
PFSAPPPA
Homo sapiens
Homo sapiens
OAID
ELISA
118948
PGAIEQGPADDPGEGPSTGP
Homo sapiens
Human herpesvirus
NI
ACAbB
−0.04
−0.04
−0.041
−0.036
−0.041
78323
YSFRD
Mus musculus
Bluetongue virus 1
AID
ELISA
78341
AALTAENTAIKKRNADAKA
Homo sapiens
Streptococcus mutans
EEE
ELISA
0.01
0.008
0.007
0.013
0.01
145831
IPLGTRP
Mus musculus
Human papillomavirus
AID
ELISA
145841
KEDFRYAISSTNEIGLLGA
Sus scrofa
Classical swine
AID
PAC
−0.04
−0.04
−0.045
−0.04
−0.043
119592
HTFPAVLQ
Homo sapiens
Homo sapiens
OAID
ELISA
119596
IHIPSEKIWRPDLVLY
Mus musculus
Homo sapiens
AID
RIA
0.01
0.012
0.01
0.017
0.009
58780
SKAANLSIIK
MD
Beet necrotic
MD
WI
58783
SKAFSNCYPYDVPDYASL
Oryctolagus cuniculus
Influenza A virus
AID
RIA
−0.01
−0.004
−0.014
−0.009
−0.013
115153
MKGVVCTRIYEKV
Homo sapiens
Homo sapiens
NI
ELISA
115155
NNQRKKAKNTPFNMLKRERN
Mus musculus
Dengue virus 2
AID
ELISA
0.01
0.012
0.004
0.013
0.01
133629
LPLRF
Oryctolagus cuniculus
Gallus gallus
AID
ACAbB
133630
LPPGLHVFPLASNRS
Mus musculus
MD
AID
SPR
−0.01
−0.013
−0.021
−0.01
−0.01
96215
EEEEAEDKED
Homo sapiens
Homo sapiens
OAID
ELISA
96216
EEEGLLKKSADTLWNMQK
Mus musculus
Mus musculus
AID
ELISA
0.08
0.082
0.078
0.087
0.08
39782
LTAASV
Homo sapiens
Triticum aestivum
OOA
ELISA
39788
LTAELKIYSVIQAEINKHL
Oryctolagus cuniculus
Yersinia pestis
AID
ACAbB
0.01
0.014
−0.001
0.007
0.009
107479
KFNWYVD
Homo sapiens
Homo sapiens
OAID
ELISA
107482
KGEPGLPGRGFPGFP
Mus musculus
Homo sapiens
AID
ACAbB
0
0.002
0
0.007
−0.001
63569
TETVNSDI
Macaca mulatta
Shigella flexneri
AID
ELISA
63573
TEVELKERKHRIEDAVRNAK
Homo sapiens
Mycobacterium leprae
OOID
ELISA
0.05
0.036
0.041
0.049
0.05
134028
DDTIS
Mus musculus
Homo sapiens
AID
NIAA
134029
DEDENQSPRSFQKKTR
Oryctolagus cuniculus
Homo sapiens
AID
ELISA
0.05
0.053
0.05
0.05
0.048
23028
GVKYA
Homo sapiens
MD
OAID
WI
23032
GVLAKDVRFSQV
Homo sapiens
MD
EWEIR
ACAbB
0
0
0
0.004
−0.003
115293
IMCVKKILDK
Homo sapiens
Bos taurus
DEWED
WI
115295
INPSKENLCSTFCKEVVRNA
Homo sapiens
Bos taurus
OOA
FIA
−0.03
−0.03
−0.03
−0.02
−0.029
65105
TLTPENTL
Mus musculus
Shigella flexneri
AID
ELISA
65110
TLTSGSDLDRCTTFDDV
Oryctolagus cuniculus
SARS coronavirus
AID
ELISA
0.01
0.013
−0.003
0.01
0.01
134471
PKPEQ
Mus musculus
Streptococcus pneumoniae
AID
FACS
134472
PLLPGTSTTSTGPCKT
Homo sapiens
Hepatitis B virus
AID
ELISA
0.02
0.018
0.019
0.02
0.016
142228
LYCYEQLNDSSE
Homo sapiens
Human papillomavirus
NI
ELISA
142250
NWGDEPSKRRDRSNSRGRKN
Felis catus
Feline infectious
AID
ELISA
0.07
0.068
0.071
0.073
0.07
21549
GNYDFWYQS
Homo sapiens
Staphylococcus aureus
OOID
ELISA
21553
GNYNYKYRYLRHGKLRPFER
Mus musculus
SARS coronavirus
AID
ELISA
0.03
0.032
0.021
0.031
0.03
21549
GNYDFWYQS
Homo sapiens
Staphylococcus aureus
OOID
ELISA
21553
GNYNYKYRYLRHGKLRPFER
Oryctolagus cuniculus
SARS coronavirus
AID
ELISA
0.03
0.034
0.021
0.031
0.03
34908
LAPLGE
Homo sapiens
Hepatitis E virus
EWEIR
ELISA
34914
LAPSTLRSLRKRRLSSP
Homo sapiens
Human herpesvirus
OOID
ELISA
0.06
0.06
0.059
0.063
0.06
130454
CLFPNNSYC
Mus musculus
MD
AID
NIAA
130456
CRPQVNNPKEWSCAAC
Homo sapiens
MD
OOD
ACAbB
0.01
0.008
0.01
0.011
0.007
19644
GFVPSM
Homo sapiens
Hepatitis delta virus
OOID
ELISA
19647
GFVSASIFGFQAEVGPNNTR
Oryctolagus cuniculus
Vaccinia virus WR
AID
ELISA
−0.01
−0.006
−0.013
−0.009
−0.01
20678
GKRPE
Mus musculus
Streptococcus pyogenes
AID
WI
20680
GKSKRDAKNNAAKLAVDKLL
Mus musculus
Vaccinia virus WR
AID
WI
0.01
0.01
0.001
0.01
0.01
123278
GYLKDLPTT
Ovis aries
Fasciola hepatica
AID
ELISA
123282
HACQKKLLKFEALQQEEGEE
Rattus norvegicus
Gallus gallus
AID
PFF
−0.02
−0.016
−0.016
−0.02
−0.025
141067
PLSLEPDP
Mus musculus
Homo sapiens
AID
FACS
141073
REGVRWRVMAIQ
Mus musculus
Homo sapiens
AID
ELISA
0.06
0.06
0.06
0.06
0.056
139248
SFAGTVIE
Mus musculus
Classical swine
AID
WI
139305
TAAQITQRKWEAAREAEQRR
Oryctolagus cuniculus
Homo sapiens
AID
IP
0.03
0.033
0.026
0.03
0.03
104515
HDCRPKKI
Mus musculus
La Crosse virus
AID
IFAIH
104520
IAKEQENKETIGTLKKILDE
Rattus norvegicus
Streptococcus pyogenes
AID
ELISA
−0.06
−0.06
−0.073
−0.06
−0.061
113517
HLYADGLTD
Mus musculus
Human papillomavirus
AID
IFAIH
113518
HNKIQAIELEDLLRYSKLYR
Mus musculus
Homo sapiens
AID
ELISA
0.02
0.02
0.011
0.02
0.019
156970
VERHQ
Homo sapiens
Homo sapiens
OAID
ELISA
156975
WSSTVLRVSPTRTVP
Mus musculus
MD
AID
ELISA
0.01
0.012
0.003
0.017
0.01
78252
PVQNLT
Mus musculus
Porphyromonas gingivalis
AID
WI
78253
QGGCGRGWAFSATGAIEA
Mus musculus
Glycine max
AID
ELISA
0.02
0.02
0.017
0.02
0.018
6068
CCPDKNKS
Mus musculus
Human herpesvirus
AID
WI
6074
CCRHKQKDVGDVKQTLPPS
Ovis aries
MD
AID
ELISA
0.01
0.006
0.004
0.01
0.008
53109
RAGVCY
Homo sapiens
Triticum aestivum
OOA
ELISA
53116
RAILTAFSPAQDIWGTS
Oryctolagus cuniculus
SARS coronavirus
AID
ELISA
−0.02
−0.016
−0.037
−0.023
−0.02
147041
IPEQ
Homo sapiens
Triticum aestivum
OOA
WI
147064
KHQGAQYVWNRTA
Homo sapiens
Bos taurus
OOID
WI
0.06
0.06
0.047
0.057
0.06
70664
VQVVYDYQ
Oryctolagus cuniculus
Treponema pallidum
AID
ELISA
70667
VQWMNRLIAFAFAGNHVSP
Homo sapiens
Hepatitis C virus
OOID
ELISA
0.05
0.046
0.041
0.049
0.05
134343
DLYIK
Mus musculus
Human papillomavirus
AID
PhDIP
134344
DMAQVTVGPGLLGVSTL
Mus musculus
Homo sapiens
AID
PhDIP
0
0
−0.009
0
0


## 4. Conclusions

It is possible to develop general models for vaccine design able to predict the results of multiple input-output perturbations in peptide sequence and experimental assay boundary conditions using ideas of QSPR analysis, perturbation theory, and Box and Jenkins MA operators. The electronegativity values calculated with MARCH-INSIDE seem to be good molecular descriptors for this type of QSPR-perturbation models.



---

*Source: 768515-2014-01-12.xml*