Journal of Immunology Research

Volume 2014 (2014), Article ID 768515, 15 pages

http://dx.doi.org/10.1155/2014/768515

Research Article

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

Humberto González-Díaz,1,2 Lázaro G. Pérez-Montoto,³ and Florencio M. Ubeira³

¹Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940 Bilbao, Spain

²Ikerbasque, Basque Foundation for Science, 48011 Bilbao, Spain

³Department of Microbiology and Parasitology, University of Santiago de Compostela (USC), 15782 Santiago de Compostela, Spain

Received 21 August 2013; Accepted 17 November 2013; Published 12 January 2014

Academic Editor: Darren R. Flower

Copyright © 2014 Humberto González-Díaz et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.

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, RNA, drug-protein complexes, and protein-protein complexes. In general, QSAR/QSPR-like methods transform molecular structures into numeric molecular descriptors () 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 of th molecules , organic compounds, drugs, xenobiotics, and/or peptide sequences, the efficiency of the compound 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 () of assay for the initial experiment including structure of the molecule (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 th mean values of different physicochemical molecular properties for th molecules . These values are calculated as an average of values for all atoms placed at topological distance ; which are in turn the means of atomic properties for all atoms in the molecule and its neighbors placed at . For instance, it is possible to derive average estimations of molecular refractivities , partition coefficients , and hardness for atoms placed at different topological distances . In this first work, we calculated only one type of values. We calculated for all peptides the average value of all the atomic electronegativities for all atoms connected to the th atom and their neighbors placed at a distance [59]:

We calculate the probabilities for any atomic property including 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 th peptide molecules denoted as with a value of efficiency as epitopes experimentally determined under a set of boundary conditions . We put the main emphasis here on peptides reported in the database IEDB. In this sense, the boundary conditions used here are the same reported in this database, the specific peptide, , , , and . 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 . Consequently, we have to predict the discrete function of B-epitope efficiency for epitopes reported in the conditions and , otherwise. Our main aim is to predict the shift or change in a function of the output efficiency that takes place after a change, variation, or perturbation in the structure and/or boundary conditions of a peptide of reference. But we know the efficiency of the process of reference in addition to the molecular structure and the set of conditions for initial (reference) and final processes (new). Consequently, to predict we have to predict only the efficiency function of the new state obtained by a change in the structure of the peptide and/or the boundary conditions. Let be a perturbation in a function ; we can define as the state information function for the reference and new states. According to our recent model [58], we can write as a function of the conditions and structure of the peptide as follows. In fact, the variational state functions have to be written in pairs in order to describe the initial (reference) and final (new) states of a perturbation, as follow:

The state function is for the th peptide measured under a set of boundary conditions in output, final, or new state. The conjugated state function is for the th peptide measured under a set of boundary conditions for the input, initial, or reference state. The difference between the new (output) state and the reference (input) state is the additive perturbation [58]. Consider

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 for all the pairs of peptides and a linear relationship between perturbations of input/output boundary conditions with coefficients , , , and . Consider

We can use elemental algebraic operations to obtain from these equations an expression for efficiency as epitope of the peptide . In this case, considering , 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

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

The first input term is the value is the scoring function of the efficiency of the initial process (known solution). The function if the th peptide could experimentally be demonstrated to be a B-epitope in the assay of reference (reference) carried out in the conditions , otherwise. The variational-perturbation terms 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.
%

Sp
97.0
84607
2660

Sn
93.6
4354
63548

Total train
Ac
95.5

Sp
97.1
28060
840

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 first 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 quantify values of the conditions of the new assay -new that represent perturbations with respect to the initial conditions -ref of the assay of reference. The quantities and are the average values of the mean electronegativity values and for all new and reference peptides in IEDB that are epitopes under the th or th 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 and of the new and reference peptides and the tabulated values of and 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 and .

Table 2: Average values and count of input-output cases for different organisms, process, and techniques.

Source organism (so)

*

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)

*

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)

*

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)

*

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 ∗ is the average value of the mean electronegativity () for all the peptides in IEDB that are epitopes for the same boundary condition.

In Table 3 we depict the sequences and input-output boundary conditions for top perturbations present in IEDB. All these perturbations have observed value of 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

115153
MKGVVC

TRIYEKV
Homo sapiens
Homo sapiens
OAID
ELISA
115155
NNQRK

KAKNTP

FNMLKRERN
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
QQQQGG

SQSQKGKQQ
Homo sapiens
Glycine max
OOA
WI
0
0
−0.018
0
0.002

3639
APLGVT
Homo sapiens
Hepatitis E virus
EWEIR
ELISA
3652
APLTRG

SCRKRN

RSPER
Homo sapiens
Human herpesvirus
OOID
ELISA
0.04
0.04
0.039
0.043
0.04

135959
LTRAYA

KDVKFG
Homo sapiens
Homo sapiens
OAID
ELISA
135963
NGQEEK

AGVVS

TGLIGGG
Mus musculus
MD
AID
ELISA
−0.04
−0.038
−0.047
−0.033
−0.04

108075
PREPQVY
Homo sapiens
Homo sapiens
OAID
ELISA
108078
PTSPSGV

EEWIVTQ

VVPGVA
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
HWGNH

SKSHPQR
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
PPFTSAV

GGVDHRS
Mus musculus
MD
AID
SAC
0.02
0.022
0.008
0.021
0.021

40988
LYVVAYQA
Mus musculus
Viscum album
AID
ELISA
41004
MAARLCC

QLDPARDV
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
QDCNCSI

YPGHAS

GHRMAWD
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
KKGSEEE

GDITNPIN
Homo sapiens
Arachis hypogaea
OOA
IFAIH
−0.05
−0.05
−0.053
−0.04
−0.051

116171
TQDQDP

BBHFFK

NIVTPR
Homo sapiens
Homo sapiens
OAID
ACAbB
116286
CGKGLS

ATVTGG

QKGRGSR
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
LLTEHR

MTWDPA

QPPRDLTE
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
PHWIK

KPNRQG

LGYYS
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
STNPKP

QRKTKRN

TNRRPQD
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
VTKYITK

GWKEVH
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
LPPRVTP

KWSLDA

WSTWR
Homo sapiens
MD
OOD
WI
0.01
0.006
−0.002
0.011
0.012

23028
GVKYA
Homo sapiens
MD
OAID
WI
23032
GVLAKD

VRFSQV
Homo sapiens
MD
OOID
ELISA
0
0
0
0.007
−0.002

51199
QKKAIE
Oryctolagus cuniculus
Vibrio cholerae
AID
ELISA
51204
QKKNK

RNTNRR

PQDV
Homo sapiens
Hepatitis C virus
OOID
ELISA
0.03
0.026
0.018
0.029
0.03

144783
SHVVT

MLDNF
Homo sapiens
Homo sapiens
NI
ELISA
144786
SMNRGRG

THPSLIWM
Mus musculus
MD
AID
ACAbB
0.03
0.032
0.023
0.033
0.029

134343
DLYIK
Mus musculus
Human papillomavirus
AID
NIAA
134344
DMAQV

TVGPGLL

GVSTL
Mus musculus
Homo sapiens
AID
WI
0
0
−0.009
0
0

38321
LNQLAGRM
Anas platyrhynchos
Duck hepatitis
AID
ELISA
38323
LNQTAR

AFPDCAI

CWEPSPP
Oryctolagus cuniculus
Bovine leukemia virus
AID
ACAbB
−0.01
−0.008
−0.022
−0.01
−0.011

144657
GQITVD

MMYG
Homo sapiens
Homo sapiens
OAID
ELISA
144661
GREGYP

ADGGCA

WPACYC
Oryctolagus cuniculus
MD
AID
WI
0.02
0.024
0.013
0.027
0.022

21084
GLQN
Mus musculus
Chlamydia trachomatis
AID
ELISA
21093
GLRAQD

DFSGWDI

NTPAFEW
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
SICSNN

PTCWAIC

KRIPNKK
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
SICSNN

PTCWAIC

KRIPNKK
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
EKERRP

SPIGTATLL
Homo sapiens
MD
OOA
ELISA
0.05
0.048
0.043
0.053
0.05

110857
FTGEAY

SYWSAK
Homo sapiens
Mycoplasma penetrans
EWEIR
ELISA
110859
GEESRIS

LPLPNF

SSLNLRE
Mus musculus
Homo sapiens
AID
FIA
0
0.002
−0.017
0.003
0.003

36315
LGSAYP
Mus musculus
Mycobacterium leprae
MD
ELISA
36317
LGSGAFG

TIYKG
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
WNPAD

YGGIKWN

PADYGGIK
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
HVAPTH

YVTESDA

SQRVTQL
Homo sapiens
Hepatitis C virus
OOID
ELISA
0
−0.002
−0.014
−0.001
0

36162
LGIHE
Oryctolagus cuniculus
Candida albicans
AID
ELISA
36166
LGIMGE

YRGTPRN

QDLYDAA
Mus musculus
Human respiratory  virus
AID
RIA
0
−0.003
−0.007
0
−0.001

67253
TWEVLH
Mus musculus
Plasmodium vivax
AID
ELISA
67257
TWGEN

ETDVLLL

NNTRPPQ
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
QHEQDR

PTPSPAP

SRPFSVL
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
RFSTRY

GNQNGRI

RVLQRFD
Homo sapiens
Arachis hypogaea
EWED
ELISA
0.03
0.028
0.018
0.03
0.03

111036
TESTFT

GEAYSV
Homo sapiens
Mycoplasma penetrans
EWEIR
ELISA
111039
TGVPID

PAVPDSS

IVPLLES
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
IGIIDLIE

KRKFNQ
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
CTDVST

AIHADQL

TPAW
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
PIRALV

GDEVELP

CRISPGK
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
WNPDDY

GGVKWNP

DDYGGVK
Rattus norvegicus
MD
AID
RIA
0
0
−0.009
0
−0.001

131878
FLMLVG

GSTL
Homo sapiens
Homo sapiens
OAID
ACAbB
131879
FLVAHT

RARAPSA

GERARRS
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
VQWMNR

LIAFAFAG

NHVSP
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
VTVRGGL

RILSPDRK
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
TDVRYK

DDMYHFF

CPAIQAQ
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
HHPART

AHYGSLP

QKSHGRT
Homo sapiens
Homo sapiens
AID
ELISA
0
0
0
0.007
0

119581
FSCSVMHE
Homo sapiens
Homo sapiens
OAID
ELISA
119582
GLQLIQL

INVDEVNQI
Mus musculus
Homo sapiens
AID
RIA
−0.01
−0.008
−0.01
−0.003
−0.011

144657
GQITVD

MMYG
Homo sapiens
Homo sapiens
OAID
ELISA
144659
GREGYP

ADGGAA

GYCNTE
Oryctolagus cuniculus
MD
AID
WI
−0.01
−0.006
−0.017
−0.003
−0.008

25013
HVADI

DKLID
Mus musculus
Puumala virus Kazan
AID
ELISA
25022
HVAPTH

YVVESDA

SQRVTQV
Homo sapiens
Hepatitis C virus
OOID
ELISA
0
−0.002
−0.014
−0.001
0

144652
GMRGM

KGLVY
Homo sapiens
Homo sapiens
OAID
ELISA
144654
GPHPTLE

VVPMGRGS
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
IGTLKKIL

DETVKD

KIAKEQ
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
CYGLPDS

EPTKTNGK
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
CYGLPDS

EPTKTNGK
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
DFNGSF

DMNGTITA
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
ATASTM

DHARHGF

LPRHRDT
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
LGGWKLQ

SDPRAYAL
Homo sapiens
Ambrosia artemisiifolia
OOA
RIA
0.01
0.01
0.007
0.013
0.009

115256
FRELKD

LKGY
Homo sapiens
Bos taurus
DEWED
WI
115261
GDLEILL

QKWENG

ECAQKKI
Homo sapiens
Bos taurus
OOA
FIA
−0.01
−0.01
−0.01
0
−0.009

129024
KADQLYK
Homo sapiens
Homo sapiens
OAID
ELISA
129026
KAKKP

AAAAGA

KKAKS
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
YVPIVT

FYSEISM

HSSRAIP
Oryctolagus cuniculus
MD
AID
ELISA
0
0.002
−0.007
0
−0.002

150850
GY
Mus musculus
Human papillomavirus
AID
ELISA
150853
HIGGLSI

LDPIFGVL
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
GDRSGYS

SPGSPG
Mus musculus
Homo sapiens
AID
ACAbB
−0.03
−0.028
−0.03
−0.023
−0.031

114859
ICGTD

GVTYT
Homo sapiens
Gallus gallus
OOA
WI
114865
IVERETR

GQSENPL

WHALRR
Rattus norvegicus
Human herpesvirus
AID
ELISA
0.04
0.042
0.035
0.037
0.038

62149
SVHLF
Homo sapiens
MD
OAID
WI
62150
SVIALGS

QEGALHQ

ALAGAI
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
SICSNNP

TCWAICK

RIPNKK
Mus musculus
Human respiratory  virus
AID
IFAIH
0.04
0.04
0.027
0.04
0.041

61783
STNKAV

VSLS
Bos taurus
Bovine respiratory
AID
ELISA
61791
STNPKPQ

RKTKRNT

NRRPQ
Homo sapiens
Hepatitis C virus
EWEIR
ACAbB
0.04
0.035
0.029
0.037
0.039

100318
NAPKT

FQFIN
Mus musculus
Bacillus anthracis
AID
ELISA
100319
NASSELH

LLGFGIN

AENNHR
Homo sapiens
Arachis hypogaea
EWED
ELISA
0
−0.002
−0.012
0
0

118947
PFSAPPPA
Homo sapiens
Homo sapiens
OAID
ELISA
118948
PGAIEQG

PADDPGE

GPSTGP
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
AALTAEN

TAIKKRN

ADAKA
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
KEDFRY

AISSTNEI

GLLGA
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
IHIPSEKI

WRPDLVLY
Mus musculus
Homo sapiens
AID
RIA
0.01
0.012
0.01
0.017
0.009

58780
SKAANLSIIK
MD
Beet necrotic
MD
WI
58783
SKAFSN

CYPYDVP

DYASL
Oryctolagus cuniculus
Influenza A virus
AID
RIA
−0.01
−0.004
−0.014
−0.009
−0.013

115153
MKGVVC

TRIYEKV
Homo sapiens
Homo sapiens
NI
ELISA
115155
NNQRKK

AKNTPFN

MLKRERN
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
LPPGLHV

FPLASNRS
Mus musculus
MD
AID
SPR
−0.01
−0.013
−0.021
−0.01
−0.01

96215
EEEEAE

DKED
Homo sapiens
Homo sapiens
OAID
ELISA
96216
EEEGLLK

KSADTLW

NMQK
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
LTAELKI

YSVIQAEI

NKHL
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
KGEPGL

PGRGFP

GFP
Mus musculus
Homo sapiens
AID
ACAbB
0
0.002
0
0.007
−0.001

63569
TETVNSDI
Macaca mulatta
Shigella flexneri
AID
ELISA
63573
TEVELKER

KHRIEDAV

RNAK
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
DEDENQS

PRSFQKKTR
Oryctolagus cuniculus
Homo sapiens
AID
ELISA
0.05
0.053
0.05
0.05
0.048

23028
GVKYA
Homo sapiens
MD
OAID
WI
23032
GVLAKDV

RFSQV
Homo sapiens
MD
EWEIR
ACAbB
0
0
0
0.004
−0.003

115293
IMCVKK

ILDK
Homo sapiens
Bos taurus
DEWED
WI
115295
INPSKEN

LCSTFCK

EVVRNA
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
TLTSGSD

LDRCTT

FDDV
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
PLLPGT

STTSTG

PCKT
Homo sapiens
Hepatitis B virus
AID
ELISA
0.02
0.018
0.019
0.02
0.016

142228
LYCYEQ

LNDSSE
Homo sapiens
Human papillomavirus
NI
ELISA
142250
NWGDEP

SKRRDRS

NSRGRKN
Felis catus
Feline infectious
AID
ELISA
0.07
0.068
0.071
0.073
0.07

21549
GNYDFW

YQS
Homo sapiens
Staphylococcus aureus
OOID
ELISA
21553
GNYNYKY

RYLRHGK

LRPFER
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
GNYNYKY

RYLRHGK

LRPFER
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
LAPSTL

RSLRKR

RLSSP
Homo sapiens
Human herpesvirus
OOID
ELISA
0.06
0.06
0.059
0.063
0.06

130454
CLFPNNSYC
Mus musculus
MD
AID
NIAA
130456
CRPQVN

NPKEWS

CAAC
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
GFVSASI

FGFQAEV

GPNNTR
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
GKSKRD

AKNNAAK

LAVDKLL
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
HACQKK

LLKFEAL

QQEEGEE
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
REGVRW

RVMAIQ
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
TAAQITQ

RKWEAA

REAEQRR
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
IAKEQE

NKETIGT

LKKILDE
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
HNKIQA

IELEDLLR

YSKLYR
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
WSSTV

LRVSPT

RTVP
Mus musculus
MD
AID
ELISA
0.01
0.012
0.003
0.017
0.01

78252
PVQNLT
Mus musculus
Porphyromonas gingivalis
AID
WI
78253
QGGCGR

GWAFSA

TGAIEA
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
CCRHKQ

KDVGDVK

QTLPPS
Ovis aries
MD
AID
ELISA
0.01
0.006
0.004
0.01
0.008

53109
RAGVCY
Homo sapiens
Triticum aestivum
OOA
ELISA
53116
RAILTA

FSPAQDI

WGTS
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
KHQGA

QYVWN

RTA
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
VQWMN

RLIAFAF

AGNHVSP
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
DMAQV

TVGPGLL

GVSTL
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.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

The present study was partially supported by Grants AGL2010-22290-C02 and AGL2011-30563-C03 from Ministerio de Ciencia e Innovación, Spain, and Grant CN 2012/155 from Xunta de Galicia, Spain.

References

B. Peters, J. Sidney, P. Bourne et al., “The design and implementation of the immune epitope database and analysis resource,” Immunogenetics, vol. 57, no. 5, pp. 326–336, 2005.

B. Peters, J. Sidney, P. Bourne et al., “The immune epitope database and analysis resource: from vision to blueprint,” PLoS Biology, vol. 3, no. 3, p. e91, 2005.

P. Wang, A. A. Morgan, Q. Zhang, A. Sette, and B. Peters, “Automating document classification for the Immune Epitope Database,” BMC Bioinformatics, vol. 8, article 269, 2007.

A. Sette, “The immune epitope database and analysis resource: from vision to blueprint,” Genome Informatics, vol. 15, no. 2, p. 299, 2004.

N. Salimi, W. Fleri, B. Peters, and A. Sette, “The immune epitope database: a historical retrospective of the first decade,” Immunology, vol. 137, no. 2, pp. 117–123, 2012.

Y. Kim, A. Sette, and B. Peters, “Applications for T-cell epitope queries and tools in the Immune Epitope Database and Analysis Resource,” Journal of Immunological Methods, vol. 374, no. 1-2, pp. 62–69, 2011.

Y. Kim, J. Ponomarenko, Z. Zhu, D. Tamang, et al., “Immune epitope database analysis resource,” Nucleic Acids Research, pp. W525–W530, 2012.

A. M. Helguera, R. D. Combes, M. P. Gonzalez, and M. N. D. S. Cordeiro, “Applications of 2D descriptors in drug design: a DRAGON tale,” Current Topics in Medicinal Chemistry, vol. 8, no. 18, pp. 1628–1655, 2008.

G. M. Casañola-Martín, Y. Marrero-Ponce, M. T. H. Khan et al., “Dragon method for finding novel tyrosinase inhibitors: Biosilico identification and experimental in vitro assays,” The European Journal of Medicinal Chemistry, vol. 42, no. 11-12, pp. 1370–1381, 2007.

I. V. Tetko, J. Gasteiger, R. Todeschini et al., “Virtual computational chemistry laboratory—design and description,” Journal of Computer-Aided Molecular Design, vol. 19, no. 6, pp. 453–463, 2005.

A. R. Katritzky, A. Oliferenko, A. Lomaka, and M. Karelson, “Six-membered cyclic ureas as HIV-1 protease inhibitors: a QSAR study based on CODESSA PRO approach,” Bioorganic and Medicinal Chemistry Letters, vol. 12, no. 23, pp. 3453–3457, 2002.

A. R. Katritzky, S. Perumal, R. Petrukhin, and E. Kleinpeter, “CODESSA-based theoretical QSPR model for hydantoin HPLC-RT lipophilicities,” Journal of Chemical Information and Computer Sciences, vol. 41, no. 3, pp. 569–574, 2001.

S. Vilar, G. Cozza, and S. Moro, “Medicinal chemistry and the molecular operating environment (MOE): application of QSAR and molecular docking to drug discovery,” Current Topics in Medicinal Chemistry, vol. 8, no. 18, pp. 1555–1572, 2008.

G. Marzaro, A. Chilin, A. Guiotto et al., “Using the TOPS-MODE approach to fit multi-target QSAR models for tyrosine kinases inhibitors,” The European Journal of Medicinal Chemistry, vol. 46, no. 6, pp. 2185–2192, 2011.

S. Vilar, E. Estrada, E. Uriarte, L. Santana, and Y. Gutierrez, “In silico studies toward the discovery of new anti-HIV nucleoside compounds through the use of tops-mode and 2D/3D connectivity indices. 2. Purine derivatives,” Journal of Chemical Information and Modeling, vol. 45, no. 2, pp. 502–514, 2005.

E. Estrada, J. A. Quincoces, and G. Patlewicz, “Creating molecular diversity from antioxidants in Brazilian propolis. combination of TOPS-MODE QSAR and virtual structure generation,” Molecular Diversity, vol. 8, no. 1, pp. 21–33, 2004.

E. Estrada and H. González, “What are the limits of applicability for graph theoretic descriptors in QSPR/QSAR? modeling dipole moments of aromatic compounds with TOPS-MODE descriptors,” Journal of Chemical Information and Computer Sciences, vol. 43, no. 1, pp. 75–84, 2003.

Y. Marrero-Ponce, J. A. Castillo-Garit, E. Olazabal et al., “Tomocomd-Cardd, a novel approach for computer-aided “rational” drug design: I. Theoretical and experimental assessment of a promising method for computational screening and in silico design of new anthelmintic compounds,” Journal of Computer-Aided Molecular Design, vol. 18, no. 10, pp. 615–634, 2004.

Y. Marrero Ponce, R. Medina Marrero, E. A. Castro et al., “Protein quadratic indices of the “macromolecular pseudograph's α-carbon atom adjacency matrix”. 1. prediction of arc repressor alanine-mutant's stability,” Molecules, vol. 9, no. 12, pp. 1124–1147, 2004.

H. González-Díaz, F. Prado-Prado, and F. M. Ubeira, “Predicting antimicrobial drugs and targets with the MARCH-INSIDE approach,” Current Topics in Medicinal Chemistry, vol. 8, no. 18, pp. 1676–1690, 2008.

R. García-Domenech, J. Gálvez, J. V. de Julián-Ortiz, and L. Pogliani, “Some new trends in chemical graph theory,” Chemical Reviews, vol. 108, no. 3, pp. 1127–1169, 2008.

E. Estrada and E. Uriarte, “Recent advances on the role of topological indices in drug discovery research,” Current Medicinal Chemistry, vol. 8, no. 13, pp. 1573–1588, 2001.

R. Todeschini and V. Consonni, Handbook of Molecular Descriptors, Wiley-VCH, Weinheim, Germany, 2008.

E. Estrada, E. J. Delgado, J. B. Alderete, and G. A. Jaña, “Quantum-connectivity descriptors in modeling solubility of environmentally important organic compounds,” Journal of Computational Chemistry, vol. 25, no. 14, pp. 1787–1796, 2004.

E. Besalu, X. Girones, L. Amat, and R. Carbo-Dorca, “Molecular quantum similarity and the fundamentals of QSAR,” Accounts of Chemical Research, vol. 35, no. 5, pp. 289–295, 2002.

D. A. Rincón, M. N. D. S. Cordeiro, and R. A. Mosquera, “On the electronic structure of cocaine and its metabolites,” Journal of Physical Chemistry A, vol. 113, no. 50, pp. 13937–13942, 2009.

M. Mandado, M. J. González-Moa, and R. A. Mosquera, “Chemical graph theory and n-center electron delocalization indices: a study on polycyclic aromatic hydrocarbons,” Journal of Computational Chemistry, vol. 28, no. 10, pp. 1625–1633, 2007.

T. Hill and P. Lewicki, STATISTICS: Methods and Applications: A Comprehensive Reference for Science, Industry and Data Mining, StatSoft, Tulsa, Okla, USA, 2006.

E. Frank, M. Hall, L. Trigg, G. Holmes, and I. H. Witten, “Data mining in bioinformatics using Weka,” Bioinformatics, vol. 20, no. 15, pp. 2479–2481, 2004.

G. Patlewicz, N. Ball, E. D. Booth, E. Hulzebos, E. Zvinavashe, and C. Hennes, “Use of category approaches, read-across and (Q)SAR: general considerations,” Regulatory Toxicology and Pharmacology, vol. 67, no. 1, pp. 1–12, 2013.

D. W. Roberts and G. Y. Patlewicz, “Updating the skin sensitization in vitro data assessment paradigm in 2009—a chemistry and QSAR perspective,” Journal of Applied Toxicology, vol. 30, no. 3, pp. 286–288, 2010.

D. W. Roberts and G. Y. Patlewicz, “Nonanimal alternatives for skin sensitization: letter to the editor,” Toxicological Sciences, vol. 106, no. 2, pp. 572–574, 2008.

E. Estrada, G. Patlewicz, and Y. Gutierrez, “From knowledge generation to knowledge archive. a general strategy using TOPS-MODE with DEREK to formulate new alerts for skin sensitization,” Journal of Chemical Information and Computer Sciences, vol. 44, no. 2, pp. 688–698, 2004.

G. F. Gerberick, C. A. Ryan, P. S. Kern et al., “A chemical dataset for evaluation of alternative approaches to skin-sensitization testing,” Contact Dermatitis, vol. 50, no. 5, pp. 274–288, 2004.

G. Y. Patlewicz, D. A. Basketter, C. K. Smith Pease et al., “Further evaluation of quantitative structure–activity relationship models for the prediction of the skin sensitization potency of selected fragrance allergens,” Contact Dermatitis, vol. 50, no. 2, pp. 91–97, 2004.

E. Estrada, G. Patlewicz, M. Chamberlain, D. Basketter, and S. Larbey, “Computer-aided knowledge generation for understanding skin sensitization mechanisms: the TOPS-MODE approach,” Chemical Research in Toxicology, vol. 16, no. 10, pp. 1226–1235, 2003.

G. Patlewicz, R. Rodford, and J. D. Walker, “Quantitative structure-activity relationships for predicting skin and eye irritation,” Environmental Toxicology and Chemistry, vol. 22, no. 8, pp. 1862–1869, 2003.

G. Y. Patlewicz, R. A. Rodford, G. Ellis, and M. D. Barratt, “A QSAR model for the eye irritation of cationic surfactants,” Toxicology In Vitro, vol. 14, no. 1, pp. 79–84, 2000.

E. Tenorio-Borroto, C. G. Penuelas Rivas, J. C. Vasquez Chagoyan, N. Castanedo, F. J. Prado-Prado, and X. Garcia-Mera, “ANN multiplexing model of drugs effect on macrophages; theoretical and flow cytometry study on the cytotoxicity of the anti-microbial drug G1 in spleen,” Bioorganic & Medicinal Chemistry, vol. 20, no. 20, pp. 6181–6194, 2012.

D. R. Flower, H. McSparron, M. J. Blythe et al., “Computational vaccinology: quantitative approaches,” Novartis Foundation Symposium, vol. 254, pp. 102–120, 2003.

I. A. Doytchinova, P. Guan, and D. R. Flower, “Quantitative structure-activity relationships and the prediction of MHC supermotifs,” Methods, vol. 34, no. 4, pp. 444–453, 2004.

Y. Xiao and M. R. Segal, “Prediction of genomewide conserved epitope profiles of HIV-1: classifier choice and peptide representation,” Statistical Applications in Genetics and Molecular Biology, vol. 4, no. 1, article 25, 2005.

T. Fagerberg, V. Zoete, S. Viatte, P. Baumgaertner, P. M. Alves, P. Romero, et al., “Prediction of cross-recognition of peptide-HLA A2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationships,” PLoS ONE, vol. 8, no. 7, Article ID e65590, 2013.

D. Barh, A. N. Misra, A. Kumar, and A. Vasco, “A novel strategy of epitope design in Neisseria gonorrhoeae,” Bioinformation, vol. 5, no. 2, pp. 77–82, 2010.

J. Bi, R. Song, H. Yang et al., “Stepwise identification of HLA-A*0201-restricted CD8⁺ T-cell epitope peptides from herpes simplex virus type 1 genome boosted by a steprank scheme,” Biopolymers, vol. 96, no. 3, pp. 328–339, 2011.

R. D. Bremel and E. J. Homan, “An integrated approach to epitope analysis II: a system for proteomic-scale prediction of immunological characteristics,” Immunome Research, vol. 6, no. 1, article 8, 2010.

C. M. Diez-Rivero, B. Chenlo, P. Zuluaga, and P. A. Reche, “Quantitative modeling of peptide binding to TAP using support vector machine,” Proteins, vol. 78, no. 1, pp. 63–72, 2010.

E. Martínez-Naves, E. M. Lafuente, and P. A. Reche, “Recognition of the ligand-type specificity of classical and non-classical MHC I proteins,” FEBS Letters, vol. 585, no. 21, pp. 3478–3484, 2011.

M. Bhasin, E. L. Reinherz, and P. A. Reche, “Recognition and classification of histones using support vector machine,” Journal of Computational Biology, vol. 13, no. 1, pp. 102–112, 2006.

P. A. Reche and E. L. Reinherz, “PEPVAC: a web server for multi-epitope vaccine development based on the prediction of supertypic MHC ligands,” Nucleic Acids Research, vol. 33, no. 2, pp. W138–W142, 2005.

E. M. Lafuente and P. A. Reche, “Prediction of MHC-peptide binding: a systematic and comprehensive overview,” Current Pharmaceutical Design, vol. 15, no. 28, pp. 3209–3220, 2009.

A. Gaulton, L. J. Bellis, A. P. Bento et al., et al., “A large-scale bioactivity database for drug discovery,” Nucleic Acids Research, pp. D1100–D1107, 2012.

W. H. Cropper, Great Physicists: The Life and Times of Leading Physicists from Galileo to Hawking, Oxford University Press, New York, NY, USA, 2004.

E. L. Bouzarth, A. Brooks, R. Camassa et al., “Epicyclic orbits in a viscous fluid about a precessing rod: theory and experiments at the micro-and macro-scales,” Physical Review E, vol. 76, no. 1, part 2, Article ID 016313, 2007.

M. Laberge, “Intrinsic protein electric fields: basic non-covalent interactions and relationship to protein-induced Stark effects,” Biochimica et Biophysica Acta, vol. 1386, no. 2, pp. 305–330, 1998.

F. M. Fernandez and J. A. Morales, “Perturbation theory without wave functions for the Zeeman effect in hydrogen,” Physical Review A, vol. 46, no. 1, pp. 318–326, 1992.

B. D. Marshall and W. G. Chapman, “Thermodynamic perturbation theory for associating fluids with small bond angles: effects of steric hindrance, ring formation, and double bonding,” Physical Reiew E, vol. 87, no. 5, Article ID 052307, 12 pages, 2013.

H. Gonzalez-Diaz, S. Arrasate, A. Gomez-San Juan, et al., “New theory for multiple input-output perturbations in complex molecular systems. 1. Linear QSPR electronegativity models in physical, organic, and medicinal chemistry,” Current Topics in Medicinal Chemistry, 2013.

H. Gonzalez-Diaz, S. Arrasate, N. Sotomayor, et al., “MIANN models in medicinal, physical and organic chemistry,” Current Topics in Medicinal Chemistry, vol. 13, no. 5, pp. 619–641, 2013.

G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control, Holden-Day, San Francisco, Calif, USA, 1970.

A. Speck-Planche, V. V. Kleandrova, F. Luan, and M. N. Cordeiro, “Unified multi-target approach for the rational in silico design of anti-bladder cancer agents,” Anti-Cancer Agents in Medicial Chemistry, vol. 13, no. 5, pp. 791–800, 2013.

A. Speck-Planche, V. V. Kleandrova, and M. N. Cordeiro, “New insights toward the discovery of antibacterial agents: multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugs,” The European Journal of Pharmaceutical Sciences, vol. 48, no. 4-5, pp. 812–818, 2013.

A. Speck-Planche, V. V. Kleandrova, F. Luan, and M. N. Cordeiro, “Multi-target inhibitors for proteins associated with Alzheimer: in silico discovery using fragment-based descriptors,” Current Alzheimer Research, vol. 10, no. 2, pp. 117–124, 2013.

A. Speck-Planche, V. V. Kleandrova, F. Luan, and M. N. Cordeiro, “In silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosis,” Combinatorial Chemistry & High Throughput Screening, vol. 15, no. 8, pp. 666–673, 2012.

A. Speck-Planche, V. V. Kleandrova, F. Luan, and M. N. D. S. Cordeiro, “Chemoinformatics in anti-cancer chemotherapy: multi-target QSAR model for the in silico discovery of anti-breast cancer agents,” The European Journal of Pharmaceutical Sciences, vol. 47, no. 1, pp. 273–279, 2012.