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Predicting Allergen Cross Reactionsby Protein Sequence
Graduate School for Cellular and Biomedical Sciences
University of Bern
MD-PhD Thesis
Submitted by
Pascal Bruno Pfiffnerfrom Mels SG and Mels-Weisstannen SG
Thesis advisor
Prof. Dr. Beda M StadlerUniversity Institute of Immunology
Medical Faculty of the University of Bern
Original document saved on the web server of the University
Library of Bern
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Accepted by the Faculty of Medicine, the Faculty of Science and
the
Vetsuisse Faculty of the University of Bern at the request of
the Graduate
School for Cellular and Biomedical Sciences
Bern, Dean of the Faculty of Medicine
Bern, Dean of the Faculty of Science
Bern, Dean of the Vetsuisse Faculty Bern
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Contents
1 Abstract 6
2 Abbreviations 7
3 Scientific Overview 93.1 Allergen Cross-Reactions . . . . . .
. . . . . . . . . . . . . . . . . . 9
3.1.1 Allergy . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . 93.1.2 The Context of Cross-Reactions . . . . . . . . . .
. . . . . . 93.1.3 The Molecular Basis . . . . . . . . . . . . . .
. . . . . . . . 10
3.2 Allergenicity Testing . . . . . . . . . . . . . . . . . . .
. . . . . . . 143.2.1 From Skin Testing to Laboratory Analysis . .
. . . . . . . . 143.2.2 Quantifying Cross-Reactivity . . . . . . .
. . . . . . . . . . 153.2.3 Allergy Array Test System . . . . . . .
. . . . . . . . . . . . 16
3.3 Allergenicity Prediction . . . . . . . . . . . . . . . . . .
. . . . . . . 163.3.1 Necessity . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 163.3.2 Epitope Focused Prediction . . . . . .
. . . . . . . . . . . . 173.3.3 Structure-Sequence Relationship . .
. . . . . . . . . . . . . . 183.3.4 Identifying Conserved Domains .
. . . . . . . . . . . . . . . 183.3.5 Motifs and General Profiles .
. . . . . . . . . . . . . . . . . 20
3.4 Bioinformatics of Cross Reactions . . . . . . . . . . . . .
. . . . . . 213.4.1 Motif Calculation . . . . . . . . . . . . . . .
. . . . . . . . . 213.4.2 Web Interface . . . . . . . . . . . . . .
. . . . . . . . . . . . 22
3.5 Outlook: Protein Surface Comparison . . . . . . . . . . . .
. . . . . 223.5.1 Ab initio Protein Folding Prediction . . . . . .
. . . . . . . 223.5.2 Homology Modeling . . . . . . . . . . . . . .
. . . . . . . . 243.5.3 Prediction of Similar Surfaces . . . . . .
. . . . . . . . . . . 25
4 Results – Dissertation Equivalents 354.1 Dissertation
Equivalent I . . . . . . . . . . . . . . . . . . . . . . . . 364.2
Dissertation Equivalent II . . . . . . . . . . . . . . . . . . . .
. . . 45
5 Acknowledgements 64
4
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A Declaration of Originality 65
List of Figures
3.1 Structural alignment of Bet v 1 and Mal d 1 . . . . . . . .
. . . . . 123.2 Structures and folds newly added to the PDB . . . .
. . . . . . . . 193.3 Iterative motif discovery . . . . . . . . . .
. . . . . . . . . . . . . . 213.4 Web-based front-end . . . . . . .
. . . . . . . . . . . . . . . . . . . 23
List of Tables
3.1 Allergens homologous to pathogenesis related proteins . . .
. . . . . 13
LIST OF TABLES 5
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1Abstract
Clinically important allergen cross-reactions such as the
pollen-food syndromeshave been shown to originate from structural
homology. Additionally, in the lasttwo years no new protein folds
were discovered, implying that the universe ofunique protein folds
may be almost complete. For allergy this may suggest that theimmune
system also reacts adversely in a predictable way, namely by
recognizinghomologous proteins once a sensitization is established.
Thus we have appliedsequence-based computational homology
prediction to assess the extent of cross-reactivity.
In a first paper we have analyzed more than 5’000 serum samples,
each testedfor specific IgE against multiple allergen extracts
(ImmunoCAP R©). We foundthe degree of cross-reactivity to be
astonishingly high. However, as specific IgEdeterminations were
based on crude allergen extracts, we were unable to concludethat
the observed cross-reactivity reliably depends on the allergen
sequence.
Thus in a second paper we utilized data obtained for specific
IgE directed againsthighly purified natural or recombinant proteins
on a new allergen chip system(ISAC R©). Thereby we assessed the
sensitization pattern against 105 proteins inmore than 3’000 serum
samples. Protein pairs predicted to cross-react, basedon
computationally identified homology, co-reacted significantly more
often thanprotein pairs without apparent homology. Additionally, we
demonstrated thatthe allergen source, and therefore
co-sensitization, was much less important thanprotein homology.
We conclude that cross-reactivity is an important mechanism in
the developmentof allergic diseases, more important than generally
accepted. Allergy diagnosis andtreatment may benefit from the
combination of allergen chip data, i.e. specific IgEvalues directed
against purified and recombinant proteins, and
computationallypredicted cross-reactivity. Finally, our continuous
endeavor to assess the numberof structural motifs in allergens
shows that not only protein folds, but also thenumber of allergen
motifs may soon reach a plateau. Hence allergenicity predictionmay
become as valid as wet lab testing for new and potential
allergens.
6
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2Abbreviations
APC Antigen Presenting Cell
Bet v 1 Betula verrucosa 1, major birch pollen allergen
CASP critical Assessment of Techniques for Protein Structure
Prediction
CCD Cross-reacting Carbohydrate Determinant
CDR Complementary Determining Region
CRD Component-Resolved Diagnostics
EVD Extreme Value Distribution
FAO Food and Agriculture Organization of the United Nations
FEIA Fluoro-Enzyme Immuno Assay
GM Genetically Modified
IDT Intradermal Dilutional Testing
IgE Immunoglobulin E
ISAC Immuno Solid-phase Allergen Chip
Mal d 1 Malus domesticus 1, major apple allergen
NMR Nuclear Magnetic Resonance
PDB Protein Data Bank
PR Pathogenesis-Related Protein
PSSM Position-Specific Scoring Matrix
PTM Post-Translational Modification
RAST Radio Allergo-Sorbent Tests
rmsd Root Mean Square Deviation
7
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SET Skin End Point Titration
SPT Skin Prick Test
TCR T cell receptor
WHO World Health Organization
2. Abbreviations 8
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3Scientific Overview
3.1 Allergen Cross-Reactions
3.1.1 Allergy
Allergy is a hypersensitivity type I disorder. It is
characterized by typical clinicalreactions such as hay fever,
asthma, food allergies, eczema and urticaria againstusually
harmless substances. These manifestations are mostly mediated by
Im-munoglobulin E (IgE) antibodies, highly specific binding
proteins produced byplasma cells. By recognizing a certain pattern
on the surface of their antigen, theepitope, antibodies elicit
various immune reactions against these molecules. Anti-bodies of
the IgE class are also expressed in high quantities during
infections withhelminths (Erb, 2007), but are the main culprit in
allergic sensitizations leadingto type I hypersensitivity (Gould et
al., 2003).
The allergic reaction Antibodies of the IgE subclass are
required for type Ihypersensitivity reactions (Kay, 2000). Symptoms
occurring during an allergicresponse, like swelling and itching,
are a result of mast cell and basophil degran-ulation. Mast cells
and basophils carry Fc� receptors on their surface which areable to
bind to the Fc portion of IgE antibodies. Cross-linking
surface-bound IgEon high-affinity Fc� type I receptors triggers
degranulation (Helm et al., 1988).The cells release mediators such
as histamine and serotonine into the surroundingtissue (Metzger,
1991; Nadler et al., 2000), which causes the symptoms typical
forallergy.
3.1.2 The Context of Cross-Reactions
The phenomenon of cross-reactivity has long been known. Reports
from thenineteen-thirties mention evolutionary relationship as
possible explanations for ob-
9
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served cross-reactivity, but state that “mere similarity would
be sufficient” (Hookerand Boyd, 1934). After publications revealed
that any form of gelatin, essen-tially a denatured protein fragment
without fixed 3-D structure, was antigenic inman (Maurer, 1954),
Gell and Benaceraf presumed that “any specificity whichit [the
protein] has must therefore be resident in the amino-acid sequence
of theprotein chain” (Gell and Benacerraf, 1959). In a series of
publications, they as-sessed various aspects of cross-reactivity
against native and denatured proteins.Interestingly, they found
delayed type hypersensitivity skin reactions in guineapigs
sensitized to native ovalbumin when challenged with denatured
ovalbumin,and vice versa (Gell and Benacerraf, 1959). Still, the
denatured proteins wereunable to elicit immediate type
hypersensitivity reactions, meaning the antibod-ies recognizing the
native protein were unable to recognize linear parts thereof.This
suggested that structural epitopes are more important for antigen
recogni-tion by antibodies than sequence features alone. The two
even considered differentparts of the proteins to be independently
antigenic, coining the term “antigenicmotifs” (Benacerraf and Gell,
1959).
Allergic patients commonly react to more than a single allergen,
true single positivesensitizations are very rare (cf. dissertation
equivalent I). In which proportionthis observation is caused by
multiple sensitizations and in which proportion bycross-reactions
may be disputed. On one hand, a TH2 response to an allergen inthe
TH2-tilted milieu of allergic patients facilitates the
sensitization against con-currently present proteins, for example
different proteins in a pollen grain. Inprinciple, T helper cells
will also stimulate B cells that are reactive to a
non-cross-reactive epitope. On the other hand it can be
demonstrated that co-sensitizationbetween proteins occurring in the
same material is astonishingly low (cf. disser-tation equivalent
II).
Cross-reactivity may not be sufficiently explained by predicting
IgE cross-reactivityalone. Factors other than the protein itself,
most importantly the type and time ofexposure, are also crucial to
sensitization (Ferreira et al., 2004). Yet these factorscan neither
be influenced nor measured for diagnostic purposes. Thus the
questionremains what diagnostic and therapeutic value IgE
cross-reactivity prediction canadd to the current allergy
evaluation procedure.
3.1.3 The Molecular Basis
The immune system recognizes allergen antigens in two different
forms:
• Antigen presenting cells (APCs) present peptide fragments of
the digestedantigens on MHC class II molecules cells as linear
structures. T helper cellsrequire this form of antigen presentation
in order to elicit effector functions.
• Antibodies and therewith B cell receptors recognize
conformational epitopeson the tertiary structure of the folded
antigen.
3. Scientific Overview 10
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Ultimately, the activation of B cells resulting in antibody
production requires bothforms of recognition, as most allergens are
proteins and thus thymus-dependentantigens. There is no B cell
activation without help of T helper cells. For cross-reactivity the
question arises which presentation leads to the generation of a
cross-reactive immune response.
T cell cross-reactivity Peptides presented on MHC class II
molecules are usu-ally 10 - 25 amino acids long. Of this fragment,
only a limited number of residues isdirectly interfaced by the T
cell receptor (TCR) (Wucherpfennig and Strominger,1995). By
contrast, a high affinity antibody can form around fifteen to
twentybonds with its antigen (Davies et al., 1988; Lafont et al.,
2007). Thus the T cellis, already for statistical reasons, more
likely to encounter indistinguishable struc-tures originating from
different proteins when compared to the B cell. It can
bedemonstrated that some TCRs recognize not only a single peptide,
but rather alimited repertoire of related peptides, derived from
different antigens. This cross-recognition leads to efficient
activation of the T cell, for example in the setting ofautoimmunity
linked to viral antigens such as Multiple Sclerosis
(Wucherpfennigand Strominger, 1995). Additionally, during the
physiological process of positiveselection in the thymus,
cross-reactivity at the T cell level is a common phe-nomenon. T
cells with low affinity for self-MHC molecules are kept alive
(positiveselection) while T cells with higher affinity for the
self-MHC/self-peptide complexare deleted (negative selection)
(Kappler et al., 1987; Kisielow et al., 1988).These findings
demonstrate that cross-reactivity at the T cell level is common.
Anactivated T helper cell potentially activates B cells presenting
a range of differentpeptides, hence T helper cells do not limit
allergen cross-reactivity.
Cross-reactive antibodies Cross-reactive antibodies have the
ability to bindan antigen different from its immunogen. Given the
high specificity antibodiesachieve during affinity maturation, the
existence of antibodies recognizing struc-tures different from
their template structure seems surprising. A way out of thiscatch
is looking at antigen specificity as a quantitative rather than
qualitativeconcept. Antigen-antibody interactions are based on
physicochemical processesdependent on spatial and electrostatic
properties of both molecules’ surfaces. Inthis context, a perfect
match in the sense of spatially and electrostatically per-fectly
complementary molecules, reminiscent of the key-lock analogue,
would re-sult in maximum affine binding. However, less-than-perfect
surface pairs wouldstill be able to bind, admittedly with lower
affinity. An antibody may thereforebe expected to bind various
related and unrelated molecules given a high enoughsimilarity. The
quality of such an interaction would only differ quantitatively
(punintended) in thermodynamic properties, such as interaction
rates or dissociationconstants. This mechanism is commonly termed
“molecular mimicry”. Thus, fromthis stereochemical standpoint,
molecular mimicry and therewith structural simi-larity between
proteins build the foundation for cross-reactivity on the level of
the
3. Scientific Overview 11
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antibody.
The need for structural similarities for cross-reactivity at the
antibody level is notonly of theoretical nature but can easily be
demonstrated. Structural similarityas a consequence of phylogenetic
inheritance correlates well with observed cross-reactivity. When
examining clinically well known cross-reactions such as the
apple-birch cross-reactivity, their major allergenic proteins Bet v
1 and Mal d 1 exhibitpotential epitopes for cross-reactive
antibodies as identified by crystal structureand sequence analysis
(Holm et al., 2001). Figure 3.1 demonstrates the
similaritiesbetween the backbones of Bet v 11 and Mal d 12. Further
examples are highlyconserved proteins such as taxonomically related
group I grass pollen allergens,which demonstrate a high degree of
cross-reactivity (Laffer et al., 1994, 1996).Thus also
experimentally, phylogenetic relationship and with it conserved
proteindomains exhibiting structural similarity are a main cause
for cross-reactivity.
Figure 3.1: Cartoon models of a structural alignment of Bet v 1
(purple, PDB accession number1B6F) and Mal d 1 (green, modeled).
Mal d 1 has been modeled after Pru av 1 (PDB accessionnumber 1E09).
Orientation in view B is perpendicular to the Y-axis of view A.
Profilins and pathogenesis related proteins Following the early
investiga-tions in the apple-birch cross-reactivity, it became
clear that even much more dis-tantly related proteins were capable
of eliciting cross-reactions. For the apple-birchcross-reactivity,
the proline binding protein, profilin, was identified as causative
al-lergen (Valenta et al., 1992). This protein turned out to be the
most importantcross-reacting allergen discovered thus far. The
profilin family constitutes a typeof pan-allergens sharing IgE
epitopes present in most cells of eukaryotic organ-isms (Valenta et
al., 1992). Nowadays, there are many well-known
cross-reactionswhich can be allotted to omnipresent, evolutionary
conserved pan-allergens. Theseinclude profilins, α-Amylase
inhibitors, peroxidases, thiol-proteases, seed storageproteins and
lectins (Breiteneder and Ebner, 2000).
1PDB Model 1B6F:
http://www.pdb.org/pdb/explore/explore.do?structureId=1e092Protein
model based on template 1E09 chain A:
http://www.proteinmodelportal.org/
?pid=modelDetail&pmpuid=1000000075750
3. Scientific Overview 12
http://www.pdb.org/pdb/explore/explore.do?structureId=1e09http://www.proteinmodelportal.org/?pid=modelDetail&pmpuid=1000000075750http://www.proteinmodelportal.org/?pid=modelDetail&pmpuid=1000000075750
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Additionally to profilins, a high homology between
pathogenesis-related proteins(PRs) of different plant species has
been found (Midoro-Horiuti et al., 2001). Theseare plant proteins
induced by viral infections of the plant and various other
en-vironmental stresses (Surplus et al., 1998). The high homology
between PRsexplains the high frequency of cross-reactivity among
plant allergens. Examplesof allergens homologous to PRs can be
found in table 3.1.
PR Classification Example allergens
PR-2 β-1,3-Glucanases Banana, latex, potato, tomatoPR-3 Basic
chitinases Avocado, banana, chestnut, latexPR-4 Win-like proteins
Elderberry, turnipPR-5 Thaumatin-like proteins Apple, bell pepper,
cherry, kiwi,
mountain cedarPR-10 Bet v 1 homologs Apple, apricot, carrot,
celery,
cherry, parsley, pear, potatoPR-14 Lipid transfer proteis Apple,
barley, peach, soybean
Table 3.1: Examples of allergens homologous to pathogenesis
related proteins
Hydrophobic Stickiness It has been suggested that antibodies may
be cross-reactive due to hydrophobic stickiness, a nonspecific
hydrophobic interaction (Pad-lan, 1994). Additionally, antibodies
have been demonstrated to bind a range ofantigens directly related
to their hydrophobicity (Barbas et al., 1997). However,such
nonspecific binding can not explain the high specificity with which
antibod-ies are known to interact with their antigen. Furthermore,
no correlation betweenhydrophobicity and affinity has been found in
recent studies (James and Tawfik,2003), thus hydrophobic stickiness
may contribute to cross-reactions, but is nottheir basis.
Post-Translational Modification An aspect easily forgotten is
that transla-tion is not the final step in the formation of a
protein from DNA. Post-translationalmodifications (PTM) are
processes not entirely defined by the DNA sequence, butinstead
determined by factors of the host. A broad range of PTMs has
beendescribed. For example, these mechanisms are able to add
functional groups oreven entire proteins by gamma-carboxylation,
change the chemical nature of aminoacids by citrullination or
induce structural changes, most notably by disulfide
bondformation.
A PTM leading to an extraordinary broad cross-reactivity through
anti-carbo-hydrate responses is glycosylation, creating
cross-reacting carbohydrate determi-nants (CCDs) on proteins from
different sources. CCDs are common in plantallergens (pollen as
well as food) and in Hymenoptera venoms. Even though aclinical
effect of CCDs has been suggested (Fötisch et al., 1999), it is
the general
3. Scientific Overview 13
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opinion that CCDs are not clinically relevant but must be
considered when inter-preting in-vitro specific IgE assays,
especially in pollen- and Hymenoptera venomsensitivity (Aalberse et
al., 1981; Mari et al., 1999; Erzen et al., 2009). The clini-cal
insignificance mostly stems from an inability to trigger mast cells
or basophilsthrough receptor cross-linking. CCD structures are
monoglycosylated as a conse-quence of their small size, and thus
only represent monovalent epitopes (Viethset al., 2002), unable to
establish the cross-linking.
Cross-reactions require homology It seems clear that there is
one main rea-son for cross-reactivity: Homology. There is no
relevant cross-reactivity withoutstructural similarity (Aalberse et
al., 2001). Cross-reactions without sequencesimilarity so far have
only been demonstrated between anti-idiotypic antibodies.These
antibodies do not have any similarity in the amino-acid sequences
encodingtheir complementary determining region (CDR) (Lescar et
al., 1995).With the exception of these antibodies, cross-reactive
allergens without apparenthomology have not been demonstrated so
far (Aalberse, 2005).
3.2 Allergenicity Testing
3.2.1 From Skin Testing to Laboratory Analysis
Skin testing Since its inception by Blackley in 1873 (Blackley,
1873), skin pricktests (SPT) are still the most widely used
clinical tests to assess sensitizationagainst a substance of
interest (Neto and Rosário, 2009). Hypersensitivity typeI
reactions can be provoked by pricking or injecting a minute amount
of allergenintradermally, usually to a patient’s forearm. Comparing
the size of the whealinduced by an allergen to the size of a
control wheal (usually provoked by a salinesolution) allows to
diagnose whether a patient is sensitized against the allergen,to a
certain degree even the strength of the reaction. This allows for
quick andreliable testing as it has a good negative predictive
value (Sicherer and Sampson,2010). However, SPTs are impractical to
assess the full range of sensitizations dueto the number of pricks
a patient would have to endure.
To not only demonstrate the presence of sensitization but also
to quantitate itsstrength, intradermal dilutional testing (IDT) can
be performed by applying var-ious dilutions of the antigen. As one
early form of IDT, skin end point titration(SET) is widely used in
the diagnosis and treatment of inhalant allergens. Itsefficacy in
guiding desensitization immunotherapy however is only little
supportedby controlled experimental data (Krouse and Mabry, 2003),
despite clinical ex-pertise having shown its usefulness and
effectiveness. SET is commonly used tofind a safe starting dose for
immunotherapy. In the case of food allergies, SET isstill
investigatory and is not typically used in the clinical setting,
however holdspromise to become a realistic diagnostic choice
(Tripodi et al., 2009).
3. Scientific Overview 14
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In vitro The presence of IgE antibodies specific against an
allergen is necessarybut not sufficient to provoke allergic
responses. In other words, sensitization doesnot necessarily imply
clinical allergy. This, however, is a topic beyond the scopeof this
thesis. Nevertheless, testing patients for the presence of specific
IgE is animportant part of every thorough allergic assessment.
Today, the fluoro-enzyme immuno assay (FEIA) is the most widely
used specificIgE detection method. It has replaced the previously
used radio allergo-sorbenttests (RAST). After adding a patient’s
serum sample to the test capsule, specificIgE present in the serum
binds to covalently coupled allergen preparations. Ananti-human IgE
antibody mixture, fluorescently labelled, is then added and
theresulting fluorescence is measured in a spectrophotometer
(ImmunoCAP R© system,Phadia AB, Uppsala, Sweden). IgE quantities
are expressed in kilo-units of antigenper liter (kUA/L), where 1
unit corresponds to 2.4 ng of IgE (Pastorello et al.,1995).
The allergen preparations used in these assays are mixtures of
proteins which areprepared from biological extracts and are known
to be heterogeneous, often alsocontaining non-allergenic proteins
(Chapman et al., 2000). They can even be con-taminated with
allergens from different sources. For these reasons, the use
ofhighly purified natural or even recombinant allergen proteins has
been promoted.Recombinant allergen proteins are an attractive
choice because their pure form pro-motes reproducibility and
standardization (Hamilton, 2010) and allows to exactlydetermine
against which proteins a patient is sensitized. The latest
microarraychip technology utilizing recombinant proteins will be
discussed in more detail insection 3.2.3.
3.2.2 Quantifying Cross-Reactivity
In a first study (Dissertation Equivalent I) we have utilized a
large database ofspecific IgE values obtained by FEIA (ImmunoCAP
R©) to evaluate the degree ofsensitization against various
allergens and their relationship to cross-reactivity. Wefound that
allergen cross-reactions might be much more common than
generallyassumed. For some extracts we found that well over 80% of
the patients testedpositive were also tested positive against
extracts presumably cross-reacting withthe original extract.
Furthermore, with an increasing number of extracts tested,the
percentage of sera sensitized against only one single allergen
extract decreasedfrom approximately 10% for sera tested against 10
to 20 extracts to 1.6% for seratested against at least 90 extracts.
This suggests that the true number of singlepositive sera must be
low and therefore the rate of co-sensitization and presumablythe
rate of cross-reactivity must be high. We concluded that using
allergen extractsfor cross-reactivity assessment might introduce a
certain bias as an extract containsa number of allergenic and
non-allergenic proteins. Therefore, an assessment atthe protein
level using recombinant proteins would be desirable.
3. Scientific Overview 15
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3.2.3 Allergy Array Test System
The ability to clone and purify single proteins has recently
opened the door tocomponent-resolved diagnostics (CRD) (Valenta et
al., 1999; van Hage-Hamstenand Pauli, 2004). CRD has been
commercialized in the form of a microarray chip,the Immuno
Solid-phase Allergen Chip (ISAC R©) (Hiller et al., 2002).
Firstly,CRD allows to identify the disease-eliciting protein and
not only the extract po-tentially containing many different
proteins. Secondly, CRD in the form of theISAC system allows to
determine sensitivity against a broad panel of allergensin a single
measurement. Currently available chips contain 103 different
purifiedallergen molecules. Plans to further extend this number are
made in pursuit ofoffering an allergen screening test covering the
widest range of allergens possibleand necessary.
By testing a patient’s serum against 103 purified proteins, the
sensitization pat-tern exhibited allows to further study the
relationship between co-sensitizationand cross-reaction patterns.
In our second study (Dissertation Equivalent II) weanalyzed the
sensitization pattern of 3’142 patients, determined by ISAC
eval-uations. The focus of our analysis lied with the relationship
between predictedcross-reactions and observed co-reactions, as
described in section 3.3. We founda high correlation between
predicted and observed reactions, which further vali-dates the use
of probabilistic sequence motifs for allergenicity prediction of
newproteins.
3.3 Allergenicity Prediction
3.3.1 Necessity
Risk in biotechnology Allergenicity is one of the most
frequently asked ques-tions in connection with the safety of
genetically modified (GM) foods (FAO andWHO, 2001). Consequently,
allergenicity assessment of GM foods is one of themost important
parts of risk assessment in biotechnology, in line with
evaluationof direct toxicological and nutritional effects. The
importance of the allergenicityaspect has especially become clear
after the inadvertent generation of an allergenicsoy plant by
transfer of a brazil-nut allergen (Nordlee et al., 1996). As a
result ofthis assessment, development of said soy plant was
abandoned and the organismwas never introduced to the food
chain.
Naturally, the amino-acid sequences of GM foods are known. Hence
the mostobvious choice to assess potential allergenicity is by
sequence comparison to knownallergens. Significant similarity
between transgenic and known allergen sequencewould predict the
transgene to be allergenic itself or to cross-react with
knownallergens. The question arises what constitutes a “significant
similarity”.
3. Scientific Overview 16
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A Joint FAO (Food and Agriculture Organization of the United
Nations) /WHO(World Health Organization) Expert Consultation on
Foods Derived from Biotech-nology devised guidelines for
allergenicity evaluation. According to these guide-lines, a novel
protein is regarded allergenic if:
a) it has an identity of at least six contiguous amino acids
or
b) more than 35% sequence similarity over a window of 80 amino
acids
compared to any known allergen3. However, this method proved to
be of low preci-sion, predicting more than 40% of human proteins as
allergens (Stadler and Stadler,2003). In the same study, a new
sequence based method has been proposed, tobe introduced in section
3.4.1. Various alternative allergenicity prediction meth-ods
utilizing different statistical models have been published in the
years after (Liet al., 2004; Riaz et al., 2005; Thomas et al.,
2005; Saha and Raghava, 2006; Zhanget al., 2006; Kong et al., 2007;
Cui et al., 2007; Schein et al., 2007; Barrio et al.,2007; Tong and
Tammi, 2008; Lim et al., 2008; Muh et al., 2009; Ivanciuc et
al.,2009).
Significance in standard allergy assessment Cross-reactivity has
also sev-eral implications in clinical allergy assessment. Not only
are cross-reactions in vitropotentially creating clinically
non-significant results, as for example with CCDs,but determining
the original sensitizing agent may be complicated due to
truecross-reactions. Identification of the primary sensitizing
allergen however is likelyto be relevant, because desensitization
against the “true sensitizer” may cover thewidest spectrum of
specificities (Aalberse et al., 2001) and is likely to relieve
symp-toms provoked by other allergens as well (Asero, 1998).
Additionally, the abilityto predict potential cross-reactions may
alleviate the need for multiple allergentests. These predictions
would probably comprise a broader range of allergensthan direct
testing and would therefore also caution the patient about
unforeseenallergic (cross-)reactions. Thus, allergen
cross-reactivity prediction would consti-tute a valuable asset in
clinical allergy diagnosis.
3.3.2 Epitope Focused Prediction
As mentioned above, T cell epitopes possess a certain potential
in inducing cross-reactions. However, cross-reactivity at the T
cell level is abundant and thereforenot the limiting step in
cross-reactivity induction. The key point whether cross-reactivity
occurs rather lies at the level of the antibody, therefore
cross-reactivityprediction efforts should commence at the antibody
level.
Cross-reacting antibodies are able to recognize epitopes on
different proteins, giventhat these epitopes are stereochemically
similar enough. As we have seen, the sin-
3Full report:
http://www.who.int/foodsafety/publications/biotech/en/ec_jan2001.pdf
3. Scientific Overview 17
http://www.who.int/foodsafety/publications/biotech/en/ec_jan2001.pdfhttp://www.who.int/foodsafety/publications/biotech/en/ec_jan2001.pdf
-
gle most important cause for two proteins exhibiting similar
epitopes is homologybased on evolutionary conservation. The
prediction of homologous structures fromthe sequence level is a
desirable goal because protein sequence data is widely avail-able.
Thus the question arises whether homologous structures might be
detectableat the sequence level.
3.3.3 Structure-Sequence Relationship
When Pascarella and Argos in 1991 grouped protein structures
with similar main-chain fold, they found that proteins in the same
group exhibited strong sequenceand functional similarities. Not
only did this strongly imply their evolution froma common ancestor
(Pascarella and Argos, 1992), their findings confirmed
thatstructural similarities are reflected in sequence
similarity.
Current State of the PDB If similar sequences encode the same
main-chainfold, then a large number of sequences has to encode for
only a limited numberof folds. Indeed, when comparing the number of
protein structures versus theamount of different folds in the
Protein Data Bank (PDB), this reasoning seemsto hold true. During
the first decade of this millennium (2000-2009) the numberof
structures contained in the PDB increased from 9’749 to 57’613, an
almost6 fold increase. In the same timeframe, the number of unique
folds grew from622 to 1’393, a more than 2 fold increase after all.
The yearly increase of newfolds however stagnated at around 100 new
folds until 2006, dropped to 6 in2008 and since then, no new folds
have been added (as of January 2011). Thisdeclining rate is
documented in figure 3.2 where it can also be seen that thenumber
of new structures characterized yearly keeps to increase. This
observationroughly coincides with early predictions that the
majority of proteins stems fromno more than a thousand different
families (Chothia, 1992; Aloy and Russell, 2004),representing the
structural building blocks of protein evolution.
3.3.4 Identifying Conserved Domains
A correlation between structural similarity and amino-acid
sequence seems plausi-ble, as mentioned above, however measuring
sequence similarity as percent identityin sequence alignments is
too simplistic. As an illustration, proteins of the globinfamily
have diversely evolved in different species, yet are still folded
in the samegeneral 3-D pattern. The amino-acid sequences are only
identical in very fewresidues, some globins differ from others in
as many as 130 of the approximately150 positions (Dickerson and
Geis, 1983). This means that the “globin fold“ isencoded in various
amino-acid sequences barely reminiscent of each other. Thus,a more
dedicated approach to assess structural from sequence similarity,
termed“generalized profiles“, was introduced.
3. Scientific Overview 18
-
1990 1995 2000 2005 2010
02500
5000
7500
Structures and folds newly added to the PDB (1990 - 2010)
year
Num
ber o
f new
stru
ctur
es
050
100
150
Num
ber o
f new
fold
s
New structuresNew folds
Figure 3.2: The number of newly added structures and folds to
the PDB, per year. The numbersfor the years 1990-2010 have been
extracted from data available at
http://www.rcsb.org/pdb/statistics/contentGrowthChart.do?content=molType-protein&seqid=100
(Yearly Growthof Protein Structures) and
http://www.rcsb.org/pdb/statistics/contentGrowthChart.do?content=fold-scop
(Growth Of Unique Folds Per Year As Defined By SCOP (v1.75))
Generalized Profiles Generalized profiles are a very sensitive
method for de-tecting even distant protein relationships by
sequence comparison (Gribskov et al.,1987). In order to identify
homologous proteins, the query sequences are notqueried by a single
sequence but a profile constructed from a family of related
se-quences. The profiles themselves are derived from multiple
alignments of an initialsequence pool and contain the following
information:
• The residues which are allowed at what position• The
importance of the positions• Which positions allow insertions•
Which positions may be dispensable
As such, generalized profiles may describe common
characteristics of even dis-tantly related protein sequences.
Proteins which contain the desired motif can beidentified by
comparing their sequence to the profile.
3. Scientific Overview 19
http://www.rcsb.org/pdb/statistics/contentGrowthChart.do?content=molType-protein&seqid=100http://www.rcsb.org/pdb/statistics/contentGrowthChart.do?content=molType-protein&seqid=100http://www.rcsb.org/pdb/statistics/contentGrowthChart.do?content=fold-scophttp://www.rcsb.org/pdb/statistics/contentGrowthChart.do?content=fold-scop
-
3.3.5 Motifs and General Profiles
The profile information is stored in a position-specific scoring
matrix (PSSM).This matrix can be used to search a sequence database
for occurrences of themotif. As the name “scoring matrix” implies,
comparing sequences to PSSMsreturns a match score, a quantitative
rather than a qualitative estimation for therelationship between a
protein sequence and the profile. In order to decide whethera query
sequence contains a motif, the significance of the profile-sequence
matchhas to be evaluated by defining threshold levels for the match
score. Sequencesscoring above the match score threshold likely
contain the motif and are thereforepredicted to be phylogenetically
related.
Obtaining relevant cut-off levels is a difficult task. We used
an approach based onthe probability of finding a profile in nature,
substituting ‘nature’ with a random-ized database. Our randomized
database was created from Uniprot4 release 44.0by regional
shuffling using a window of 20 amino-acid residues, thereby
preservingsize, sequence length distribution and amino-acid
composition (Pearson and Lip-man, 1988). This approach has been
criticized for introducing significant bias suchas over-fitting and
failure to reflect the natural processes of random nucleotide
andamino acid replacement, thus using a database consisting of
randomly selected butunshuffled sequences might be an alternative
worth considering (Mitrophanov andBorodovsky, 2006).
Scoring all amino acid sequences of the randomized database
against the profilereturns an empirical score frequency
distribution. By fitting an extreme value dis-tribution (EVD,
Gumbel distribution) to these scores, an E-value can be deducedfor
each score with the formula:
E(x,A) = A× 10−R1−R2x
where R1 =lnA
N− λµ
ln10and R2 =
λ
ln10
The E-value associates an expected number of chance hits with
each score, i.e.the number of hits with scores exceeding x in a
database with A residues (Pagniand Jongeneel, 2001). N corresponds
to the number of sequences in the database,λ and µ are
characteristics of the EVD. The E-value parameters R1 and R2
canthen be used by profile search algorithms to return normalized
scores5. Unlike rawscores, the normalized scores can be compared
between different profiles and athreshold value separating
significant from random matches can be defined.
4Uniprot: http://www.uniprot.org/5pftools Nscore calculation:
http://www.isrec.isb-sib.ch/profile/scoredoc.html
3. Scientific Overview 20
http://www.uniprot.org/http://www.isrec.isb-sib.ch/profile/scoredoc.html
-
3.4 Bioinformatics of Cross Reactions
3.4.1 Motif Calculation
The theoretical background to allergen cross-reactivity
prediction has been de-scribed in section 3.3.5. We have utilized
an approach previously developed atour institute (Stadler and
Stadler, 2003), which aimed at identifying potentiallyallergenic
novel proteins in GM food. This approach uses MEME 6 (Bailey
andElkan, 1994) and the pftools7 (Bucher et al., 1996) in an
iterative fashion as shownin figure 3.3.
remove matching sequences
Motif discovery
repeat with remaining sequences
collect motifs
Profile scalingAllergome database
Motifs
Run set Motif
cd-hit
Dataset
MEME pftools
Figure 3.3: Iterative motif discovery. Allergen sequences are
downloaded from Allergome andclustered using cd-hit. MEME analyzes
all sequences in the run set and identifies the mostsignificant
motif. This motif is scaled against a randomized database using
pfscale and storedas a PSSM. The run set is scanned using pfscan
and all matching sequences are removed fromthe set. With the
remaining sequences this cycle is repeated until no more
significant motifs arefound.
Three changes to the original approach have been applied:
First, since the inception of the original approach, almost five
times as many aller-gen sequences are now available. This increase
in sequences also saw an increasein isoforms. Therefore we decided
to cluster sequences with a an identity of 90%or more prior to
submission to our iterative motif discovery using cd-hit8 (Li et
al.,2001). This clustering not only reduced the required
calculation time, which raisesexponentially with the number of
sequences, but also prevents the generation ofmotifs consisting
entirely of isoforms.
Second, in order to allow several motifs per protein sequence,
all protein sequences
6MEME is available from http://meme.sdsc.edu/meme/7pftools are
available from
http://www.isrec.isb-sib.ch/ftp-server/pftools/8cd-hit is available
from http://www.bioinformatics.org/cd-hit/
3. Scientific Overview 21
http://meme.sdsc.edu/meme/http://www.isrec.isb-sib.ch/ftp-server/pftools/http://www.
bioinformatics.org/cd-hit/
-
are screened against all discovered motifs. In the original
approach, only the motifdiscovered during the iterative process was
assigned to a protein.
Third, MEME was allowed to choose a variable motif length of 35
to 70 aminoacid residues (cf. dissertation equivalent II).
3.4.2 Web Interface
The work presented here requires data from different sources.
PSSMs representingallergen motifs (1) are calculated from allergen
sequences (2) retrieved from anonline database, Allergome9. These
calculations are compared against wet labdata (3) obtained in the
form of spreadsheet files. In order to efficiently workwith this
diverse data, source material was processed and stored in a
MySQL10
database. A web-based front-end in the PHP11 and JavaScript
programminglanguages was built in order to allow quick data
lookups. It is publicly accessiblefrom our institute’s website:
http://www.iib.unibe.ch/allergen/. Figure 3.4shows a screenshot of
the front-end.
3.5 Outlook: Protein Surface Comparison
The rationale behind predicting cross-reactivity by sequence
similarity mostlystems from the broad availability of sequence
data. A prediction closer to naturewould be direct comparison of
two proteins’ surfaces and consequentially judge thepossibility of
a cross-reaction. After all, structure is evolutionary more
conservedthan sequence (Holm and Sander, 1996). The low number of
available structurescompared to the number of sequences made this
approach unfeasible so far. How-ever, this proportion is changing
over time. More and more protein structuresare being determined by
X-ray or nuclear magnetic resonance (NMR) imaging.Additionally,
structures not yet experimentally determined may be inferred fromab
initio protein folding prediction and homology modeling with
increasing relia-bility.
3.5.1 Ab initio Protein Folding Prediction
Ab initio protein folding prediction is the prediction of yet
unknown protein struc-tures only from amino-acid sequences. Despite
the vast amount of possible con-formations for each sequence,
proteins generally fold into uniquely native states,their
thermodynamically most stable conformation. The dihedral angles φ
and ψmay each assume one of three stable positions, hence knowledge
of the amino-acid
9Allergome: http://www.allergome.org/10MySQL is available from
http://www.mysql.com/downloads/mysql/11PHP is available from
http://www.php.net/downloads.php
3. Scientific Overview 22
http://www.iib.unibe.ch/allergen/http://www.allergome.org/http://www.mysql.com/downloads/mysql/http://www.php.net/downloads.php
-
Figure 3.4: Screenshot of the web-based front-end. The website
is built with Web 2.0 tech-nologies and allows to lookup allergen
extracts, proteins and motifs via life-search. Furthermore,custom
protein sequences can be checked for occurrences of allergen
motifs.
sequence is potentially sufficient to predict the native fold of
a protein. The idea ofletting a computer test all possible
conformations comes to mind. This computerwould choose the
thermodynamically most favorable conformation, thus findinga
protein’s native state would merely be a question of available
computer time.However, an amino-acid sequence of 100 residues may
hypothetically fold into 3198
potential conformations (three states for each of the 99 φ and
99 ψ angles). Ifa protein was to fold into each of these
conformations in order to find its nativestate, it would have to
fold for a time period much longer than the age of ourknown
universe, even if it would only use picoseconds per state (cf. the
Levinthalparadox ). As of December 2010, the fastest supercomputer
in the world can per-form 2’570 calculations per picosecond12. This
machine would have to calculatefor more than 1071 years, even by
oversimplifying one complete structure compar-ison to one clock
cycle. It is evident that this brute force approach is not
leastimpossible.
When applying evolutionary information and stochastic methods to
this approach,the calculations still require vast computational
resources and so far have only been
122.566 petaflops. Rank 1 in November 2010’s TOP500 list of the
world’s most powerfulsupercomputers:
http://www.top500.org/lists/2010/11
3. Scientific Overview 23
http://www.top500.org/lists/2010/11
-
carried out for tiny, fast-folding proteins. Nevertheless, there
are promising ap-proaches emerging from this field, the computer
cost however is still too expensivefor broad use of the technique
(Zhang, 2008). As an example, folding a 36 residuealpha-helical
protein to an accuracy of on average 1.7Å to 1.9Å away from the
na-tive state uses a total simulation time of approximately 1000
CPU years (Zagrovicet al., 2002). Folding@Home is a promising
approach utilizing distributed molecu-lar dynamics calculations,
running on thousands of personal computers around theworld (Shirts
and Pande, 2000). The Folding@Home team has published a wealthof
papers providing insight not only into the potential of ab initio
protein folding,but also into understanding the mechanisms of
protein folding kinetics (Pande,2010). Still, even this approach is
limited to predicting the structure of peptidesand small
proteins.
3.5.2 Homology Modeling
A different approach to bioinformatic protein structure
prediction is homologymodeling or template based modeling. Compared
to ab initio prediction, homol-ogy modeling predicts the protein
structure via comparison to a template struc-ture. Therefore, the
existence of similar structures in the PDB is a necessityfor a
successful prediction. Identifying and aligning the best template
structure(termed threading or fold recognition) is the first
important step towards a correctprediction. Not surprisingly, the
most often used amongst the many threadingapproaches use sequence
profile-profile alignments to identify phylogenetically re-lated
structure templates (Skolnick et al., 2004; Jaroszewski et al.,
2005). Zhangand Skolnick recently showed that high-quality
full-length models can be builtfor all protein targets with an
average root mean square deviation (rmsd) of 2.25Å (Zhang and
Skolnick, 2005). This suggests that the structural universe of
thecurrent PDB library is essentially complete for solving the
protein structure prob-lem, at least for single-domain
proteins.
The protein folding prediction field has quite literally turned
into a sport withdifferent research groups trying to best each
other in predicting structures in abiannual large-scale experiment
known as the Critical Assessment of Techniquesfor Protein Structure
Prediction (CASP)13. The advances in the field already to-day offer
the possibility to predict yet unknown protein structures from
sequencewith an astonishing accuracy. For cross-reactivity
prediction, homology modelingmay possibly provide protein
structures even for novel proteins, which may subse-quently be used
to seek surface epitopes. Using above-mentioned rmsd of 2.25 Åas a
reference, the accuracy of the predicted structures is potentially
high enoughfor the prediction of cross-reactive epitopes, given
that the antibody-antigen bind-ing surface encompasses almost 1’000
Å2 (Davies et al., 1988; Braden and Poljak,1995). Therefore it
seems feasible to substitute computationally predicted struc-tures
for protein structures which have not yet been experimentally
determined.
13CASP: http://predictioncenter.org/
3. Scientific Overview 24
http://predictioncenter.org/
-
The number of available 3-D allergen structures may approach the
number of non-isotypic allergen sequences, enabling high quality
cross-reactivity prediction basedon tertiary structures and thereby
protein surfaces.
3.5.3 Prediction of Similar Surfaces
Predicting the fold of a protein however is only half the story.
After the generationof the tertiary structure, the proteins’ B-cell
epitopes have to be identified andsubsequently, these epitopes have
to be compared in order to identify cross-reactingproteins. A full
molecular docking prediction is not needed as we are not
interestedin the binding capacity of an antibody, but merely the
similarity of two proteinsurfaces.
The first problem, accurately predicting B-cell epitopes, is a
major challenge es-pecially in vaccine development. However, even
though recent publications pro-pose improved epitope prediction
methods (Scarabelli et al., 2010; Fiorucci andZacharias, 2010), the
field has apparently not yet achieved a high level of reliabil-ity
allowing to forego laboratory experiments (Bryson et al., 2010).
Whether thereliability would be high enough for cross-reactivity
prediction would have to bedetermined. Anyway, a similarity search
on entire protein surfaces as opposed toonly searching epitopes
might eliminate the need to identify epitopes in the
firstplace.
Thus a last problem persists: comparing the surfaces of two
proteins and identifysimilar patches. Several approaches to this
problem have been proposed, somepurely geometrical (e.g. spin-image
representations (Bock et al., 2007), geometricinvariant
fingerprints (Yin et al., 2009)), others respecting electrostatic
properties(e.g. the adaptive Poisson-Boltzmann solver (Baker et
al., 2001)). It would cer-tainly be interesting to apply these
techniques to 3-D allergen structures in orderto identify
potentially cross-reacting surface patches.
3. Scientific Overview 25
-
References
Klaus J Erb. Helminths, allergic disorders and ige-mediated
immune responses:where do we stand? Eur J Immunol, 37(5):1170–3,
May 2007. doi: 10.1002/eji.200737314.
Hannah J Gould, Brian J Sutton, Andrew J Beavil, Rebecca L
Beavil, Na-talie McCloskey, Heather A Coker, David Fear, and Lyn
Smurthwaite.The biology of ige and the basis of allergic disease.
Annu Rev Im-munol, 21:579–628, Jan 2003. doi:
10.1146/annurev.immunol.21.120601.141103.URL
http://www.annualreviews.org/doi/abs/10.1146/annurev.immunol.21.120601.141103.
A B Kay. Overview of ’allergy and allergic diseases: with a view
to the future’. BrMed Bull, 56(4):843–64, 2000. URL
http://www.ncbi.nlm.nih.gov/pubmed/11359624.
Birgit Helm, Philip Marsh, Donata Vercelli, Eduardo Padlan,
Hannah Gould, andRaif Geha. The mast cell binding site on human
immunoglobulin e. Nature, 331(6152):180, Jan 1988. doi:
doi:10.1038/331180a0. URL
http://www.nature.com/nature/journal/v331/n6152/abs/331180a0.html.
H Metzger. The high affinity receptor for ige on mast cells.
Clin Exp Allergy, 21(3):269–79, May 1991.
M J Nadler, S A Matthews, H Turner, and J P Kinet. Signal
transduction by thehigh-affinity immunoglobulin e receptor fc
epsilon ri: coupling form to function.Adv Immunol, 76:325–55, Jan
2000.
Sanford B Hooker and William C Boyd. The existence of antigenic
determinantsof diverse specificity in a single protein — the
journal of immunology. Journalof Immunology, 26:469–79, 1934. URL
http://www.jimmunol.org/content/26/6/469.abstract.
P H Maurer. I. antigenicity of oxypolygelatin and gelatin in
man. J Exp Med,100(5):497–513, Nov 1954. URL
http://www.ncbi.nlm.nih.gov/pubmed/13211910.
P G H Gell and B Benacerraf. Studies on hypersensitivity. ii.
delayed hypersensi-tivity to denatured proteins in guinea pigs.
Immunology, 2(1):64–70, Jan 1959.URL
http://www.ncbi.nlm.nih.gov/pubmed/13640681.
B Benacerraf and P G H Gell. Studies on hypersensitivity. i.
delayed and arthus-type skin reactivity to protein conjugates in
guinea pigs. Immunology, 2(1):53–63, Jan 1959. URL
http://www.ncbi.nlm.nih.gov/pubmed/13640680.
F Ferreira, T Hawranek, P Gruber, N Wopfner, and Adriano Mari.
Allergic cross-reactivity: from gene to the clinic. Allergy,
59(3):243–67, Mar 2004. doi: 10.1046/j.1398-9995.2003.00407.x.
3. Scientific Overview 26
http://www.annualreviews.org/doi/abs/10.1146/annurev.immunol.21.120601.141103http://www.annualreviews.org/doi/abs/10.1146/annurev.immunol.21.120601.141103http://www.ncbi.nlm.nih.gov/pubmed/11359624http://www.ncbi.nlm.nih.gov/pubmed/11359624http://www.nature.com/nature/journal/v331/n6152/abs/331180a0.htmlhttp://www.nature.com/nature/journal/v331/n6152/abs/331180a0.htmlhttp://www.jimmunol.org/content/26/6/469.abstracthttp://www.jimmunol.org/content/26/6/469.abstracthttp://www.ncbi.nlm.nih.gov/pubmed/13211910http://www.ncbi.nlm.nih.gov/pubmed/13211910http://www.ncbi.nlm.nih.gov/pubmed/13640681http://www.ncbi.nlm.nih.gov/pubmed/13640680
-
K W Wucherpfennig and J L Strominger. Molecular mimicry in t
cell-mediatedautoimmunity: viral peptides activate human t cell
clones specific for myelinbasic protein. Cell, 80(5):695–705, Mar
1995.
D R Davies, S Sheriff, and E A Padlan. Antibody-antigen
complexes. J BiolChem, 263(22):10541–4, Aug 1988. URL
http://www.jbc.org/content/263/22/10541.long.
Virginie Lafont, Michael Schaefer, Roland H Stote, Danièle
Altschuh, and AnnickDejaegere. Protein-protein recognition and
interaction hot spots in an antigen-antibody complex: free energy
decomposition identifies ”efficient amino acids”.Proteins,
67(2):418–34, May 2007. doi: 10.1002/prot.21259. URL
http://www.ncbi.nlm.nih.gov/pubmed/17256770.
J W Kappler, N Roehm, and P Marrack. T cell tolerance by clonal
elimination inthe thymus. Cell, 49(2):273–80, Apr 1987.
P Kisielow, H S Teh, H Blüthmann, and H von Boehmer. Positive
selection ofantigen-specific t cells in thymus by restricting mhc
molecules. Nature, 335(6192):730–3, Oct 1988. doi:
10.1038/335730a0. URL
http://www.nature.com/nature/journal/v335/n6192/abs/335730a0.html.
J Holm, G Baerentzen, M Gajhede, H Ipsen, J N Larsen, H
Løwenstein, M Wis-senbach, and M D Spangfort. Molecular basis of
allergic cross-reactivity be-tween group 1 major allergens from
birch and apple. J Chromatogr B BiomedSci Appl, 756(1-2):307–13,
May 2001. URL http://www.ncbi.nlm.nih.gov/pubmed/11419722.
S Laffer, R Valenta, S Vrtala, M Susani, R van Ree, D Kraft, O
Scheiner, andM Duchêne. Complementary dna cloning of the major
allergen phl p i fromtimothy grass (phleum pratense); recombinant
phl p i inhibits ige binding togroup i allergens from eight
different grass species. J Allergy Clin Immunol, 94(4):689–98, Oct
1994. URL http://www.ncbi.nlm.nih.gov/pubmed/7930302.
S Laffer, M Duchene, I Reimitzer, M Susani, C Mannhalter, D
Kraft, and R Va-lenta. Common ige-epitopes of recombinant phl p i,
the major timothy grasspollen allergen and natural group i grass
pollen isoallergens. Mol Immunol, 33(4-5):417–26, Jan 1996. URL
http://www.ncbi.nlm.nih.gov/pubmed/8676893.
R Valenta, M Duchene, C Ebner, P Valent, C Sillaber, P Deviller,
F Ferreira,M Tejkl, H Edelmann, and D Kraft. Profilins constitute a
novel family offunctional plant pan-allergens. J Exp Med,
175(2):377–85, Feb 1992.
H Breiteneder and C Ebner. Molecular and biochemical
classification of plant-derived food allergens. J Allergy Clin
Immunol, 106(1 Pt 1):27–36, Jul 2000.doi:
10.1067/mai.2000.106929.
T Midoro-Horiuti, E G Brooks, and R M Goldblum.
Pathogenesis-related proteinsof plants as allergens. Ann. Allergy
Asthma Immunol., 87(4):261–71, Oct 2001.doi:
10.1016/S1081-1206(10)62238-7.
3. Scientific Overview 27
http://www.jbc.org/content/263/22/10541.longhttp://www.jbc.org/content/263/22/10541.longhttp://www.ncbi.nlm.nih.gov/pubmed/17256770http://www.ncbi.nlm.nih.gov/pubmed/17256770http://www.nature.com/nature/journal/v335/n6192/abs/335730a0.htmlhttp://www.nature.com/nature/journal/v335/n6192/abs/335730a0.htmlhttp://www.ncbi.nlm.nih.gov/pubmed/11419722http://www.ncbi.nlm.nih.gov/pubmed/11419722http://www.ncbi.nlm.nih.gov/pubmed/7930302http://www.ncbi.nlm.nih.gov/pubmed/8676893
-
S L Surplus, B R Jordan, A M Murphy, J P Carr, B Thomas, and S A
H Mack-erness. Ultraviolet-b-induced responses in arabidopsis
thaliana: role of salicylicacid and reactive oxygen species in the
regulation of transcripts encoding pho-tosynthetic and acidic
pathogenesis-related proteins - surplus - 2002 - plant, cell&
environment - wiley online library. Plant, Cell and Environment,
21:685–94,1998. URL
http://onlinelibrary.wiley.com/doi/10.1046/j.1365-3040.1998.00325.x/pdf.
E A Padlan. Anatomy of the antibody molecule. Molecular
Immunology, 31(3):169–217, 1994. URL
http://www.ncbi.nlm.nih.gov/pubmed/8114766.
C F Barbas, A Heine, G Zhong, T Hoffmann, S Gramatikova, R
Björnestedt,B List, J Anderson, E A Stura, I A Wilson, and R A
Lerner. Immune versusnatural selection: antibody aldolases with
enzymic rates but broader scope.Science, 278(5346):2085–92, Dec
1997. URL http://www.sciencemag.org/content/278/5346/2085.long.
Leo C James and Dan S Tawfik. The specificity of
cross-reactivity: promis-cuous antibody binding involves specific
hydrogen bonds rather than nonspe-cific hydrophobic stickiness.
Protein Science : A Publication of the Pro-tein Society,
12(10):2183–93, Oct 2003. doi: 10.1110/ps.03172703.
URLhttp://www.ncbi.nlm.nih.gov/pubmed/14500876.
K Fötisch, F Altmann, D Haustein, and S Vieths. Involvement of
carbohy-drate epitopes in the ige response of celery-allergic
patients. Int Arch Al-lergy Immunol, 120(1):30–42, Sep 1999. URL
http://content.karger.com/produktedb/produkte.asp?typ=fulltext&file=iaa20030.
Rob C Aalberse, V Koshte, and J G J Clemens. Immunoglobulin e
antibod-ies that crossreact with vegetable foods, pollen, and
hymenoptera venom.Journal of Allergy and Clinical Immunology,
68(5):356–364, 1981. doi: doi:10.1016/0091-6749(81)90133-0. URL
http://www.ncbi.nlm.nih.gov/pubmed/7298999.
A Mari, P Iacovacci, C Afferni, B Barletta, R Tinghino, G Di
Felice, andC Pini. Specific ige to cross-reactive carbohydrate
determinants strongly af-fect the in vitro diagnosis of allergic
diseases. J Allergy Clin Immunol, 103(6):1005–11, Jun 1999. URL
http://linkinghub.elsevier.com/retrieve/pii/S0091674999003486.
Renato Erzen, Peter Korosec, Mira Silar, Ema Music, and Mitja
Kosnik. Car-bohydrate epitopes as a cause of cross-reactivity in
patients allergic to hy-menoptera venom. Wiener klinische
Wochenschrift, 121(9-10):349–52, Jan 2009.doi:
10.1007/s00508-009-1171-1.
Stefan Vieths, Stephan Scheurer, and Barbara Ballmer-Weber.
Current under-standing of cross-reactivity of food allergens and
pollen. Ann N Y Acad Sci,964:47–68, May 2002.
3. Scientific Overview 28
http://onlinelibrary.wiley.com/doi/10.1046/j.1365-3040.1998.00325.x/pdfhttp://onlinelibrary.wiley.com/doi/10.1046/j.1365-3040.1998.00325.x/pdfhttp://www.ncbi.nlm.nih.gov/pubmed/8114766http://www.sciencemag.org/content/278/5346/2085.longhttp://www.sciencemag.org/content/278/5346/2085.longhttp://www.ncbi.nlm.nih.gov/pubmed/14500876http://content.karger.com/produktedb/produkte.asp?typ=fulltext&file=iaa20030http://content.karger.com/produktedb/produkte.asp?typ=fulltext&file=iaa20030http://www.ncbi.nlm.nih.gov/pubmed/7298999http://www.ncbi.nlm.nih.gov/pubmed/7298999http://linkinghub.elsevier.com/retrieve/pii/S0091674999003486http://linkinghub.elsevier.com/retrieve/pii/S0091674999003486
-
Rob C Aalberse, J Akkerdaas, and R van Ree. Cross-reactivity of
ige antibodiesto allergens. Allergy, 56(6):478–90, Jun 2001.
J Lescar, M Pellegrini, H Souchon, D Tello, R J Poljak, N
Peterson, M Greene,and P M Alzari. Crystal structure of a
cross-reaction complex between fabf9.13.7 and guinea fowl lysozyme.
J Biol Chem, 270(30):18067–76, Jul 1995.URL
http://www.jbc.org/content/270/30/18067.long.
Rob C Aalberse. Assessment of sequence homology and
cross-reactivity. ToxicolAppl Pharmacol, 207(2 Suppl):149–51, Sep
2005. doi: 10.1016/j.taap.2005.01.021.
C H Blackley. Experimental researches on the causes and nature
of cattarrhusaestivus. Balliere, Trindall, & Cox, 1873.
H J Chong Neto and N A Rosário. Studying specific ige: in vivo
or in vitro.Allergologia et immunopathologia, 37(1):31–5, Jan 2009.
URL
http://www.elsevier.es/revistas/ctl_servlet?_f=7014&articuloid=13133446.
Scott H Sicherer and Hugh A Sampson. Food allergy. J. Allergy
Clin. Immunol.,125(2 Suppl 2):S116–25, Feb 2010. doi:
10.1016/j.jaci.2009.08.028. URL
http://www.ncbi.nlm.nih.gov/pubmed/20042231.
John H Krouse and Richard L Mabry. Skin testing for inhalant
allergy 2003:current strategies. Otolaryngol Head Neck Surg, 129(4
Suppl):S33–49, Oct 2003.URL
http://www.ncbi.nlm.nih.gov/pubmed/14574280.
S Tripodi, A Di Rienzo Businco, C Alessandri, V Panetta, P
Restani, and P M Ma-tricardi. Predicting the outcome of oral food
challenges with hen’s egg throughskin test end-point titration.
Clin Exp Allergy, 39(8):1225–33, Aug 2009.
doi:10.1111/j.1365-2222.2009.03250.x. URL
http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2222.2009.03250.x/abstract.
E A Pastorello, C Incorvaia, C Ortolani, S Bonini, G W Canonica,
S Romag-nani, A Tursi, and C Zanussi. Studies on the relationship
between the levelof specific ige antibodies and the clinical
expression of allergy: I. definitionof levels distinguishing
patients with symptomatic from patients with asymp-tomatic allergy
to common aeroallergens. J Allergy Clin Immunol, 96(5 Pt1):580–7,
Nov 1995. URL
http://linkinghub.elsevier.com/retrieve/pii/S0091-6749(95)70255-5.
Martin D Chapman, A M Smith, L D Vailes, L K Arruda, V Dhanaraj,
andA Pomés. Recombinant allergens for diagnosis and therapy of
allergic dis-ease. J Allergy Clin Immunol, 106(3):409–18, Sep 2000.
doi: 10.1067/mai.2000.109832. URL
http://linkinghub.elsevier.com/retrieve/pii/S0091674900564069.
Robert G Hamilton. Clinical laboratory assessment of
immediate-type hyper-sensitivity. J. Allergy Clin. Immunol., 125(2
Suppl 2):S284–96, Feb 2010.
3. Scientific Overview 29
http://www.jbc.org/content/270/30/18067.longhttp://www.elsevier.es/revistas/ctl_servlet?_f=7014&articuloid=13133446http://www.elsevier.es/revistas/ctl_servlet?_f=7014&articuloid=13133446http://www.ncbi.nlm.nih.gov/pubmed/20042231http://www.ncbi.nlm.nih.gov/pubmed/20042231http://www.ncbi.nlm.nih.gov/pubmed/14574280http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2222.2009.03250.x/abstracthttp://onlinelibrary.wiley.com/doi/10.1111/j.1365-2222.2009.03250.x/abstracthttp://linkinghub.elsevier.com/retrieve/pii/S0091-6749(95)70255-5http://linkinghub.elsevier.com/retrieve/pii/S0091-6749(95)70255-5http://linkinghub.elsevier.com/retrieve/pii/S0091674900564069http://linkinghub.elsevier.com/retrieve/pii/S0091674900564069
-
doi: 10.1016/j.jaci.2009.09.055. URL
http://www.ncbi.nlm.nih.gov/pubmed/20176264.
R Valenta, J Lidholm, V Niederberger, B Hayek, D Kraft, and H
Grönlund. Therecombinant allergen-based concept of
component-resolved diagnostics and im-munotherapy (crd and crit).
Clin Exp Allergy, 29(7):896–904, Jul 1999.
M van Hage-Hamsten and G Pauli. Provocation testing with
recombinant aller-gens. Methods, 32(3):281–91, Mar 2004. doi:
10.1016/j.ymeth.2003.08.007.
URLhttp://www.ncbi.nlm.nih.gov/pubmed/14962763.
Reinhard Hiller, Sylvia Laffer, Christian Harwanegg, Martin
Huber, Wolfgang MSchmidt, Anna Twardosz, Bianca Barletta, Wolf M
Becker, Kurt Blaser, HeimoBreiteneder, Martin Chapman, Reto
Crameri, Michael Duchêne, Fatima Fer-reira, Helmut Fiebig, Karin
Hoffmann-Sommergruber, Te Piao King, TamaraKleber-Janke, Viswanath
P Kurup, Samuel B Lehrer, Jonas Lidholm, UlrichMüller, Carlo Pini,
Gerald Reese, Otto Scheiner, Annika Scheynius, Horng-DerShen,
Susanne Spitzauer, Roland Suck, Ines Swoboda, Wayne Thomas,
Raf-faela Tinghino, Marianne Van Hage-Hamsten, Tuomas Virtanen,
Dietrich Kraft,Manfred W Müller, and Rudolf Valenta. Microarrayed
allergen molecules: diag-nostic gatekeepers for allergy treatment.
FASEB J., 16(3):414–6, Mar 2002. doi:10.1096/fj.01-0711fje. URL
http://www.fasebj.org/content/early/2002/03/02/fj.01-0711fje.long.
FAO and WHO. Evaluation of allergenicity of genetically modified
foods. reportof a joint fao/who expert consultation on
allergenicity of foods derived frombiotechnology. Jan 2001.
J A Nordlee, S L Taylor, J A Townsend, L A Thomas, and R K Bush.
Identificationof a brazil-nut allergen in transgenic soybeans. N.
Engl. J. Med., 334(11):688–92, Mar 1996. doi:
10.1056/NEJM199603143341103. URL
http://www.nejm.org/doi/full/10.1056/NEJM199603143341103.
Michael B Stadler and Beda M Stadler. Allergenicity prediction
by protein se-quence. FASEB J., 17(9):1141–3, Apr 2003. doi:
10.1096/fj.02-1052fje.
URLhttp://www.fasebj.org/cgi/content/abstract/02-1052fjev1.
Kuo-Bin Li, Praveen Issac, and Arun Krishnan. Predicting
allergenic proteinsusing wavelet transform. Bioinformatics,
20(16):2572–8, Nov 2004. doi: 10.1093/bioinformatics/bth286. URL
http://bioinformatics.oxfordjournals.org/cgi/reprint/20/16/2572.
Tariq Riaz, Hen Ley Hor, Arun Krishnan, Francis Tang, and
Kuo-Bin Li. We-ballergen: a web server for predicting allergenic
proteins. Bioinformatics,21(10):2570–1, May 2005. doi:
10.1093/bioinformatics/bti356. URL
http://bioinformatics.oxfordjournals.org/cgi/content/full/21/10/2570.
Karluss Thomas, Gary Bannon, Susan Hefle, Corinne Herouet,
Michael Holsap-ple, Gregory Ladics, Sue Macintosh, and Laura
Privalle. In silico methods for
3. Scientific Overview 30
http://www.ncbi.nlm.nih.gov/pubmed/20176264http://www.ncbi.nlm.nih.gov/pubmed/20176264http://www.ncbi.nlm.nih.gov/pubmed/14962763http://www.fasebj.org/content/early/2002/03/02/fj.01-0711fje.longhttp://www.fasebj.org/content/early/2002/03/02/fj.01-0711fje.longhttp://www.nejm.org/doi/full/10.1056/NEJM199603143341103http://www.nejm.org/doi/full/10.1056/NEJM199603143341103http://www.fasebj.org/cgi/content/abstract/02-1052fjev1http://bioinformatics.oxfordjournals.org/cgi/reprint/20/16/2572http://bioinformatics.oxfordjournals.org/cgi/reprint/20/16/2572http://bioinformatics.oxfordjournals.org/cgi/content/full/21/10/2570http://bioinformatics.oxfordjournals.org/cgi/content/full/21/10/2570
-
evaluating human allergenicity to novel proteins: International
bioinformaticsworkshop meeting report, 23-24 february 2005.
Toxicological Sciences, 88(2):307–10, Dec 2005. doi:
10.1093/toxsci/kfi277.
Sudipto Saha and G P S Raghava. Algpred: prediction of
allergenic proteins andmapping of ige epitopes. Nucleic Acids Res,
34(Web Server issue):W202–9, Jul2006. doi: 10.1093/nar/gkl343. URL
http://nar.oxfordjournals.org/cgi/content/full/34/suppl_2/W202.
ZH Zhang, JL Koh, GL Zhang, KH Choo, MT Tammi, and JC Tong.
Allertool:a web server for predicting allergenicity and allergic
cross-reactivity in proteins.Bioinformatics, Dec 2006. doi:
10.1093/bioinformatics/btl621. URL
http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btl621v1.
Waiming Kong, Tsu Soo Tan, Lawrence Tham, and Keng Wah Choo.
Improvedprediction of allergenicity by combination of multiple
sequence motifs. In Sil-ico Biol (Gedrukt), 7(1):77–86, Jan 2007.
URL http://www.bioinfo.de/isb/2006070006/.
Juan Cui, Lian Yi Han, Hu Li, Choong Yong Ung, Zhi Qun Tang,
Chan JuanZheng, Zhi Wei Cao, and Yu Zong Chen. Computer prediction
of allergen pro-teins from sequence-derived protein structural and
physicochemical properties.Mol Immunol, 44(4):514–20, Jan 2007.
doi: 10.1016/j.molimm.2006.02.010.
Catherine H. Schein, Ovidiu Ivanciuc, and Werner Braun.
Bioinformatics ap-proaches to classifying allergens and predicting
cross-reactivity. Immunol-ogy and allergy clinics of North America,
27(1):1, Feb 2007. doi: 10.1016/j.iac.2006.11.005. URL
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1941676.
Alvaro Martinez Barrio, Daniel Soeria-Atmadja, Anders Nistér,
Mats G Gustafs-son, Ulf Hammerling, and Erik Bongcam-Rudloff.
Evaller: a web server forin silico assessment of potential protein
allergenicity. Nucleic Acids Res, 35(Web Server issue):W694–700,
Jul 2007. doi: 10.1093/nar/gkm370.
URLhttp://nar.oxfordjournals.org/cgi/content/full/35/suppl_2/W694.
Joo Chuan Tong and Martti T Tammi. Prediction of protein
allergenicity usinglocal description of amino acid sequence. Front
Biosci, 13:6072–8, Jan 2008.URL
http://www.bioscience.org/2008/v13/af/3138/fulltext.htm.
Shen Jean Lim, Joo Chuan Tong, Fook Tim Chew, and Martti T
Tammi. The valueof position-specific scoring matrices for
assessment of protein allegenicity. BMCBioinformatics, 9 Suppl
12:S21, Jan 2008. doi: 10.1186/1471-2105-9-S12-S21.
Hon Cheng Muh, Joo Chuan Tong, and Martti T Tammi. Allerhunter:
asvm-pairwise system for assessment of allergenicity and allergic
cross-reactivityin proteins. PLoS ONE, 4(6):e5861, Jan 2009. doi:
10.1371/journal.pone.0005861. URL
http://www.plosone.org/article/info%253Adoi%252F10.1371%252Fjournal.pone.0005861.
3. Scientific Overview 31
http://nar.oxfordjournals.org/cgi/content/full/34/suppl_2/W202http://nar.oxfordjournals.org/cgi/content/full/34/suppl_2/W202http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btl621v1http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btl621v1http://www.bioinfo.de/isb/2006070006/http://www.bioinfo.de/isb/2006070006/http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1941676http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1941676http://nar.oxfordjournals.org/cgi/content/full/35/suppl_2/W694http://www.bioscience.org/2008/v13/af/3138/fulltext.htmhttp://www.plosone.org/article/info%253Adoi%252F10.1371%252Fjournal.pone.0005861http://www.plosone.org/article/info%253Adoi%252F10.1371%252Fjournal.pone.0005861
-
Ovidiu Ivanciuc, Catherine H Schein, Tzintzuni Garcia, Numan
Oezguen, Suren-dra S Negi, and Werner Braun. Structural analysis of
linear and conformationalepitopes of allergens. Regul Toxicol
Pharmacol, 54(3 Suppl):S11–9, Aug 2009.doi:
10.1016/j.yrtph.2008.11.007.
R Asero. Effects of birch pollen-specific immunotherapy on apple
allergy inbirch pollen-hypersensitive patients. Clin Exp Allergy,
28(11):1368–73, Nov1998. URL
http://onlinelibrary.wiley.com/doi/10.1046/j.1365-2222.1998.00399.x/abstract.
S Pascarella and P Argos. A data bank merging related protein
structuresand sequences. Protein Eng, 5(2):121–37, Mar 1992. URL
http://peds.oxfordjournals.org/content/5/2/121.long.
C Chothia. Proteins. one thousand families for the molecular
biologist. Nature,357(6379):543–4, Jun 1992. doi: 10.1038/357543a0.
URL
http://www.nature.com/nature/journal/v357/n6379/abs/357543a0.html.
Patrick Aloy and Robert B Russell. Ten thousand interactions for
the molecular bi-ologist. Nature Biotechnology, 22(10):1317, Oct
2004. doi: doi:10.1038/nbt1018.URL
http://www.nature.com/nbt/journal/v22/n10/full/nbt1018.html.
R E Dickerson and I Geis. Hemoglobin: structure, function,
evolution and pathol-ogy. Benjamin/Cummings Publishing Co., Inc.,
1983.
M Gribskov, A D McLachlan, and D Eisenberg. Profile analysis:
detection ofdistantly related proteins. Proc Natl Acad Sci USA,
84(13):4355–8, Jul 1987.URL
http://www.pnas.org/cgi/reprint/84/13/4355.
W R Pearson and D J Lipman. Improved tools for biological
sequence comparison.Proc Natl Acad Sci USA, 85(8):2444–8, Apr 1988.
URL http://www.pnas.org/content/85/8/2444.long.
Alexander Yu Mitrophanov and Mark Borodovsky. Statistical
significance in bio-logical sequence analysis. Brief
Bioinformatics, 7(1):2–24, Mar 2006.
M Pagni and C V Jongeneel. Making sense of score statistics for
sequencealignments. Brief Bioinformatics, 2(1):51–67, Mar 2001. URL
http://bib.oxfordjournals.org/content/2/1/51.long.
Timothy L Bailey and C Elkan. Fitting a mixture model by
expectation maxi-mization to discover motifs in biopolymers.
Proceedings / International Con-ference on Intelligent Systems for
Molecular Biology ; ISMB International Con-ference on Intelligent
Systems for Molecular Biology, 2:28–36, Jan 1994.
URLhttp://www.ncbi.nlm.nih.gov/pubmed/7584402?dopt=abstract.
P Bucher, K Karplus, N Moeri, and K Hofmann. A flexible motif
search techniquebased on generalized profiles. Comput Chem,
20(1):3–23, Mar 1996. URL
http://www.ncbi.nlm.nih.gov/pubmed/8867839.
3. Scientific Overview 32
http://onlinelibrary.wiley.com/doi/10.1046/j.1365-2222.1998.00399.x/abstracthttp://onlinelibrary.wiley.com/doi/10.1046/j.1365-2222.1998.00399.x/abstracthttp://peds.oxfordjournals.org/content/5/2/121.longhttp://peds.oxfordjournals.org/content/5/2/121.longhttp://www.nature.com/nature/journal/v357/n6379/abs/357543a0.htmlhttp://www.nature.com/nature/journal/v357/n6379/abs/357543a0.htmlhttp://www.nature.com/nbt/journal/v22/n10/full/nbt1018.htmlhttp://www.pnas.org/cgi/reprint/84/13/4355http://www.pnas.org/content/85/8/2444.longhttp://www.pnas.org/content/85/8/2444.longhttp://bib.oxfordjournals.org/content/2/1/51.longhttp://bib.oxfordjournals.org/content/2/1/51.longhttp://www.ncbi.nlm.nih.gov/pubmed/7584402?dopt=abstracthttp://www.ncbi.nlm.nih.gov/pubmed/8867839http://www.ncbi.nlm.nih.gov/pubmed/8867839
-
W Li, L Jaroszewski, and A Godzik. Clustering of highly
homologous sequencesto reduce the size of large protein databases.
Bioinformatics (Oxford, England),17(3):282–3, Mar 2001.
L Holm and C Sander. Mapping the protein universe. Science,
273(5275):595–603,Aug 1996. URL
http://www.sciencemag.org/content/273/5275/595.long.
Yang Zhang. Progress and challenges in protein structure
prediction. Curr OpinStruct Biol, 18(3):342–8, Jun 2008. doi:
10.1016/j.sbi.2008.02.004.
Bojan Zagrovic, Christopher D Snow, Michael R Shirts, and Vijay
S Pande.Simulation of folding of a small alpha-helical protein in
atomistic detail usingworldwide-distributed computing. J Mol Biol,
323(5):927–37, Nov 2002.
URLhttp://www.ncbi.nlm.nih.gov/pubmed/12417204.
Michael Shirts and Vijay S Pande. Screen savers of the world
unite! Science, 290(5498):1903–4, 2000. doi:
10.1126/science.290.5498.1903. URL
http://www.ncbi.nlm.nih.gov/pubmed/17742054.
Vijay S Pande. A simple theory of protein folding kinetics.
2010. URL http://arxiv.org/abs/1007.0315.
Jeffrey Skolnick, Daisuke Kihara, and Yang Zhang. Development
andlarge scale benchmark testing of the prospector 3 threading
algo-rithm. Proteins, 56(3):502–18, Aug 2004. doi:
10.1002/prot.20106. URL
http://onlinelibrary.wiley.com/doi/10.1002/prot.20106/abstract;jsessionid=7D0BBD01853416611E9A418F01E41F82.d02t02.
Lukasz Jaroszewski, Leszek Rychlewski, Zhanwen Li, Weizhong Li,
and AdamGodzik. Ffas03: a server for profile–profile sequence
alignments. Nucleic AcidsRes, 33(Web Server issue):W284–8, Jul
2005. doi: 10.1093/nar/gki418.
URLhttp://nar.oxfordjournals.org/content/33/suppl_2/W284.long.
Yang Zhang and Jeffrey Skolnick. The protein structure
prediction problem couldbe solved using the current pdb library.
Proc Natl Acad Sci USA, 102(4):1029–34, Jan 2005. doi:
10.1073/pnas.0407152101. URL
http://www.pnas.org/content/102/4/1029.long.
B C Braden and R J Poljak. Structural features of the reactions
between antibodiesand protein antigens. FASEB J., 9(1):9–16, Jan
1995.
Guido Scarabelli, Giulia Morra, and Giorgio Colombo. Predicting
interaction sitesfrom the energetics of isolated proteins: a new
approach to epitope mapping.Biophys J, 98(9):1966–75, May 2010.
doi: 10.1016/j.bpj.2010.01.014.
URLhttp://www.ncbi.nlm.nih.gov/pubmed/20441761.
Sébastien Fiorucci and Martin Zacharias. Prediction of
protein-protein interactionsites using electrostatic desolvation
profiles. Biophys J, 98(9):1921–30, May2010. doi:
10.1016/j.bpj.2009.12.4332. URL
http://www.ncbi.nlm.nih.gov/pubmed/20441756.
3. Scientific Overview 33
http://www.sciencemag.org/content/273/5275/595.longhttp://www.ncbi.nlm.nih.gov/pubmed/12417204http://www.ncbi.nlm.nih.gov/pubmed/17742054http://www.ncbi.nlm.nih.gov/pubmed/17742054http://arxiv.org/abs/1007.0315http://arxiv.org/abs/1007.0315http://onlinelibrary.wiley.com/doi/10.1002/prot.20106/abstract;jsessionid=7D0BBD01853416611E9A418F01E41F82.d02t02http://onlinelibrary.wiley.com/doi/10.1002/prot.20106/abstract;jsessionid=7D0BBD01853416611E9A418F01E41F82.d02t02http://nar.oxfordjournals.org/content/33/suppl_2/W284.longhttp://www.pnas.org/content/102/4/1029.longhttp://www.pnas.org/content/102/4/1029.longhttp://www.ncbi.nlm.nih.gov/pubmed/20441761http://www.ncbi.nlm.nih.gov/pubmed/20441756http://www.ncbi.nlm.nih.gov/pubmed/20441756
-
Christine J Bryson, Tim D Jones, and Matthew P Baker. Prediction
ofimmunogenicity of therapeutic proteins: validity of computational
tools.BioDrugs, 24(1):1–8, Feb 2010. doi:
10.2165/11318560-000000000-00000.URL
http://adisonline.com/biodrugs/pages/articleviewer.aspx?year=2010&issue=24010&article=00001&type=abstract.
Mary Ellen Bock, Claudio Garutti, and Concettina Guerra.
Discovery of similarregions on protein surfaces. J Comput Biol,
14(3):285–99, Apr 2007. doi: 10.1089/cmb.2006.0145.
S Yin, E. A Proctor, A. A Lugovskoy, and N. V Dokholyan. Fast
screening ofprotein surfaces using geometric invariant
fingerprints. Proceedings of the Na-tional Academy of Sciences,
106(39):16622–16626, Sep 2009. doi: 10.1073/pnas.0906146106. URL
http://www.pnas.org/content/106/39/16622.full.
N A Baker, D Sept, S Joseph, M J Holst, and J A McCammon.
Electrostatics ofnanosystems: application to microtubules and the
ribosome. Proc Natl Acad SciUSA, 98(18):10037–41, Aug 2001. doi:
10.1073/pnas.181342398. URL
http://www.pnas.org/content/98/18/10037.long.
3. Scientific Overview 34
http://adisonline.com/biodrugs/pages/articleviewer.aspx?year=2010&issue=24010&article=00001&type=abstracthttp://adisonline.com/biodrugs/pages/articleviewer.aspx?year=2010&issue=24010&article=00001&type=abstracthttp://www.pnas.org/content/106/39/16622.fullhttp://www.pnas.org/content/98/18/10037.longhttp://www.pnas.org/content/98/18/10037.long
-
4Results – DissertationEquivalents
Dissertation Equivalent I
Pfiffner P, Truffer R, Matsson P, Rasi C, Mari A, Stadler BM.
Allergen crossreactions: a problem greater than ever thought?
Allergy 2010; 65: 1536–1544.
Dissertation Equivalent II
Pfiffner P, Stadler BM, Rasi C, Scala E, Mari A. Allergen
clustering evaluated byin silico motifs or in vitro IgE microarray
testing using highly purified allergensmanuscript in
preparation.
35
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ORIGINAL ARTICLE EXPERIMENTAL ALLERGY AND IMMUNOLOGY
Allergen cross reactions: a problem greater than everthought?P.
Pfiffner1, R. Truffer1, P. Matsson2, C. Rasi3, A. Mari3,4 & B.
M. Stadler1
1University Institute of Immunology, Bern, Switzerland; 2Phadia
AB, Uppsala, Sweden; 3Center for Clinical and Experimental
Allergology,
IDI-IRCCS, Rome; 4Allergy Data Laboratories s.c., Latina,
Italy
To cite this article: Pfiffner P, Truffer R, Matsson P, Rasi C,
Mari A, Stadler BM. Allergen cross reactions: a problem greater
than ever thought? Allergy 2010;
65: 1536–1544.
Determination of specific IgE in patient sera is a valuable
test for allergologists (1). The number of potential allergens
is
steadily increasing (2) and suppliers of allergy tests are
provid-
ing ever longer lists of allergenic preparations to be used for
in
vitro assays. In most instances, allergens are still
relatively
crude extracts of organisms or parts thereof (3). Recently,
allergen diagnosis has improved by the use of highly
purified
natural or recombinant allergens and protein microarrays (3–
8). This may improve allergy diagnostics in the future.
Cross reactions are allergic reactions against other aller-
gens without prior sensitization. They have been extensively
studied and a handful of well-defined cross reactivity syn-
dromes are clinically highly important, e.g., the
pollen-food
syndromes (9). Cross reactions between recombinant aller-
gens are also documented (3, 7, 10). Thus, the immune sys-
tem might recognize common structures, allowing to predict
allergic reactions that have not been tested physically but
were derived by similarity.
We have previously shown that a bioinformatic approach is
capable to define a much lower number of potentially aller-
genic structures, termed motifs, than the number of known
protein sequences of allergens (11). These motifs represent
a
scaled profile over a window of 50 amino acids, derived from
all currently known allergen protein sequences. They serve
as
an identifier for evolutionary conserved protein domains.
Con-
sequently, if protein sequences match a given motif, these
pro-
teins are predicted to fold into the same protein domain and
therefore exhibit similar surface structures. We showed that
Keywords
allergens; bioinformatics; sensitization;
sequence motifs; specific IgE.
Correspondence
Pascal Pfiffner, University Institute of
Immunology, Sahli Haus 2, Inselspital, 3010
Bern, Switzerland.
Tel.: +41 31 632 22 89
Fax: +41 31 632 35 00
E-mail: [email protected]
Accepted for publication 4 May 2010
DOI:10.1111/j.1398-9995.2010.02420.x
Edited by: Reto Crameri
Abstract
Background: Cross reactions are an often observed phenomenon in
patients with
allergy. Sensitization against some allergens may cause
reactions against other seem-
ingly unrelated allergens. Today, cross reactions are being
investigated on a per-case
basis, analyzing blood serum specific IgE (sIgE) levels and
clinical features of
patients suffering from cross reactions. In this study, we
evaluated the level of sIgE
compared to patients’ total IgE assuming epitope specificity is
a consequence of
sequence similarity.
Methods: Our objective was to evaluate our recently published
model of molecular
sequence similarities underlying cross reactivity using
serum-derived data from IgE
determinations of standard laboratory tests.
We calculated the probabilities of protein cross reactivity
based on conserved
sequence motifs and compared these in silico predictions to a
database consisting of
5362 sera with sIgE determinations.
Results: Cumulating sIgE values of a patient resulted in a
median of 25–30% total
IgE. Comparing motif cross reactivity predictions to sIgE levels
showed that on
average three times fewer motifs than extracts were recognized
in a given serum
(correlation coefficient: 0.967). Extracts belonging to the same
motif group
co-reacted in a high percentage of sera (up to 80% for some
motifs).
Conclusions: Cumulated sIgE levels are exaggerated because of a
high level of
observed cross reactions. Thus, not only bioinformatic
prediction of allergenic
motifs, but also serological routine testing of allergic
patients implies that the
immune system may recognize only a small number of allergenic
structures.
Allergy
1536 Allergy 65 (2010) 1536–1544 ª 2010 John Wiley & Sons
A/S
4. Results – Dissertation Equivalents 36
-
this method of cross reactivity prediction is superior to
the
FAO/WHO rule, which states that a protein is allergenic if
it
has either an identity of at least six continuous amino acids
or
more than 35% sequence similarity over a window of 80 amino
acid residues. Especially in view of false positive matches
(67.3% of all Swiss-Prot proteins were predicted to be
allergenic by the FAO/WHO rule), the motif-based approach
performed much better (2.6% predicted to be allergenic)
(11).
Thus, the question remains whether the in silico prediction
of allergenicity may be confirmed by wet lab data. For this
pur-
pose, we have analyzed 5362 sera corresponding to 203 283
specific IgE determinations. We could demonstrate that the
degree of cross reaction was greater than ever thought.
Materials and methods
Serum samples
Data on 5456 serum samples were obtained by testing for
IgE using Phadia’s ImmunoCAP� (former UniCAP�, Phadia
AB, Uppsala, Sweden) systems. These are sandwich immuno-
assay systems where serum IgE antibodies react with anti-IgE
covalently coupled to the system in case of total IgE deter-
mination or with solid-phase bound allergen extracts to
determine specific IgE. Bound antibodies are detected and
quantified using enzyme-labeled anti-IgE-antibodies and
fluo-
rescence detection.
Tests were performed in the years from 1988 to 2006 in 17
different countries in different laboratories. Raw,
anonymized
IgE data (no age, sex, and other demographic and clinical
information) were collected as quality assurance; therefore,
no selection criteria were applied. Test results were
collected
in a clinical setting; most sera are presumably from
patients
with atopy.
All IgE levels are expressed in kilo units of antigen per
liter serum (kUA/l). Specific IgE levels >0.35 kUA/l (Class
I
and higher) were regarded as a positive test result, levels
>100 kUA/l were capped at 100 kUA/l, which affected 1578
values.
Included in the database were serum levels for 99 allergens
as well as the total IgE level. According to the
manufacturer,
the 99 allergen extracts used to determine the specific IgE
val-
ues are the 99 most tested allergens among a list of more
than 700 allergens available in Phadia’s catalog. Table 1
lists
the extracts and groups them into major subsets.
Sera had to be tested for total IgE, against at least 10
dif-
ferent allergens and yield at least one positive specific IgE
test
result to be allowed for the final database. With a total of
203 283 specific IgE tests, 5362 sera met our criteria and
were
used for the analysis.
Databases and software
We created a MySQL database to hold the serum data (MyS-
QL 5.0, obtained from http://www.mysql.com/). Allergen
protein sequences were extracted from the Allergome data-
base (http://www.allergome.org/ as of January 2009). MEME
3.5.7 (12) (obtained from http://meme.sdsc.edu/meme/) and
pftools 2.3.4 (13) (obtained from
http://www.isrec.isb-sib.ch/
ftp-server/pftools/) were used for the iterative allergen
motif
discovery. Perl 5.8.8 (http://www.perl.org/), PHP 5.2+
(http://www.php.net/), and R 2.8 (14) (http://www.r-project.
org/) scripts were created to extract the desired
statistical
calculations.
Allergen motifs
We performed the iterative allergen motif discovery
according
to Stadler and Stadler. (11) using 2189 protein sequences
from