From the Service de Conformation de
Macromolécules Biologiques et Bioinformatique, CP263, Centre de
Biologie Structurale et Bioinformatique, Université Libre de
Bruxelles, Blvd. du Triomphe, 1050 Brussels, Belgium, the
§ Département d'Immunologie, Inserm U567-CNRS UMR
8104-Université Paris V René Descartes, Institut Cochin IFR
116, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, and ¶ UPR
9021 CNRS Immunologie et Chimie Thérapeutiques, Institut de
Biologie Moléculaire et Cellulaire, 15 Rue René Descartes,
67084 Strasbourg, France
Received for publication, July 9, 2002, and in revised form, October 13, 2002
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ABSTRACT |
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An automatic protein design procedure was used to
compute amino acid sequences of peptides likely to bind the HLA-A2
major histocompatibility complex (MHC) class I allele. The only
information used by the procedure are a structural template, a rotamer
library, and a well established classical empirical force field. The
calculations are performed on six different templates from x-ray
structures of HLA-A0201-peptide complexes. Each template consists of
the bound peptide backbone and the full atomic coordinates of the MHC
protein. Sequences within 2 kcal/mol of the minimum energy sequence
are computed for each template, and the sequences from all the
templates are combined and ranked by their energies. The five lowest
energy peptide sequences and five other low energy sequences re-ranked
on the basis of their similarity to peptides known to bind the same MHC
allele are chemically synthesized and tested for their ability to bind
and form stable complexes with the HLA-A2 molecule. The most efficient
binders are also tested for inhibition of the T cell receptor
recognition of two known CD8+ T effectors. Results
show that all 10 peptides bind the expected MHC protein. The six
strongest binders also form stable HLA-A2-peptide complexes, albeit to
varying degrees, and three peptides display significant inhibition of
CD8+ T cell recognition. These results are rationalized in
light of our knowledge of the three-dimensional structures of the
HLA-A2-peptide and HLA-A2-peptide-T cell receptor complexes.
Major histocompatibility complex
(MHC)1 class I molecules are
cell surface glycoproteins, which consist of a highly polymorphic heavy
chain non-covalently associated with the invariant
The allelic specificity of peptide binding is introduced by additional
pockets containing MHC polymorphic residues, which accommodate a few
peptide anchor side chains (4). The HLA-A2 class I allele, expressed in
46% of the Caucasian population, preferentially binds peptides with
Leu, Ile, or Met at position P2 and Val, Leu, or Ile at position P9 of
the peptide. The central part of the peptide arches away from the floor
of the binding site so as to expose particular amino acid side chains
for T cell recognition (5). Correlation between binding capacity and
potency of CD8+ T cell responses has been established, and
it was shown that only HLA-peptide complexes of sufficient binding
affinity and stability can be loaded and efficiently recognized by T
cell receptors (TCR) of cytolytic CD8+ T cells (CTL)
(6-8).
The presentation and recognition of self-derived peptides by
autoreactive T cells are involved in the pathogenesis of many autoimmune diseases characterized by a break of tolerance to self. In
such disease processes, an attractive route for immunotherapy consists
in interfering with peptide presentation by blocking the HLA-binding
groove with antagonist peptide competitors. The use of synthetic
peptides and analogues of immunogenic peptides is hence of particular
interest in the treatment of autoimmune diseases and allergies as well
as in the development of peptide-based therapeutic vaccines
(9-11).
Alternatively, altered peptide ligands (APLs) presented by MHC
molecules and recognized in this context by the TCR can play the role
of partial agonists. APLs exhibiting TCR partial agonism can change the
cytokine patterns secreted by T cells; they can also affect the
expression levels of specific activation markers such as CD11a (LFA-1)
and CD25 (IL-2R) on the surface of T cells, and consequently influence
the nature of intracellular signaling. This can lead to anergy or
deletion of autoreactive T cells or on the contrary to the stimulation
of the other T cell subsets (12-16). Design of novel peptides with
both high HLA binding affinity and capable of targeting specific HLA
alleles can therefore also be used for modulating the immune system.
Many studies devoted to these goals have used theoretical approaches to
predict peptide sequences with the required affinity and selectivity
for specific MHC molecules (for review see Ref. 17). Most of these
approaches relied on sequence information alone (18). By and large,
they trained Neural Networks (19) or Hidden Markov Models (20) on
peptide sequences shown experimentally to bind, or not to bind, a
specific MHC allele, and then used these models to predict new peptide
sequences likely to specifically recognize the same protein. The
majority of these studies were done on class I molecules, where peptide
recognition is more specific and better understood than in the class II
molecules (19, 21).
In this paper we report an alternative approach to the prediction of
peptide sequences that bind to MHC class I molecules. This approach
uses an automatic procedure, implemented in the software DESIGNER (22)
for selecting amino acid sequences that are compatible with a given
peptide backbone conformation. This conformation is considered in the
context of the three-dimensional structure of a specific MHC class I
allele, the human HLA-A0201 allele, to which the peptide is bound.
DESIGNER has been developed as a general procedure for selecting
families of amino acid sequence likely to fold into the
three-dimensional structure defined by a given template (22, 23).
Selected sequences are those that minimize a fitness function, which
represents the protein free energy of folding. This function relies on
basic physical chemical principles that underlie molecular interactions and protein stability. It combines the all atom force field of CHARMM
(24, 25) with a simple empirical surface area-dependent hydration term (26). It is noteworthy that unlike most other studies
(27-30) the parameters of this fitness function have not been adjusted
to yield native-like sequences, and no constraints are imposed on the
amino acid composition of the designed sequences.
To predict peptide sequences that bind to the HLA-A0201 molecule,
DESIGNER was run on six different templates derived from representative
x-ray structures of HLA-A0201-peptide complexes deposited in the PDB
(31), as shown in Fig. 1. For each of the six templates, DESIGNER
produced all the sequences within 2 kcal/mol of the minimum energy
sequence, and the sequences computed for all the six complexes were
combined and ranked in order of increasing energy. In addition, the
computed low energy sequences were re-ranked on the basis of their
similarity to sequences of peptides retrieved from the MHCPEP data base
(32) and known to bind the same MHC allele.
The five best ranking peptide sequences produced by the pure
free-energy ranking and five other sequences obtained by the combined
energy and similarity ranking were retained for experimental analysis.
The 10 peptides were chemically synthesized and tested for their
ability to bind HLA-A2 molecules and to form stable HLA-A2-peptide
complexes. In addition, the most efficient binders were tested for
their capacity to inhibit the recognition of two known CD8+
T effectors.
The remarkable results reported here are that all 10 peptides were
found to actually bind the HLA-A2 molecules. Six peptides displayed
50-113% of the binding activity measured with the natural peptide
used as control, as well as significant MHC stabilization. Furthermore,
of the six most active peptides three displayed significant inhibition
of the CD8+ T responses.
In the following these findings are described in detail. They are
rationalized in light of our current understanding of the factors that
determine MHC-peptide interaction and TCR recognition, and their
implications for the design of peptides that modulate the MHC-mediated
immune response are discussed.
Computational Procedures
Selection of Template Structures--
The HLA-A0201 allele type
chosen for this work has the largest number of structures of
MHC-peptide complexes deposited in the Protein Data Bank (PDB) (31).
The April 2000 release of the PDB contained a total of 13 complexes
with 9-residue peptides with different sequences and conformations. To
ensure adequate exploration of sequence space for the bound peptide by
the sequence design procedure (see below), this procedure was applied
to several of these complexes, used as structural templates. To select
the templates, the peptide backbones from all 13 complexes were
superimposed, and whenever two peptide backbones were within 1-Å
r.m.s. deviation of one another, they were considered as too similar,
and one of the complexes (with the lowest resolution structure) was
discarded. This yielded six MHC-peptide complexes with the following
PDB codes: 1AKJ, 1BD2, 1B0G, 1HHG, 1HHK, and 1HHI, which were used as
template for the sequence calculations.
Automatic Sequence Design of MHC-binding Peptides--
The
peptide sequences are predicted using the procedure implemented in the
software DESIGNER, described previously (22). This procedure selects
amino acid sequences compatible with a given structural template. It
has two main components as follows: the fitness function, which
measures the fitness of a given sequence for the structure at hand, and
the optimization procedure, which selects highly scoring sequences from
a very large number of possibilities.
The fitness function is a quantity akin to the folding free energy.
This quantity is computed as the difference between the free energies
of the protein native folded state and a reference state used as a
model for the protein unfolded state.
The free energy of the folded state comprises an interaction energy
with terms computed using the standard all atoms molecular mechanics force field of CHARMM (24, 25), and an implicit hydration
term that depends linearly on the solvent-accessible surface area of
the solute (26). The electrostatic term is computed using a dielectric
constant of 8 and a switching function operating at a distance of 6-7
Å. Side chain conformations are modeled using the
backbone-dependent rotamer library of Dunbrack and Karplus (33). At each position, all natural amino acids are considered, except
prolines. The free energy of the reference state is computed as the sum
of the free energies of isolated amino acids, using exactly the same
force field.
To select amino acid sequences with lowest free energies, we used both
the Dead-End elimination and a heuristic procedure with 250,000 iterations (22).
Peptide sequences were selected by running DESIGNER on each of the six
representative MHC-peptide complexes of the HLA-A0201 allele. For each
complex, the bound peptide was stripped of its side chains. The bared
peptide backbone and both the backbone and side chains of
the MHC molecule were kept fixed at their crystallographic positions,
constituting the fixed structural template, in whose environment the
peptide sequence selection was performed. For each of the six
complexes, DESIGNER produced all the sequences within a 2 kcal/mol of
the minimum energy sequence, and the sequences computed for all the six
complexes were combined and ranked in order of increasing energy.
Whenever the bound peptide used as template contained a Pro residue,
this residue was re-modeled as an Ala residue, by removing the C Use of Profiles of MHC Binding Peptides to Re-rank Peptide
Sequences Selected by DESIGNER--
The above-described sequence
selection procedure should in principle produce a family of sequences
corresponding to peptides that form stable complexes with the
considered MHC allele. By having ranked these sequences in order of
increasing free energy, those most likely to yield stable complexes are
expected to appear at the top of the list. This is however not
guaranteed, given the inaccuracies of the sequence selection procedure.
It therefore seemed useful to apply in addition another selection
criterion, based on sequences of peptides that are known to bind the
same MHC allele. Such a criterion was obtained by scoring the predicted sequence against the profile derived from sequences of peptides known
to bind to the HLA-A0201 allele.
This profile was derived from sequences stored in the MHPEP data base
(32). Because this data base contains very similar peptide sequences,
the sequences were first clustered into groups with more than 50%
sequence identity. For each group the occurrence of each of the 20 natural amino acids was recorded at each sequence position (9 in all)
as a binary event (a given amino acid either occurs at a given position
or it does not). The number of times an amino acid occurred at a given
sequence position in all groups was then determined and used to derive
the position-specific amino acid frequency matrix, or probability
p(a,k), of having the amino acid a, at position
k along the sequence.
The similarity score Sim, of a peptide sequence against this
profile was computed as shown in Equation 1,
All the peptides selected by DESIGNER as having a free energy of 2 kcal/mol above the minimum were then re-ranked in order of decreasing
Sim value. Those with the highest Sim values were considered as most resembling the sequences of peptides known to bind
the considered MHC allele, and hence as most likely to yield a stable complex.
Experimental Procedures
Peptide Synthesis
Peptides were synthesized by Fmoc chemistry with the stepwise
solid-phase methodology using a multichannel peptide synthesizer (34).
Protected amino acids were coupled by in situ
activation with (benzotriazol-1-yloxy)tris-(dimethylamino)phosphonium
hexafluorophosphate, and N-Fmoc de-protection was performed
as described previously (34). Side chain deprotection and cleavage of
peptides from the solid support was performed by treatment with reagent
K (82.5% trifluoroacetic acid, 5% phenol, 5% water, 5% thioanisole,
2.5% 1,2-ethanedithiol) for 2 h 30 min at 20 °C (35). Peptides
were purified by reversed-phase HPLC (RP-HPLC) using a PerkinElmer Life
Sciences preparative HPLC system on an Aquapore ODS 20-µm column
(100 × 10 mm). Elution was performed with a linear gradient of
aqueous 0.1% trifluoroacetic acid (A) and 0.08%
trifluoroacetic acid in 80% acetonitrile, 20% water (B) at a flow
rate of 6 ml/min with UV detection at 220 nm.
Analytical RP-HPLC was run on a Beckman Instruments (Gagny, France)
with a Nucleosil C18 5-µm column (150 × 4.6 mm) using a linear
gradient of 0.1% trifluoroacetic acid in water and acetonitrile containing 0.08% trifluoroacetic acid at a flow rate of 1.2 ml/min. Matrix-assisted laser desorption and ionization time-of-flight spectra
were obtained on a Protein TOFTM mass spectrometer (Bruker,
Wissembourg, France).
Preparation of HLA-A2 Molecules
HLA-A2 molecules were purified as described previously (36) from
Epstein-Barr virus-transformed B cell lines by affinity columns coupled
to BB7.2 antibodies (Ab) directed against HLA-A2 molecules (HB82, ATCC,
Manassas, VA) and frozen at Detection of Peptide/HLA Interactions
Direct Binding Test--
The conditions of the test were
described previously (36). Briefly, 50 µg of denatured HLA molecules
in 2.5 ml of PBS containing all the additives described above were
divided in aliquots containing 1 µg of HLA and incubated with
exogenous peptide at 10 Stability of Peptide-HLA Complexes--
HLA denaturation
and renaturation with 10 Inhibition of T Cell Responses
Generation of Effector T cells--
Human T cell effectors were
generated as described previously (16) after in vitro
stimulation of peripheral blood mononuclear cells (PBMCs) from
HLA-A2-ositive donors with synthetic peptides. Unfractionated PBMCs
were seeded in 24-well plates with 1 µg/ml tetanus toxoid and 1 µM peptide M.58-66 or MART (Leu-27) 26-35 (ELAGIGILTV).
Interleukins IL-7 and IL-2 were added as reported previously.
Anti-M.58-66 T cell effectors were obtained after 3 weekly
stimulations and anti-MART (Leu-27)-(26-35) T cell effectors after 6 stimulations.
T Cell Recognition of HLA-A2-Peptide Complexes--
This was
assessed using an Enzyme-linked Immunospot (ELISPOT) assay detecting
secretion of IFN- Inhibition Assay--
Presenting T cells were sensitized
with 5 × 10 Prediction of Peptide Sequences That Bind MHC Class I
Molecules--
The sequence design procedure DESIGNER was run using as
templates the three-dimensional structures of each of the six
HLA-A201-peptide complexes listed in Table
I and illustrated in Fig.
1. This yielded between 98 and 702 different sequences for individual templates, whose energies were
within a 2 kcal/mol window of the minimum energy sequence computed for
each template. Combining the results from all six templates the number
of low energy peptide sequences totaled 1430.
From these sequences, 10 were selected as the most likely candidates
for HLA-A2 binding. These are numbered 1-10 and are listed in
Table II. Sequences 1-5
correspond to the highest ranking (or lowest energy) sequences from
among all the 1430 designed sequences. Sequences 6-10 are those from
among the same 1430 peptides, but which display the highest sequence
similarity score s, relative to peptide sequences known to
bind to the HLA-A0201 allele. This similarity score was computed using
a sequence profile derived from 903 HLA-A0201-binding, 9-residue-long
peptide sequences stored in the last release of the MHCPEP data base
(32), as described under "Materials and Methods."
Table II lists for all the selected sequences, the energy computed by
DESIGNER, the value of their similarity score Sim, and the
maximum identity score with the HLA-A0201-bound peptide sequences in
the MHCPEP data base. As expected, the first five peptides have
energies ranging from
Inspection of the 10 sequences clearly reveals that they all have one
of the anchor residues required for HLA-A2 binding (Leu, Ile, Met, or
Val in position 2 of the peptide (P2), and Val, Leu, Ile, Ala, or Met
at the peptide C terminus (P
It is furthermore noteworthy that the first five lowest energy peptides
have very similar sequences, with only the central positions 4 and 5 and to a lesser extent position 1 (which is aromatic, but either
Phe or Tyr) displaying some variability. In the last five
highest scoring peptides the sequence variability is more pronounced
and spreads over positions 3-8. All the predicted peptide sequences in
Table II except for one (FLLRDRIFV, peptide number 3) display at most
55% sequence identity to known MHC binders, corresponding to a
moderate statistical significance (p value of
6.1e
To illustrate the variability of the designed sequences and how it
compares with that of their natural counterparts from the MHCPEP data
base, Table III lists the
position-specific amino acid frequency matrices, or profiles, of both
sequence ensembles. Inspection of these profiles shows clearly that the
natural sequences of the HLA-A2 binders are significantly more diverse
than the designed sequences, in all positions except for the anchoring
residues, which are quite conserved in both sequence groups. The
diversity of the natural sequences in those positions is too large for
deriving a meaningful consensus sequence from which sequences of HLA-A2 binders can be predicted. The position-specific entropy computed from
the corresponding amino acid frequencies, as detailed in the legend of
Table III and listed in the last row of this Table, reflects well these
properties. In the MHCPEP sequences this entropy is quite large (near
its maximum value of 2.996 =
Close inspection of the two profiles in Table III furthermore reveals
that the designed sequences reproduce several of the trends observed in
the natural sequences. For instance, Asp occurs most often in position
4, in both the natural and designed peptides; Glu occurs frequently in
positions 4, 5, and 8, whereas Phe occurs most often in position 1.
We thus see that although our sequence design procedure yields
predictions that could not have been deduced readily from the analysis
of the natural peptide sequences, these predictions nonetheless reproduce key features of these sequences.
Inherent in our procedure for predicting peptide sequences is the fact
that it also predicts their most likely three-dimensional conformation
in the environment of the MHC molecule. The conformations of the lowest
energy peptides predicted using the 1HHK and 1B0G templates,
respectively, are displayed in Fig. 2.
These conformations are shown superimposed onto those of the x-ray
structures of the corresponding bound peptide in each complex. We see
that although the amino acid sequences of the predicted peptides are quite different from those in the two x-ray structures, the side chain
conformations of equivalent residues are quite similar and feature very
similar values of the
This result cannot be entirely attributed to the fact that the
predictions of the peptide amino acid sequences and conformations were
performed in the framework of the fixed template, with the latter
including the atomic coordinates of native peptide backbone and the
full three-dimensional structure of MHC molecule. Indeed the design
calculations were not carried out on a single template but on six
different templates, in which equivalent protein side chains in the
peptide-binding groove exhibit r.m.s. deviations of about 1 Å, as
illustrated in Fig. 1. It is well known that the backbone and side
chain conformations of an amino acid residue are closely coupled (33,
37, 38), and the similarity of the predicted and native amino acid side
chain conformations is thus most likely due to constraints imposed by
native peptide backbone templates.
However, the constrained native MHC environment seems to have played a
more important role in the selection of the amino acid type at each
position. Support for this assumption comes from test calculations in
which DESIGNER was applied to the same MHC-peptide templates, still
keeping the backbone coordinates (of both protein and bound peptide)
fixed, but allowing the MHC side chains to adjust their conformations
(but not their amino acid sequence), during the design procedure. This
yielded a set of minimum energy peptide sequences, which had higher
energies than those listed in Table II and featured none of the
expected amino acid types at the anchor residues. These peptide
sequences were therefore not considered for further analysis, whereas
the 10 predicted peptide sequences of Table II were synthesized, and
their binding to MHC and ability to impair CD8+ T cell
recognition were analyzed.
Binding of Peptides to HLA-A2 Molecules--
To test
binding to HLA-A2, the 10 selected peptides, numbered 1-10, were added
at various concentrations to purified HLA-A2 molecules as described
under "Materials and Methods." Two CD8+ T cell epitopes
from the influenza virus were also tested. The NP 383-391 peptide
(peptide 11) from the viral nucleoprotein which is presented by
HLA-B27 was used as a negative control, and the M.58-66 peptide
(peptide 12) from the matrix, presented by HLA-A2, was used as a
positive control for binding.
The results presented in Fig. 3,
A and B, show that at high concentration of
10 Stability of HLA-A2-Peptide Complexes--
Because it has been
argued that in viral systems immunogenicity correlates better with
stability of HLA-peptides complexes (7), we also evaluated the
stability at 37 °C of the formed complexes. This assay was performed
using the most potent HLA-A2 binders, namely peptides 2 and 5-9. To
study their ability to stabilize the HLA-A2 molecule, they were added
at a concentration of 10 Inhibition of CD8+ T Cell Responses--
The most
potent binders 2 and 5-9 were also assessed as inhibitors of
HLA-A2-restricted CD8+ T cell responses. Inhibition of T
cell response was visualized in an ELISPOT test detecting IFN-
Interestingly, peptides 2 and 5 share very similar sequences, the only
difference being at position 1, which is Phe in peptide 2 and Tyr in
peptide 5. The presence of Phe at position 1 thus seems important for
peptide binding to HLA-A2 and for increasing its capacity to inhibit T
cell recognition. Peptides 6, 8, and 9, which were found to be very
potent binders at low peptide concentration, have also very similar
sequences with only one variation at position 4 or 5. It has to be
noted that although peptide 6 gave very stable HLA-A2 complexes, it did
not display the highest efficacy for inhibiting T cell recognition. In
summary, peptides 5, 8, and 9 are the best candidates for inhibition of
CD8+ T responses.
In this paper we used an automatic procedure to compute the amino
acid sequences of peptides that are likely to bind the HLA-A2 MHC class
I allele. A first remarkable result described here is that out of the
10 highest scoring peptides selected by our procedure, all were shown
to actually bind the expected MHC protein. The six strongest HLA-A2
binders also promoted the assembly of stable HLA-A2-peptide complexes,
albeit to varying degrees, and three peptides displayed significant
capacities to inhibit CD8+ T cell recognition (for a
summary of these results see Table S1 of the Supplemental Material).
Of the six most active predicted peptides, peptides 2 and 5 were the
best ranking candidates for MHC HLA-A2 binding, selected from among a
very large number of sequences solely on the basis of the fitness
function used by DESIGNER. It is therefore quite satisfying that
peptide 5, in particular, exhibits almost the same binding as a natural
CD8+ epitope, the M.58-66 peptide from the influenza virus
matrix, and significant inhibitory properties. Indeed, this suggests
that our fitness function, which is based on the well established
CHARMM22 force field (24) and represents a quantity akin to the peptide binding free energy, is an effective selection criterion for this design problem.
Three other predicted peptides namely, peptides 6, 8, and 9, display
similar or somewhat higher MHC binding than the natural epitope
M.58-66, and two of these (8 and 9) also exhibit efficient inhibition
of T cell recognition. These peptides belong to the second group of
peptides in Table II. They represent the low energy sequences selected
by DESIGNER, which also display the highest similarity score against
the known set of HLA-A0201-binding peptides in the MHCPEP data base.
Interestingly, all five lowest energy sequences have been computed from
the same template (that of the 1HHK PDB entry), and the values of their
DESIGNER free energies differ little (Table II). The most salient
difference between the sequences of peptides 6, 8, 9, and those of
peptides 1-5, selected on the basis of the DESIGNER free energy alone,
is that the former have Leu as the anchor residue at P9, like in the
M.58-66 epitope, whereas the latter have Val in this position.
Interestingly, this position is completely buried in the
peptide-binding groove of MHC (39). Another notable difference is in
the amino acid residue at P8. Peptides 6, 8, 9 have a Lys in this
position, whereas peptides 1-5 feature a Phe, which has very different
chemical properties. These two differences are probably at the origin
of the different activity patterns of these peptides.
Linking Observed MHC-binding Properties to Structural
Features--
In an attempt to link observed properties such as MHC
binding and stabilization to structural features of the predicted
peptides, we computed for each predicted peptide sequence and structure the number of intermolecular H-bonds formed by its side chains and the
surface area buried upon complex formation. These quantities are listed
in Table II.
We could readily establish that the correlation of the activity
properties with the number of H-bonds was poor, whereas that with the
buried surface area was better. We see indeed that peptide 6, which
displays the highest binding and stability activities of all 10 predicted peptides, forms no H-bonds with the MHC molecule, whereas
peptide 10, whose biological activity is significantly weaker, forms 4 hydrogen bonds. On the other hand peptide 6 buries the largest surface
area of all peptides in the complex, whereas peptide 10 buries the next
to lowest surface area. The other two peptides with highest buried
surface area are 8 and 9, the next most active peptides following
peptide 6. This seemingly good correspondence between the measured
activity and the buried surface area is not too surprising in view of
the fact that the peptide/MHC interactions are primarily hydrophobic in
nature. Interestingly, a near anti-correlation between the buried
surface area in the MHC-peptide interface and the number of H-bonds
formed between the protein and the peptide side chains is also observed
in the set of native MHC-peptide crystal structures used here as
templates for the sequence design (Table I).
Fig. 6 displays the conformation of one
of the most active predicted peptides (peptide 6) superimposed onto the
backbone of the natural M.58-66 epitope bound to the HLA-A2 molecule,
as in the 1HHK PDB entry. As expected, the backbone conformation of
peptide 6, which also originates from the 1HHI structure, used as
template, is quite similar to that of the bound M.58-66 peptide. The N-
and C-terminal residues of both peptides are particularly well
superimposed, and the side chains of the two anchor residues at P2 and
P9 are completely buried in pockets inside the MHC molecule. On the
other hand the central portion of the peptide backbones in the vicinity
of P5 (a Trp in peptide 6) displays slightly different conformations.
In addition, in peptide 6 the Trp side chain at P5 sticks into the
solvent and makes no interaction with the MHC molecules. On the other
hand, the corresponding Phe side chain in M.58-66 points into the
peptide-binding groove and makes hydrophobic interactions with the
protein. Despite this difference the M.58-66 epitope buries overall a
similar surface area in the native complex as peptide 6 does (1087.3 versus 1112 Å2, respectively). Thus in this
predicted peptide the N and C termini contribute significantly to
HLA-A2 binding, whereas in the M.58-66 epitope, the middle portion
contributes significantly.
Origins of the Observed Inhibition of HLA-A2-restricted
CD8+ T Cell Response--
An interesting property of some
of the designed peptides is that they impair activation of the human
CTL, mediated by the two types of T cell effectors tested here, those
recognizing the MART-(26-35) and the M.58-66 epitopes, respectively,
in the context of HLA-A2. Because CTL activation requires
recognition of the MHC-peptide complex by the TCR, it seemed worthwhile
to examine the only known crystal structure of an HLA-A2-peptide-TCR
ternary complex (see Ref. 5; PDB code 1BD2), for possible clues concerning the features leading to the inhibitory properties of the
designed peptides.
In this complex, almost the entire peptide is buried in the TCR/MHC
interface, with a majority of the contacts with the peptide being made
by the CDR1
In the HTLV-1 Tax peptide in the known structure of ternary complex and
in the M.58-66 and MART-(26-35) peptides used here in the test with T
cell effectors, position 5 is occupied by aromatics (Phe and Tyr) or
Gly and Tyr, position 8 by Thr or Tyr, position 4 by Gly, and position
6 by Val, Pro, or Ile. The correspondence, between the MART peptide,
which has 10 residues and the 2 other natural peptides was established
using structure superpositions of the peptide backbones in the
corresponding crystal structures taken from the PDB. This showed that
residues 1-4 and 7-10 of the MART peptide were structurally
equivalent, respectively, to residues 1-4 and 6-9 of the HTLV-1 Tax
and M.58-66 peptides. But position 5 in the latter peptides was roughly
overlapping with positions 5 and 6 of the MART peptide.
In contrast, in the three predicted peptides with highest inhibition
activity (peptides 5, 8, and 9), the corresponding positions feature
polar and often charged side chains (Tables II or III). Indeed position
5 is Asp in all three peptides, and position 8 is Phe in peptide 5 and
Lys in peptides 8 and 9, whereas residues 4 and 6 are, respectively,
Gln and Arg in peptide 5, Arg and Gln in peptide 8, and Gln in peptide
9. The selection of such polar and charged side chains in these
positions by DESIGNER is energetically advantageous, because the design
calculations were performed in the absence of the bound TCR molecules,
leaving the side chains at these residues accessible to the solvent.
Thus, the peptides with largest inhibitory activity seem to be those
that have a similar or somewhat higher affinity for the HLA-A2 than the
natural peptides, and which feature either 2 or 3 charged side chains
in the 4 positions mentioned above, that participate in TCR binding.
By using the atomic coordinates of the ternary HLA-A2-peptide-TCR
complex, we modeled the conformations of the M.58-66 and MART-26-35
peptides that were used in the inhibition tests and those of the three
predicted peptides with highest inhibitory activity of the CTL
response. Fig. 7A shows the
conformations of the side chains of residues 5 and 8 of the Tax HTLV-1
peptide and the two natural peptides in the environments of ternary
complex. The conformations of the corresponding residues in the three
designed inhibitory peptides are shown in Fig. 7, B-D, side
by side the side chain conformations in the natural and designed
peptides, respectively, for residues 4 and 6. Inspection of these
figures suggests that the charged and often longer amino acids of the designed peptides are likely to interfere with TCR recognition, due
primarily to unfavorable electrostatic and solvation effects. No
attempt was made here to optimize the ternary complexes modeled with
the designed peptides.
In conclusion our study demonstrates that automatic protein design
procedures can be successful in the design of peptides that bind with
high enough affinity to one protein so as to impair specific
protein-protein recognition mediated by these peptides and
involving a second protein. This opens up the prospect for the use of
these procedures as a general tool for investigating and modulating
protein-peptide and protein-protein interactions.
Although this success may be attributed, at least in part, to advances
in the protein design procedures, the constraints imposed by the
MHC-binding groove on the choice of peptide side chain conformations
and amino acid types have clearly been helpful. These constraints
result from the use of accurate structural templates (the high
resolution MHC-peptide complexes) and from the fact that the peptides
form extensive interactions with the template. We have seen indeed that
relaxing these constraints even partially, for instance by allowing the
side chains of the MHC-peptide binding groove to adjust their
conformation during the design procedure, yielded designed peptides
that lacked the anchor residues and, most likely, the expected binding
properties as well.
Generalizing the approach described here to cases where the atomic
coordinates of the MHC molecule are less accurate, as in the case of
models derived by homology (40), or to MHC class II molecules, where
the interactions with the peptide are believed to be less constraining,
is therefore likely to be more
difficult2 and will require
further developments.
INTRODUCTION
TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES
2-microglobulin. Each single MHC allele binds to a broad
array of up to 104 different peptides to allow the immune
system to both recognize foreign antigens and to remain tolerant to
self-derived peptides (1). Crystallographic studies of MHC molecules
and their complexes with cognate peptides have shown that the class I
peptide-binding site is a groove formed by two
-helices lying across
an eight-stranded
-pleated sheet which accommodates peptides of
8-10 residues long (2, 3). The N and C termini of the peptides bind
two pockets at each end of the cleft, where they form extensive
hydrogen-bonding interactions with conserved MHC residues.
MATERIALS AND METHODS
TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES
and
C
atoms; the backbone was not re-modeled.
where wk are position-specific
weights whose values were set to 1 for all k.
(Eq. 1)
80 °C. HLA molecules were denatured in
phosphate-buffered saline (PBS) containing 12.5 mM NaOH (pH
11.7) and 1.5 M urea for 1 h at 4 °C. The heavy chains and
2-microglobulin (
2m) were separated from
endogenous peptides on a Sephadex G-25 column (PD10, Amersham
Biosciences) equilibrated in PBS containing 0.05% Tween 20, 10 mg/ml
bovine serum albumin, 1 mM phenylmethylsulfonyl fluoride,
10 µg/ml trypsin inhibitor, 3 µM sodium azide (PBS-Tw).
Then 2 µg/ml exogenous
2m (Sigma) and 6 mM CHAPS were
added just before addition of exogenous peptide.
5-10
9
M, in Eppendorf microtubes for 2 h at room temperature
and then for 24 h at 4 °C. Each aliquot was further divided
into 2 wells of a microtiter plate (100-µl well, Maxisorp Nunc)
coated with anti-HLA-A2 BB7.2 Ab (10 µg/ml in PBS) and incubated for
20 h at 4 °C. Correctly folded HLA complexes were assessed
using anti-
2m monoclonal Ab M28 coupled to alkaline phosphatase and
4-methylumbelliferyl phosphate as a substrate (Sigma). Fluorescence was
measured at 355/460 nm (VICTORTM, Wallac, Evry, France).
The positive control was a viral CD8+ epitope presented by
HLA-A2, M.58-66 (GILGFVFTL) from matrix of influenza virus, and the
negative control was an epitope presented by HLA-B27, NP.383-391
(SRYWAIRTR) from the nucleoprotein of influenza virus.
6 M exogenous peptide
were performed as described above. After an overnight incubation, unbound peptides were removed by centrifugation on
NANOSEPTM 10K (Pall Filtron, Northborough, MA). Samples
were diluted in PBS-Tw and aliquoted into Eppendorf microtubes for
further incubations at 37 °C for 1, 3, 5, 24, or 48 h. One
aliquot (time 0) was tested immediately to measure the maximum number
of complexes. The incubations with anti-HLA-A2 BB7.2 Ab and anti-
2m
M28 Ab, respectively, were performed in microplates at 37 °C for
1 h. Final detection was performed as described above.
(36). 96-well nitrocellulose plates (Millipore,
Bedford, MA) were coated with 2 µg/ml mouse anti-human IFN-
monoclonal (number 1598-00 Genzyme, Rüsselheim, Germany).
PBMC, either freshly isolated or thawed, were cultured overnight in
complete medium and plated in triplicate at serial dilutions (3 × 105-104 cells per well). Appropriate stimuli
were then added, and the plates were incubated for 20 h at
37 °C in 5% CO2. After washing, the cells were
incubated with 100 µl of rabbit polyclonal anti-human IFN-
antibody diluted 1:250 (IP500, Genzyme), then with a biotinylated anti-rabbit immunoglobulin G diluted 1:500 (Roche Molecular
Biochemicals), and finally with alkaline phosphatase-labeled extravidin
(Sigma). Spots were developed by adding chromogenic alkaline
phosphatase substrate (Bio-Rad), and colored spots were counted in a
stereomicroscope. A result was considered as significant when the
numbers of spots were at least twice the background (the value given by
negative peptides) and were proportional to the numbers of plated
cells. Frequencies of IFN-
spot-forming cells were calculated.
Positive controls for interferon detection consisted of 6 wells
containing 300-1000 cells stimulated with 50 ng/ml phorbol myristate
acetate and 500 ng/ml ionomycin. This strong mitogenic stimulus
verified that freezing and thawing did not introduce artifacts and was an indirect check of overall T cell viability. Negative controls for
immune recognition consisted of epitopes derived from various viruses
(for example, peptide Tax 11-19 from HTLV-1, which associates with
HLA-A2) and never elicited a significant response compared with PBMC
incubated in medium alone.
9 M peptide MART
(Leu-27)-(26-35) and 10
8 M peptide M.58-66,
respectively. Peptides being tested as inhibitors were simultaneously
added at molar concentrations varying from 0.1 to 1000× compared with
the respective cognate peptides. ELISPOT assay was then performed as
described above.
RESULTS
TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES
Template structures of MHC-peptide complexes used in the prediction
View larger version (85K):
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Fig. 1.
Six HLA-A201-peptide complexes used as
templates for the automatic peptide sequence design procedure.
Displayed are the superimposed structures of the six known
HLA-A201-peptide complexes used in the sequence design procedure. The
MHC molecules are displayed in gray, and the backbones of
the different antigenic peptides are displayed in color. The
design calculations were performed on each of the six individual
structural templates. The template comprised the bound peptide backbone
and the full MHC protein structure. It can be seen that the
conformations of HLA-A2 moieties are quite well superimposed,
especially in the peptide-binding grove. On the other hand, the
backbones of six peptides, 1AKJ (red), 1B0G
(blue), 1BD2 (yellow), 1HHG (green),
1HHI (cyan), and 1HHK (purple), display
appreciable dispersion, in particular the central portion of the
peptides, which contributes most to the interactions with the TCR
(5).
Highest scoring sequences of peptides likely to bind the HLA-A2
molecule
55.391 to
54.811 that are about 1.8-6.6 kcal/mol lower than the last 5 peptides. In contrast, the latter all
have s values exceeding 1.2, whereas the lowest energy
peptides have s values below 1. Interestingly, all five
lowest energy sequences and three of the five peptides with highest
Sim values have been obtained using the template from the
1HHK RSCB-PDB entry. The remaining two sequences with high
Sim values have been designed using the 1B0G entry.
)). In particular, all the sequences
feature a Leu residue in P2 but either Leu or Val at P
. This
difference in anchor residue variability is most likely related to the
difference in conformational variability of the corresponding binding
pockets in the MHC molecule. We could verify that the r.m.s. deviations
of MHC residues lining the
pocket in the six considered templates
are on average larger (0.8-1.3 Å) than those of the B pocket
(0.2-0.6 Å).
4).
log (1/20)) in all positions save
those of the two anchor residues. In the designed sequences, the
position-specific entropy is lower for all positions, yet that of the
middle residues (positions 5-7) is nearly twice as high as the entropy
of the two anchor residues.
Sequence profiles of HLA-A0201 binding peptides in MHCPEP and those
predicted by DESIGNER
1 angles. This similarity is particularly
striking in positions, such as the anchor residues, for which the
predicted and native amino acids are of the same or of closely similar
types.
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Fig. 2.
Comparison of the three-dimensional
structures of the two lowest energy peptides predicted by DESIGNER, and
the experimental x-ray structures of bound peptides. A,
structures of the lowest energy peptide predicted using the 1HHK
complex as template. Its sequence is FLLQWRIFV (see row 1 of Table II
for detail). The sequence of the MHC-bound peptide in 1HHK is
LLFGYPVYV. B, structure of the lowest energy peptide
predicted using the 1B0G complex as template. Its sequence is ALFDRFAEV
(see row 7 of Table II for detail). The sequence of the bound peptide
in 1B0G is ALWGFFPVL. The main chain structures are shown in
blue, and the side chains are shown in green
(template) and gray (predicted peptide).
5 and 10
6 M, all 10 peptides
bind significantly to HLA-A2 albeit with different efficiencies. At the
low concentration of 10
8 M, only a subset of
the peptides shows significant binding. By comparing several
experiments, it was concluded that the efficient binders, given in the
order of decreasing relative binding, are peptides 6, 9, 8, 5, 7, and
2, displaying respectively, 131, 116, 111, 98, 83, and 50% of M.58-66
binding used as an internal reference (peptide 12). Interestingly,
peptide 6, exhibiting the highest HLA-A2 binding at 10
8
M, displayed reduced binding at higher concentrations of
10
6 and 10
5 M. A similar
behavior was observed with the natural M.58-66 cognate peptide (Fig.
3A).
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Fig. 3.
Binding of peptides to purified HLA-A2
molecules. Aliquots of 1 µg of HLA-A2 were incubated in tubes
with each peptide at concentrations varying from 10 9 to
10
5 M. HLA-A2-peptide complexes were retained
on BB7.2 Ab-coated microtiter plate wells and revealed by anti-
2m Ab
coupled to alkaline phosphatase. Results are expressed as arbitrary
fluorescence units at 355/460 nm measured after 2 h of incubation
with the methylumbelliferyl phosphate substrate. A, binding
measured for peptides 1-5. B, binding measured for peptides
6-10. Both panels also show the results for two control peptides,
namely peptide 12, corresponding to the epitope M.58-66 from influenza
virus matrix presented by HLA-A2, used as positive control, and
peptides 11 corresponding to the epitope NP.383-391 from nucleoprotein
of influenza virus presented by HLA-B27, known not to bind HLA-A2, used
here as negative control.
6 M to purified
HLA-A2 molecules, and maximal binding for each peptide (considered as
100% for calculation) was obtained at time 1 (defined as 0 h).
Complexes were then incubated at 37 °C for various times (1, 3, 5, and 24 h), and the amount of remaining complexes was quantified.
The maximal HLA-A2 stability was obtained with peptide M.58-66 (peptide
12), then, in decreasing order, with peptides 6, 2, 5, 9, 8, and 7 (Fig. 4). It is noticeable that HLA-A2
complexes formed with peptide 6 were stable over a 24-h period (about
75% remaining versus 90% in the case of the particularly
stable HLA-A2-M.58-66 peptide complexes). The NP 383-391 peptide, known
not to bind HLA-A2 and used as a negative control, did not produce
stable HLA-A2 complexes (data not shown).
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Fig. 4.
Stability of peptide-HLA-A2 complexes at
37 °C. The complexes were formed between peptides added at
10 6 M and aliquots of 1 µg of purified
HLA-A2 molecules. The maximal numbers of complexes (100% binding) were
obtained after a stabilization phase at 4 °C (time 1 = 0 h). Results obtained after various incubation times at 37 °C (from
2-5 corresponding to 1, 3, 5, and to 24 h, respectively) are
expressed as the percentage of the maximum number of complexes for each
peptide.
secretion. Two types of T cell effectors both restricted to HLA-A2,
were used, one recognizing the melanoma-specific MART 26-35 peptide,
and the other recognizing the influenza peptide M.58-66. In the test
designed to measure the ability of A2 binders to compete with the
binding of MART-(26-35) peptide to HLA-A2, peptides 2 and 5-9 were
tested and ranked according to their decreasing inhibition efficiency.
Peptides 9 and 8 were the most potent inhibitors followed by peptides
5, 6, and 2 in this order (Fig.
5A). In the M.58-66 peptide
system, among the four peptides tested (peptides 5, 6, 7, and 9),
peptides 5 and 9 gave the most significant inhibition, whereas peptides
6 and 7 were marginally efficient (Fig. 5B). As control, we
verified that each peptide added alone was not spontaneously recognized by the two types of T cell effectors (data not shown).
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Fig. 5.
Inhibition of T cell recognition.
Inhibition of T cell recognition in an ELISPOT assay detecting IFN-
secretion by stimulated T cells. A, results with human T
cells directed against peptide MART-(26-35) presented by the HLA-A2;
B, results with human T cells directed against peptide
M.58-66 presented by the HLA-A2. PBMC were sensitized with
5 × 10
9 M peptide MART-(26-35) or
10
8 M peptide M.58-66, respectively, and
competitor peptides were simultaneously added at the specified
concentrations (representing a molar excess of 0.1 to 1000× compared
with the cognate peptide). The maximal number of spots (defined as
100%) was 5900 for 106 cells for anti-MART-(26-35) T cell
effector and 304 for anti-M.58-66 T cell effector. Results are
expressed as the percentage of inhibition.
DISCUSSION
TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES
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Fig. 6.
Superimposed structures of a natural epitope
and one of the most active predicted peptides in the HLA-A2 binding
pocket. Stereo picture of one of the most active predicted
peptides (peptide 6) superimposed onto the natural M.58-66 peptide
epitope in the HLA-A2 binding pocket (PDB code 1HHI). The peptides are
represented by stick models and colored in orange
(predicted peptide) and green, respectively (natural
epitope); the HLA-A2 molecule is represented by its molecular surface,
represented in light gray. Figures were generated using the
WebLab-Viewer software from Accelrys (MSI)
and CDR1
loops of the TCR (5). This means that the
TCR molecule buries all or nearly all the parts of the peptide that are
not already buried in the binary MHC-peptide complex. These parts
consist primarily of the side chains of residues 5 and 8 and to a
lesser extent of residues 4 and 6.
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Fig. 7.
Key residues of the peptide moiety in the
context of MHC-peptide-TCR ternary complex. A, amino acid
residues 5 and 8 of three natural HLA-A2 epitopes shown bound at the
MHC-peptide-TCR. The natural peptides are the Tax HTLV-1 peptide from
the known MHC-peptide-TCR complex (PDB code 1BD2) (red) and
the two peptides, MART-(26-35) (green) and M.58-66
(blue), used here to elicit CTL responses. In these peptides
position 5 is occupied by aromatics (Phe and Tyr) or Gly and Tyr and
position 8 by Thr or Tyr. The correspondence between the MART peptide,
which has 10 residues, and the two other natural peptides was
established using structure superposition of the peptide backbones in
the corresponding crystal structures taken from the PDB. This showed
that residues 1-4 and 7-10 of the MART peptide were structurally
equivalent respectively, to residues 1-4 and 6-9 of the HTLV-1 Tax
and M.58-66 peptides. But position 5 in the latter peptides was roughly
overlapping with positions 5 and 6 of the MART peptide. Both residues
are therefore displayed for the MART peptide. B, the same as
above, but for the three predicted peptides (numbers 5, 8, and 9) shown
here to have the highest inhibitory activity of the TCL response.
Residues of peptide 5 are in yellow, those of peptide 8 in
orange, and of peptide 9 in cyan. In these
predicted peptides position 5 is occupied by Asp and position 8 by Phe
or Lys. C, amino acid residues 4 and 6 of the same three
natural peptides bound at the MHC-peptide-TCR interface as in
A; the color scheme is as in A. In these peptides
position 4 is occupied by Gly and position 6 by Val, Pro, or Ile. The
correspondence, between the MART decapeptide and the other 2 nanopeptides, was established from structure superimpositions of the
peptide backbones (see above). D, amino acid residues 4 and
6 of the same three predicted peptides bound at the MHC-peptide-TCR
interface, as in B; the color scheme is as in B.
In these predicted peptides positions 4 and 6 are both occupied by
either Gln or Arg. The interface is delimited by the helical portions
of the MHC moiety illustrated as a ribbon diagram in light
gray, and the CDR loops of the TCR molecules displayed as ribbons
(darker gray). The side chains of the two proteins and the
remaining portions of the peptide are not displayed for clarity
sake.
![]() |
ACKNOWLEDGEMENTS |
---|
We are grateful to Lorenz Wernisch for help with the initial calculations and to Jean Richelle as well as all the members of the European project for many useful discussions.
![]() |
FOOTNOTES |
---|
* This work was carried out as part of a project entitled "A Multidisciplinary Approach to the Development of Epitope-based Vaccines" and was supported by European Communities Grant BIO4CT980294, the Fonds National de la Recherche Scientifique Belge, and the Action de Recherches Concertées de la Communauté Française de Belgique.The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
The on-line version of this article (available at
http://www.jbc.org) contains Table S1.
To whom correspondence should be addressed: E-mail:
shosh@ucmb.ulb.ac.be.
Published, JBC Papers in Press, October 30, 2002, DOI 10.1074/jbc.M206853200
2 K. Ogata, A. Jaramillo, W. Cohen, J.-P. Briand, F. Connan, J. Choppin, S. Muller, and S. J. Wodak, unpublished results.
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ABBREVIATIONS |
---|
The abbreviations used are:
MHC, major
histocompatibility complex;
TCR, T cell receptor;
APLs, altered peptide
ligands;
2m,
2-microglobulin;
PDB, Protein Data Bank;
CHAPS, 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonate;
Fmoc, N-(9-fluorenyl)methoxycarbonyl;
RP-HPLC, reversed-phase high pressure liquid chromatography;
Ab, antibodies;
PBMCs, peripheral blood mononuclear cells;
IL, interleukin;
IFN, interferon;
PBS, phosphate-buffered saline;
r.m.s., root mean
square;
CTL, CD8+ T cells.
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REFERENCES |
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