Modeling of a P-selectin mediated adhesion inhibitor
By Loic Baerlocher
University of Geneva
Internship for the Master in Proteomics and Bioinformatics
Professor:
Prof. Olivier Michielin, Swiss Institute of Bioinformatics (SIB)
Management:
Dr. Vincent Zoete, Swiss Institute of Bioinformatics (SIB)
Aurelien Grosdidier, Swiss Institute of Bioinformatics (SIB)
March 2007
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Table of Contents
Modeling of a P-selectin mediated adhesion inhibitor............................................1
Project.....................................................................................................................3
1. Introduction.........................................................................................................3
1.1 Biological context: Leukocytes, Selectin and therapeutic interest...............3
1.2 Available material..........................................................................................5
1.3 EADock..........................................................................................................6
1.4 Molecular Mechanics Force Field and CHARMM.........................................9
2. Methods.............................................................................................................12
2.1 Overview......................................................................................................12
2.2 Structure preparation..................................................................................12
2.3 Evolutionary parameters.............................................................................13
2.4 Docking experiments...................................................................................14
2.5 Fragment-based structure-based lead optimization....................................15
2.6 Realized predictive docking experiments....................................................17
3. Results...............................................................................................................19
3.1 Ligands with known binding mode..............................................................19
3.1.1 Glycans..................................................................................................19
3.1.2 Peptides................................................................................................20
3.2 Ligands with unknown binding mode..........................................................21
3.2.1 IELLQARK.............................................................................................21
3.2.2 EWVDV..................................................................................................22
3.2.3 Fragments.............................................................................................23
3.3 Sequence mutations....................................................................................23
4. Discussion..........................................................................................................25
4.1 PSGL-1......................................................................................................... 25
4.2 IELLQARK....................................................................................................25
4.3 EWVDV and mutants...................................................................................26
4.4 Fragments....................................................................................................27
4.5 Merging EWVDV and IELLQARK................................................................27
5. Conclusion................................................................................................... ......29
6. Acknowledgments.............................................................................................31
7. Bibliography......................................................................................................31
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Project
This molecular modeling study aimed at finding new ligands of the P-selectin inhibiting the assisted
adhesion and providing potential anti-inflammatory agents. A molecular docking program was used to
find the most favorable positions of molecular fragments and ligands on the P-selectin surface. A
structure-based fragment-based lead optimization approach using these results has been experimented to
design new peptidic inhibitors.
1. Introduction
1.1 Biological context: Leukocytes, Selectin and therapeutic
interest
Inflammation is a defense reaction caused by tissue damage or injury, characterized by redness, heat,
swelling, and pain. The primary objective of inflammation is to eradicate the irritant and repair the
surrounding tissue. The inflammatory response involves three major stages: first, dilation of capillaries to
increase blood flow; second, microvascular structural changes and escape of plasma proteins from the
bloodstream; and third, leukocyte transmigration through endothelium and accumulation at the site of
injury.
The leukocyte adhesion cascade is a sequence of adhesion and activation events that ends with
extravasation of the leukocyte, whereby the cell exerts its effects on the inflamed site. The five steps of
the adhesion cascade are capture, rolling, slow rolling, firm adhesion, and transmigration [1]. Each of
these five steps appears to be necessary for effective leukocyte recruitment, since blocking any of the five
can severely reduce leukocyte accumulation in the tissue[2].
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Illustration 1: Leukocyte selectin assisted adhesion cascade on endothelium, from capture to transmigation.
Image from: http://bme.virginia.edu/ley/images/map.gif
Studies using P-Selectin-deficient mice have shown that P-selectin, a cell-surface glycoprotein expressed
on activated endothelial cells and platelets, plays a major role in initial leukocyte tethering and rolling [3]
in response to inflammatory signals by interacting with its counter-receptor, the P-Selectin Glycoprotein
Ligand-1 (PSGL-1)[4], located on leukocyte membrane. P-selectin-specific blocking antibodies have
shown that P-selectin participates in the pathophysiology of numerous acute and chronic inflammatory
diseases[5], including ischemia/reperfusion injury[6][2]. The roles of adhesion molecules in acute and
chronic inflammation has produced an interest for anti-inflammatory agents that function as blockers,
suppressors, or modulators of the inflammatory response [7].
Carcinoma metastasis in mice is facilitated by the formation of tumor cell complexes with blood
platelets[8]. Specific P-selectin/tumor cell interactions have been revealed, wherein P-selectin mediates
early interactions of platelets with tumor cells [9]. Development of antagonists of these interactions
showing significant inhibition at concentration in the clinically acceptable range is therefore promising.
Examples of both potent and specific P-selectin antagonists remain limited to P-selectin–blocking
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antibodies and PSGL-1 derivatives. The crucial role for P-Sel-specificity and affinity of a core-2 Oglycan, known as the syalil-Lewis X motif, and three tyrosine sulfates (Tys) in PSGL-1 derivatives [1012] limit their use. Therefore, the design of new peptidic antagonists of the P-selectin assisted adhesion is
of major interest.
1.2 Available material
A 3D structure from P-selectin and PSGL-1 has been obtained by X-ray crystallography [13](PDB ID:
1G1S). It consists of a dimer of P-Selectin (C-type lectin and EGF-like domain) in complex with the most
important residues from PSGL-1 at 1.9 [Å] resolution. This is made up of fourteen amino acids, further
called peptide605-618, covalently linked to the syalil-Lewis X motif made up of six glycans. The
covalent bond between the two molecular partners defining PSGL-1 takes place between residue Thr616
and residue NGA625. The structure of PSGL-1 bound to P-selectin is shown in Illustration 3 and 4. The
two molecules have been expressed in eukaryotic cells by using plasmids. It is used to represent the
conformation of the P-selectin and the position of PSGL-1 through the study.
The sequences from two known ligands of P-selectin, namely IELLQARK [14] and EWVDV [15], have
been used to generate two small peptides used as ligands for the docking experiments described later. The
IELLQARK peptide is known to mimic the syalil-Lewis X motif and to have a high affinity for P-selectin
when covalently linked to Thr616 of peptide605-618. The EWVDV peptide, found by phage display, has
been shown to be a minimum sequence peptide with high affinity for P-selectin. These peptides
specifically inhibit the P-Selectin-mediated adhesion.
1.3 EADock
This program [16] has been designed for molecular docking, i.e. finding the most favorable position and
orientation, called the binding mode, of a small ligand on a protein surface, in the context of rational
drug design. It has been used to obtain high interaction binding modes for the selected ligands.
This program rely on an evolutionary engine working under the Darwinian rule "survival of the fittest"
and of a Java API mediating the interaction between the docking algorithm and a running CHARMM
[17] instance. CHARMM is a program used for molecular mechanics calculations (see chapter 1.4).
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Illustration 2 describes the evolutionary algorithm used by EADock. An initial population of binding
Seeding
Population
Constraints checking
for the new population
Ranking of all the complexes
by the simple fitness
Clustering of the similar complexes
from the population
and evaluation of their energy
by the FullFitness
Children
Generation of children by
modification of the fittest parents
with the EADock operators
The full cluster(s) with the most
unfavorable ranking is(are) taken out
of the search space by the DSSA
Parents
Ranked population
Optimized solutions
Illustration 2:EADock evolutionary cycle from seeding to optimized solutions
modes is created from a single position of the ligand by stochastic, semi-stochastic, or deterministic
modifications with the EADock operators. This is called the seeding. The algorithm selects the fittest
solutions with an objective function (see below), and use them as parents. These are modified by the
operators to obtain new binding modes with the hope that they will be fitter than their parents. Each
created child is then submitted to a local search by energy minimization and evaluated using the
CHARMM package. The children are added to the population, replacing the worst elements of the latter.
This procedure is repeated until convergence or until a given number of optimization cycles, called
generations, have been performed.
EADock uses two different functions that complement each other as the selection pressure. The
SimpleFitness enables a fast calculation of the energy of all complexes to drive the search toward local
minima. Clusters of population elements corresponding to these local minima appear during the
evolutionary cycles. The FullFitness, more precise but computationally intensive, is then used to rank the
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mature clusters.
The SimpleFitness calculates the energy of the complex as follows:
receptor
E simp =E ligand
E vdw E elec
intra  E intra
Eligand
is the internal energy of the ligand, which corresponds to the sum of the internal bonded
intra
(bonds, angles...) and non-bonded (electrostatic and van der Waals interactions) terms.
Ereceptor
is the internal energy of the receptor, which is constant since the latter is kept fixed
intra
throughout the simulation.
Evdw and Eelec are the van der Waals and electrostatic interaction energies, respectively.
The FullFitness adds the solvation effect to Esimp to calculate full clusters average energy as follows:
∆ G solv =∆ G elec ,solv  ×SASA
∆ Gelec,solv is the electrostatic solvation free energy calculated through the analytical GB-MV2
Generalized Born model [18][19] implemented in CHARMM. The non polar contribution to the
solvation energy is assumed to be proportional to the solvent accessible surface area (SASA) that is
buried upon complexation. This is justified by the linear relationship between the SASA and the solvation
energy of saturated non polar hydrocarbons [20].  has been set to a value of 0.0072 [kcal/(mol•Å)].
The Dynamic Search Space Adjustment (DSSA), inspired by the tabu search, removes the least
favorable clusters from the search space by forbidding the appearance of children similar to these
conformations and by removing the corresponding clusters from the population. This promotes evolution
and convergence to a minimum by preventing exploration of unfavorable binding modes that have
already been identified.
1.4 Molecular Mechanics Force Field and CHARMM
CHARMM[17] is a program that has been designed for macromolecular energy minimization and
dynamics calculation. CHARMM represents molecules at the atomic level. In order to be able to build
polymeric macromolecules, such as proteins, nucleic acids and polyglycans, monomers are defined in the
Residue Topology File (RTF). The RTF coming with the standard CHARMM distribution contains the
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topologies for all natural amino acids, nucleotides and some glycans. Others can be built and appended
when needed. Residues can then be covalently linked and eventually modified to form a full molecule, or
chain. The Protein Structure File (PSF) contains the topology, i.e. the connectivity, of the system
needed to evaluate its energy. Finally the Coordinate-file (CRD file) list the atom coordinates.
CHARMM uses a molecular mechanics energy function where:
1) The bond length stretching is modeled by an harmonic potential:
V bond = ∑ k r r −r 02 , where r is the bond length, r the equilibrium distance and k the bondr
0
bonds
stretching force.
2) The bond angle bending is also modeled by an harmonic potential:
V bond angle = ∑ k  −02 , where
 is the bond angle, 0 the equilibrium value and k  the
angles
angle bending force constant.
3) The torsion of the dihedral angles is modeled by a cosine expansion:
V torsion=
∑
dihedrals
k  [1cos n −] , where  is the dihedral angle, k its force constant, n its

multiplicity and  its phase.
4) The non-bonded interactions between two atoms (here named i and j) are modeled using a Coulomb
potential term for electrostatic interactions and a Lennard-Jones potential for van der Waals interactions:
V non bonded=∑ 4 ij
i, j
[    ]
 ij
r ij
12
 ij
−
r ij
6
∑
i,j
qi q j
, where q i and q j are the atomic charges of atoms
 r ij
i and j,  the dielectric constant, ij the dispersion well depth,  ij the Lennard-Jones diameter and
r ij the distance between i and j.
The sum of these terms gives the total potential energy.
V total = ∑ k r  r−r 0   ∑ k  − 0  
2
bonds
2
angles
∑
impropers
2
k  −0  
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∑
dihedrals
k  [1cos  n−]
∑ 4 ij
i, j
[    ]
 ij
r ij
12
−
 ij
r ij
6
∑
i,j
qi q j
 r ij
The analytical Generalized Born model GB-MV2 was used to calculate the electrostatic solvation free
energy. This model was found to reproduce the solvation free energies calculated by solving the Poisson
equation with 1 % accuracy.
The Poisson method for obtaining solvation energies is generally
considered a benchmark for implicit solvation calculations. However, GB-MV2 is much faster than
solving the Poisson equation (by a factor of about 20) and is therefore very useful to calculate
∆ Gelec,solv for a large number of conformations.
The Generalized Born equation is the following:
 G elec solv=
qi q j
−1
1
1− ∑ ∑ 2
− D  0.5 , where  is the relative permitivity of the

2

i
j r ij  i  j
medium, q i and
ij
q j the atomic charges of atoms i and j, r ij is the interparticule distance i and
2
j
r ij
the atoms Born radii and Dij =
with K s being a constant set to 8.
 K s i  j 
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2. Methods
2.1 Overview
This study has been done in three steps.
First, the docking of parts of PSGL-1 have been carried out to assess the ability of the method to
reproduce the experimental binding mode.
Second, the docking of known ligands, for which no experimental structure is available, has been carried
out to predict their binding mode. One is selected to serve as a reference for the next step.
Third, a structure-based fragment-based lead optimization procedure was used to rationally design new
ligands. This approach is described in details in chapter 2.5.
2.2 Structure preparation
The P-selectin receptor has been crystallized in a dimeric form (PDB ID 1G1S). However, since the
receptor is active in the monomeric form, only one half of the crystallized dimer detailed in the X-ray
structure (i.e. chain A, D and F) has been retained for the study. The positions of the heavy atoms that
were not resolved in the experimental structure were added using the CHARMM standard topology for
proteins and glycans. All hydrogens atoms, which are not present in the PDB file due to low resolution of
the X-ray, were added using the CHARMM module HBUILD. The strontium ion, an artifact for
crystallization, has been replaced by the appropriate Ca++ ion.
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Illustration 3: The PDB file obtained from the X-ray
structure contains a dimer of P-selectin lectin/EGF domains
(residues 1-158, showed as a surface) complexed with PSGL1 peptide (residues 605-618 with linked polyglycans on THR
616, showed as ball and sticks) at a resolution of 1.9[Å]. The
color code for atoms is: red for oxygens, light blue for
sodium and strontium ions (active site), green for 2-methyl2,4-pentanediol (MDP), blue for nitrogens, yellow for sulfurs
and white for carbons.
Illustration 4: The active site obtained after detailed
manipulations. The calcium ion is shown as a dark green
sphere. The syalil-Lewis X motif, in balls and sticks, is colored
according to the sugar nature: O-Sialic acid in orange, two DGalactoses in light green, N-acetyl-D-glucosamine (NAG) in
light blue, Fucose (FUC) in purple and N-acetylgalactosamine (NGA) in pink. The peptide605-618 is shown in
balls and sticks with atom color code.
The coordinates of P-selectin were kept fixed throughout the whole study. Thus, the ligands were docked
on the fixed receptor surface. Only the ligands were flexible. This means that if some conformational
change of the receptor is required, the docking is likely to fail.
2.3 Evolutionary parameters
The number of generations of the evolutionary process must be sufficient to enable a progressive
exploration of the search space. The population size should allow several local minima of the
SimpleFitness to be represented in Clusters of limited size.
The runs have been carried out with a population of 250 individuals, renewing 25 at each of the 400
generations. The low renewal rate enhances robustness over speed. The clustering of binding modes is
based on the root mean square deviation (RMSD) of the heavy atom of the ligand. The RMSD value
between two members of a given cluster has been set to a maximum of 1.5[Å]. These settings have been
established from the benchmark of EADock [16].
The Region of Interest (ROI), defined as a 15[Å] radius sphere, corresponds to a maximum volume of
14140 [ Å]3 This volume is not fully accessible to the ligand due to the presence of the receptor.
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2.4 Docking experiments
A docking experiment consists of several docking runs with the same parameters where all optimized
solutions are merged in regards of their RMSD to give a clear overview of the ranking and convergence.
To save time, a single seeding has been done for each docking experiment. The starting binding modes
range from 0–10[Å] RMSD from the reference structure for the docking of ligands with known binding
mode, and 0-15[Å] when exploring the binding modes of molecules that were not studied experimentally.
Since a docking run using an evolutionary algorithm is not a deterministic procedure, different docking
runs, even starting from identical conditions, will not lead to exactly identical results, although they are
expected to give similar informations. While merging several runs of a given ligand/protein docking, it is
possible to estimate the redundancy (% of identity) of the results obtained by the different runs. When the
redundancy between the merged optimized solutions of a docking experiment is too low (arbitrary
threshold of 10%), the search space is thought to have been explored inadequately and additional runs are
carried out starting from a new seeding. The new results will then be merged with those already acquired.
This is repeated until the redundancy criteria is met.
For the assessment of the method, a successful optimized result is obtained when the most favorable
proposed binding mode, in terms of FullFitness, shows a RMSD between the predicted binding mode and
the crystal structure lower than 2[Å] [21]. A difference of FullFitness score greater than 3[kcal/mol] is
thought to be significant despite the energy dispersion in a cluster. The best ranked clusters according to
the FullFitness are visualized with the USCF Chimera program [22] to verify that the experimentally
determined native contacts between the ligand and P-selectin are reproduced. All visualizations and
illustrations have been done with this program that combines analytical tools with high quality graphics.
DSSA is activated to enhance convergence.
2.5 Fragment-based structure-based lead optimization
This method aims at replacing the residues of a docked ligand by new ones that are selected based on
their favorable interactions with the targeted receptor.
First, the surface of the receptor is explored by EADock with the isolated side chains of the natural amino
acids in order to find their favorable positions and orientations. The objective of this first step is to find as
many favorable positions as possible. Therefore, the DSSA is not activated for fragments docking since
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we are interested in collecting interesting binding modes, and not only finding the most favorable one.
The collection of these favorable positions for a given molecular fragment is called a map.
Illustration 5: Schematic representation of the fragment-based structure-based lead
optimization.
Docking of ligand with
unknown binding mode
Selection of the binding
mode of interest
Docking of fragments
Merging of all results
to form the maps
Generation of putative mutation
for the ligand sequence by
searching throughout the maps
Docking of mutated ligand
This study is interested in creating a peptidic ligand. However, we could also include different chemical
functions in this search, although amino acid side chains already provide a diversified panel of physicochemical properties.
Next, a list of putative mutations is generated by examining the ability of these favorably positioned side
chains to replace the actual side chains of the peptide in the selected binding mode. This examination is
based on the examination of the C  (ligand)/ C  (fragment) distance and of the angles involving the
C  atom of the ligand and the C  atom of the fragments in the map. This is done for each amino
acid of the ligand. The optimized binding modes of the fragments that are well placed to be connected to
the peptide backbone are retained as putative new residues, thus defining possible sequence modifications
of the ligand.
The user can restrict the mutations to specific residues (e.g. unfavorable ones) or construct a list of
putative peptide ligands in a combinatorial way. These putative ligands are then docked on the protein
surface using EADock. The most promising ones, in terms of interactions with the targeted protein, are
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retained for further modifications using the same fragment-based approach. This procedure might be
repeated several times, finally leading to a list of molecules to be tested experimentally.
In order to rank different ligands, the fitness functions are inadequate as the energies of the free ligands
are not identical. Therefore the free energy of binding,  G bind = E complex −E ligand −E recepteur  , is
taken into account:
 G bind = E elec  E VDW  G elec ,solv   ×SASA
Illustration 6: Map from the valine fragment docking. The molecule surface is colored
according to the electrostatic potential calculated by solving the Poisson-Bolzman
equation using the UHBD program[23] (blue positive, red negative). The optimized
results for the fragment are in pink wire. The calcium ion is represented as a green
sphere.
2.6 Realized predictive docking experiments
All peptide ligands have acetylated N-terminus and amidated C-terminus to neutralize their charge unless
specified.
The following Table summarizes the docking experiments that have been performed to predict the
unknown binding modes of ligands, along with their respective number of docking runs.
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Ligand
Number of runsSecond seeding
Fragments
2
N
IELLQARK
12
Y
IELLQARK with peptide605-618 added on receptor
14
Y
IELLQARK with NOE potential
16
Y
IELLQARK with NOE potential and peptide605-618
7
N
EWVDV
15
Y
EWVDV with charged ends
15
Y
EWVDV mutants
10
Y
EWVDV mutants with charged ends
5
N
Table 1: Summary of all unknown binding mode docking experiments
Four docking experiments were performed to find the binding mode of IELLQARK. They were carried
with or without the presence of residues 605-618 from PSGL-1 on the receptor, and with or without a
Nuclear Overhauser Effect (NOE) potential on the nitrogen atom from Lys8 side chain. The latter was
added to the CHARMM energy function to mimic the existence of a covalent bond between this atom
and Thr616 from PSGL-1. The NOE potential has a null flat bottom in a region defined with a 1[Å]
radius, allowing free motions in that area. The energy added by the NOE potential increases harmonically
with the selected atom distance to that area.
Two docking experiments were performed to find the binding mode of EWVDV. One with an acetylated
N-terminus and amidated C-terminus, and another one with charged termini. Also, several docking
experiments have been performed for EWVDV derivatives whose sequence has been modified on
position 4: the aspartic acid side chain was replaced by seven different fragments from the maps (see
below).
Two docking experiments were performed for 18 molecular fragments corresponding to all amino-acid
natural side chains, except glycine that has no side chain, and proline. The two docking experiments had
a different center for the ROI, in order to cover a larger region of the protein surface.
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3. Results
3.1 Ligands with known binding mode
Ligand
Success
(RMSD)
Cluster
rank
FullFitness [kcal/mol]
Top Cluster Redundancy
FullFitness
[kcal/mol]
FUC
Y (0.20)
4
-5071
-5075
36 %
NAG-FUC
Y (1.51)
4
-4965
-4967
20 %
Syalil-Lewis X motif N (3.40)
14
-4894
-4908
34 %
Peptide605-609
3
-5499
-5506
22 %
N (5.1)
Table 2: Summary of the docking experiments described in the next two chapters. The rank and FullFitness of the calculated
binding mode closest to the experimental one are given.
3.1.1 Glycans
For the glycans, only the docking experiments with fucose (FUC) and N-acetyl-D-glucosamie (NAG)FUC as ligands reproduced the binding mode fulfilling the imposed conditions (see Methods). For the
two largest polyglycans docked, i.e. the whole Syalil-Lewis X motif and O-Sialic acid-D-GalactoseNAG-FUC, an optimized solution with a FullFitness score 14 [kcal/mol] over the one from the top
clusters has been selected for each. It reproduced the binding mode of FUC and NAG residues with a
RMSD smaller than 2[A]. Nevertheless the overall RMSD of these ligands were found to be higher as the
other glycans were not docked as experimentally determined. The FullFitness scoring difference is
significant and would not lead these optimized solutions to be selected using the standard criteria based
only on the FullFitness. Only a comparison with the X-ray structure showed that they were relevant.
Other docking experiments failed to reproduce the experimental binding mode.
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3.1.2 Peptides
Illustration 7: Results of docking for peptide605-609. The side chain of TYS605 has moved and can now make H-bonds with
Lys 112 and 8 from P-selectin. A single rotation of a bond has swapped the side chain of amino acid 608 and the whole 609
residue. This represents ¼ of the heavy atoms and only those away from the surface of P-selectin.
For the docking of peptide605-609, the proposed binding mode for tyrosine sulfates 605 and 607
correspond to the experimental positions. By comparing the illustration above and Illustration 6, we see
that Tys605 has moved to a place enabling the interaction with Lys112 from P-selectin[14]. The high
RMSD of this binding mode is due to the swap of the side chain of amino acid 608 and the whole 609
residue. They are not in contact with the P-selectin in the experimental structure so their position is less
relevant than the one of the tyrosine sulfates. Therefore this docking is considered to be a success
although it has a high RMSD.
Other docking experiments failed to reproduce the experimental binding mode.
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3.2 Ligands with unknown binding mode
3.2.1 IELLQARK
The evolutionary process did not converge for the docking experiments of this peptide. The clusters
within 5 [kcal/mol] of the best one were all very different. Most made only few contacts with the
receptor. The hydrophobic side chains were not even buried.
The best possible position from the docking experiment with IELLQARK as ligand and the NOE
potential activated is shown in Illustration 8.
Illustration 8: Result for IELLQARK docking in balls and sticks with atom color code on P-Selectin surface. The X-ray
position of PSGL-1 is shown in pink wire. The linking oxygen from THR 616 with IELLQARK is shown as a pink ball.
This binding mode displays the two expected features from IELLQARK (c.f. Introduction > Available
data). It takes the place of the syalil-Lewis X motif and the side chain from the Lys8 from IELLQARK is
close enough to make a covalent bond with Thr616 from peptide605-618. Its FullFitness value is -5682
[kcal/mol] while the one of the top cluster is -5692[kcal/mol]. This difference is significant and therefore
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this optimized solution would not have been selected without visual validation.
3.2.2 EWVDV
Since the nature of the termini was not mentioned
in ref [15], docking experiments were carried out
once with charged N and C-termini and once with
acetylated N-terminus and amidated C-terminus.
The top clusters corresponding to the former setup
were found to be similar, but the latter setup led to
binding modes with a more favorable FullFitness
score. The uncharged N and C termini mimic larger
natural peptides and do not interfere with the
docking by creating additional bonds.
A good convergence was observed. All most
Illustration 9: Top 2 resulting clusters from EWVDV
docking. They differ only by the placement of Trp2 side
chain.
favorable clusters have similar binding modes and
are found in several runs (e.g. Cluster 1 in 6 out of
15 runs and Cluster 4 in 12 out of 15). They differ
in the placement of the tyrosine ring, the last valine
and/or small translation of the backbone.
The proposed binding mode is in the binding pocket
of the two tyrosine sulfates, namely Tys 605 and
607. It is ranked fourth with a FullFitness of -5403
[kcal/mol], only 1[kcal/mol] less favorable than the
best one. It makes six hydrogen bonds and the
tryptophan side chain interacts wih an adjacent
Illustration 10: Selected EWVDV binding mode. The
interaction sites for the tyrosine sulfates 605 and 607 are
occupied. The chosen mutation site points towards the
binding pocket of the polyglycans.
tyrosine ring in P-Selectin. The glutamate forms H-bonds with Lys112 and Lys8 from P-selectin, and
with Tys605 from PSGL-1. The two valine side chains show no interaction with the receptor.
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3.2.3 Fragments
Illustration 11: Glutamate docking results with original
position in ball and stick (white carbons, red oxygens) and
final population in pink wire. The surface of the receptor is
colored according to the electrostatic potential calculated by
solving the Poisson-Boltzmann equation using the UHBD
program[23] (blue positive, red negative).
Illustration 12: Ten best ranked optimized solution for the
Glu side chain in ball and sticks. The green wire indicate
expected H-bonds between the ligand and the receptor.
The optimized results obtained for the maps showed a good spread throughout the receptor surface and
are in agreement with their individual characteristics. For instance, the glutamate side chain is negatively
charged, and its most favorable positions were actually found in the most electrostatically positive
regions and are stabilized by H-bonds (see figure).
3.3 Sequence mutations
The mutated EWVXV peptides have showed favorable binding modes similar to the reference selected
above. The following Table gives the Gbind value obtained for the seven mutations of EWVXV, with
acetylated N-terminus and amidated C-terminus :
Mutation
 G bind [kcal/mol]
Reference Ala
-79.4
Gln
Lys
Met Thr
-62.0 -78.0 -47.8 -72.2
Trp
Tyr
-82.9 -84.5 -75.0
Table 3: Free energy of binding for EWVDV mutants
Two mutants, i.e. Thr4 and Trp4, have a significantly better Gbind than the reference. The Gln4
mutant has a Gbind closely related to the one from the reference. The difference in Gbind of the
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other mutants is significantly lower.
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4. Discussion
4.1 PSGL-1
The docking of PSGL-1 was carried out to ensure that the docking protocol was able to identify the X-ray
structure as the most favorable binding mode. But some residues play little role in the interactions
between the ligand and the receptor. Thus their position in the X-ray structure is expected to be
particularly sensitive to the crystal contacts and to the crystal water molecules. Since the scoring
functions of EADock have been developed to reproduce the situation is solution (no crystal contacts), the
docking runs are not expected to reproduce the experimental X-ray binding modes of these particular
residues.
Only the FUC and NAG-FUC binding modes were reproduced properly for the glycans. They both
contain a fucose residue that is the most buried and important glycan for the interaction [10]. Therefore
proposed binding modes not corresponding to the X-ray structure were expected for the other glycans
when docked separately. The presence of the rest of the sialyl-Lewis X motif might have been required to
docked them adequately, but this hasn't been tested.
The docking experiment of peptide605-609 reproduced the binding mode seen in the X-ray structure. The
docked positions of the two tyrosine sulfates are compatible with the experimental data. Again, these
residues are essential for the interaction of PSGL-1 and P-selectin [10].
Overall the binding modes of the most important residues for this interaction have been reproduced.
4.2 IELLQARK
The poor convergence of the docking runs carried out for this peptide indicates that the search space was
not adequately explored although the final clustering showed a redundancy over 10%. Many top ranked
clusters made only few interactions with the receptor. The use of NOE potential introduced a bias used to
oversample possible binding modes mimicking the effect of the bond between Lys8 from IELLQARK
and Thr616 from peptide605-618. Some improvements were observed, yet still insufficient for the
algorithm to converge. Therefore, the parameters for this docking experiment should be changed or the
fitness functions improved, or both.
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Reducing the ROI would reduce the number of binding modes that can be explored. Consequently the
density of populated binding modes would increase and it should enhance convergence. The population
and the number of generations can be increased but this comes at the price of longer runs.
Overall, the number of degrees of freedom resulting from the size of the peptide is thought to be one of
the limiting factor, so it could be cut down into pieces as it has been done with PSGL-1. The docking of
these pieces should at least indicate the most important residues for the interaction.
4.3 EWVDV and mutants
The two docking experiments for EWVDV
have
converged.
They
indicate
an
unambiguous binding site, although six binding
modes of close FullFitness are still competing.
It indicates that EWVDV interfere with PSGL1 docking as competitive inhibitor for the
interaction region of Tys605 and Tys607.
The chosen mutation site is located on Asp4.
Experimental data has been obtained only for
the Lys4 mutant that shows 1000 time less
affinity for P-selectin than EWVDV[15]. This
is in agreement with its lower calculated
Illustration 13: Results for high ranked binding mode for the
tryptophan mutant of EWVDV in blue and EWVDV reference
binding mode in purple. The mutation has moved the last valine
away from the reference structure, but the main interactions are
still present.
Gbind . The Gbind values for the Thr4 and Trp4 mutants suggest that they should have a slightly
higher affinity than EWVDV. The Gbind value for the Gln4 mutant suggest that it should have a
similar affinity. This should now be tested in vivo for further validation.
4.4 Fragments
At least one mutation was proposed for half of the residues of the mutated ligands. For EWVDV, a total
of 42 different mutants were generated by combinatorial replacements. This shows that using only
standard amino acid already results in a high number of putative mutations.
Runs were carried out for peptides with one or two mutations, but most have been docked with a RMSD
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to the reference binding mode greater than 2[Å]. Only EWVDV single mutants were found to have
binding modes similar to the reference. Since it is the only mutated peptide where runs have converged,
this can be thought as a prerequisite to mutation.
4.5 Merging EWVDV and IELLQARK
The results for the docking of these two peptides are close and complementary, with the N atom of Glu2
in IELLQARK and C terminal carbon of EWVDV closer than 4[Å]. Therefore a 12 residues long peptide
that fits to the receptor has been built with a glycine as linker between the two molecules. The docking of
this peptide has not been carried out because of its size.
Illustration 14: EWVDV and IELLQARK best binding modes in ball
and stick (except residue Ile in sticks for IELLQARK) with atom color
code on P-Selectin surface. In green the C-terminal carbon of EWVDV
and in yellow the nitrogen of Glu2 in IELLQARK.
Illustration 15: EWVDVGELLQARK binding mode resulting from the
merging of the two selected binding mode.
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5. Conclusion
The combination of EADock and CHARMM has shown the ability to reproduce the key features of the
binding mode of PSGL-1 fragments, but only for minimal necessary sequences. Since an uncertainty for
the position of the low interaction residues is inerrant to an X-ray structure, EADock can be used to
propose a possible binding mode of high affinity ligands, for which no experimental data is available.
The parameters used for the docking experiments of the IELLQARK peptide did not allow the
evolutionary process to converge. Several explanations are possible. For example, its interaction might
require some conformational changes of the receptor, or the size of the peptide might require a longer
evolutionary process. This means that the global minimum might have been not explored at all. Finally,
the fitness functions have shown some limitations as all runs had top ranked clusters with only few
contacts with the receptor, whereas a binding mode with a less favorable FullFitness score but more
interactions with the receptor has been generated by the docking experiment.
The use of a single NOE potential for IELLQARK has shown its ability to act as a selection pressure, as
many binding modes where explored with the required position for the selected atom. Using double NOE
potential on both ends of peptides could allow docking of fragments of chains, but should then be
considered as local search.
EWVDV docking runs converged to several highly similar binding modes. Its mutants exhibited similar
top ranked binding modes. Two mutants out of seven have a better calculated Gbind than the
reference, which makes them potential new high affinity ligands. This gives credit to the structure-based
fragment-based approach as a method for enhancing ligands affinity in the context of rational drug
design.
Given binding modes calculated for IELLQARK and EWVDV have been selected to form a 13 peptide
ligand. Our confidence on this result is limited because of the uncertainties for the selected binding mode
of IELLQARK and the lack of reference for its free energy of binding.
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6. Acknowledgments
I would like to thanks:
Olivier Michielin for welcoming me into his research group.
Amid Hussain Kahn for his help with the condor cluster that as been necessary for the calculations.
A particular thanks to:
Aurelien Grosdidier for his time, help and expertise with the informatics problems I encountered.
Vincent Zoete for his time, help and expertise with the chemical and physical issues.
Finally, I would like to thank the whole research group for making the time I spent in Lausanne
enjoyable.
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