Modeling of a P-selectin mediated adhesion inhibitor · Studies using P-Selectin-deficient mice have shown that P-selectin, a cell-surface glycoprotein expressed on activated endothelial

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

Modeling of a P-selectin mediated adhesion inhibitor1/27

Table of ContentsModeling of a P-selectin mediated adhesion inhibitor............................................1Project.....................................................................................................................31. Introduction.........................................................................................................3

1.1 Biological context: Leukocytes, Selectin and therapeutic interest...............31.2 Available material..........................................................................................51.3 EADock..........................................................................................................61.4 Molecular Mechanics Force Field and CHARMM.........................................9

2. Methods.............................................................................................................122.1 Overview......................................................................................................122.2 Structure preparation..................................................................................122.3 Evolutionary parameters.............................................................................132.4 Docking experiments...................................................................................142.5 Fragment-based structure-based lead optimization....................................152.6 Realized predictive docking experiments....................................................17

3. Results...............................................................................................................193.1 Ligands with known binding mode..............................................................19

3.1.1 Glycans..................................................................................................193.1.2 Peptides................................................................................................20

3.2 Ligands with unknown binding mode..........................................................213.2.1 IELLQARK.............................................................................................213.2.2 EWVDV..................................................................................................223.2.3 Fragments.............................................................................................23

3.3 Sequence mutations....................................................................................234. Discussion..........................................................................................................25

4.1 PSGL-1.........................................................................................................254.2 IELLQARK....................................................................................................254.3 EWVDV and mutants...................................................................................264.4 Fragments....................................................................................................274.5 Merging EWVDV and IELLQARK................................................................27

5. Conclusion................................................................................................... ......296. Acknowledgments.............................................................................................317. Bibliography......................................................................................................31


ProjectThis 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].


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


Illustration 1: Leukocyte selectin assisted adhesion cascade on endothelium, from capture to transmigation.

Image from: http://bme.virginia.edu/ley/images/map.gif

antibodies and PSGL-1 derivatives. The crucial role for P-Sel-specificity and affinity of a core-2 O-

glycan, known as the syalil-Lewis X motif, and three tyrosine sulfates (Tys) in PSGL-1 derivatives [10-

12] 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).


Illustration 2 describes the evolutionary algorithm used by EADock. An initial population of binding

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


Illustration 2:EADock evolutionary cycle from seeding to optimized solutions

Seeding

PopulationRanking of all the complexes

by the simple fitness

Constraints checkingfor the new population Clustering of the similar complexes

from the population and evaluation of their energy

by the FullFitnessChildren

Generation of children bymodification of the fittest parents

with the EADock operators

The full cluster(s) with the mostunfavorable ranking is(are) taken out

of the search space by the DSSA

Parents Ranked population

Optimized solutions

mature clusters.

The SimpleFitness calculates the energy of the complex as follows:

E simp=E intraligandE intra

receptorE vdwE elec

Eintraligand is the internal energy of the ligand, which corresponds to the sum of the internal bonded

(bonds, angles...) and non-bonded (electrostatic and van der Waals interactions) terms.

Eintrareceptor is the internal energy of the receptor, which is constant since the latter is kept fixed

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


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:

Vbond

=∑bonds

krr−r 0

2, where r is the bond length, r 0 the equilibrium distance and k r the bond-

stretching force.

2) The bond angle bending is also modeled by an harmonic potential:

V bond angle=∑angles

k −02

, where is the bond angle, 0 the equilibrium value and k the

angle bending force constant.

3) The torsion of the dihedral angles is modeled by a cosine expansion:

V torsion= ∑dihedrals

k[1cosn−] , 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=∑i , j

4ij [ ij

r ij 12

−ij

r ij 6 ]∑i , j

q i q j

r ij, where q i and q j are the atomic charges of atoms

i and j, the dielectric constant, ij the dispersion well depth, ij the Lennard-Jones diameter and

rij the distance between i and j.

The sum of these terms gives the total potential energy.

V total=∑bonds

k r r−r02∑

anglesk −0

2 ∑impropers

k −02 ∑

dihedralsk[1cos n−]


∑i , j

4ij [ ij

rij 12

−ij

r ij 6 ]∑i , j

qi q j

rij

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:

Gelec solv=−12 1−1

∑i∑

j

q i q j

rij2i j

−Dij0.5 , where is the relative permitivity of the

medium, q i and q j the atomic charges of atoms i and j, r ij is the interparticule distance i and

j the atoms Born radii and Dij=r ij

2

K si jwith K s being a constant set to 8.


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.


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.


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 D-Galactoses in light green, N-acetyl-D-glucosamine (NAG) in light blue, Fucose (FUC) in purple and N-acetyl-galactosamine (NGA) in pink. The peptide605-618 is shown in balls and sticks with atom color code.

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 PSGL-1 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-methyl-2,4-pentanediol (MDP), blue for nitrogens, yellow for sulfurs and white for carbons.

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


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.

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 physico-

chemical 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


Illustration 5: Schematic representation of the fragment-based structure-based lead optimization.

Docking of ligand withunknown binding mode Docking of fragments

Selection of the bindingmode of interest

Merging of all resultsto form the maps

Generation of putative mutationfor the ligand sequence by

searching throughout the maps

Docking of mutated ligand

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 elecE VDWG elec ,solv×SASA

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.


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.

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.


Table 1: Summary of all unknown binding mode docking experiments

Ligand Number of runsSecond seedingFragments 2 NIELLQARK 12 YIELLQARK with peptide605-618 added on receptor 14 YIELLQARK with NOE potential 16 YIELLQARK with NOE potential and peptide605-618 7 NEWVDV 15 Y

15 YEWVDV mutants 10 YEWVDV mutants with charged ends 5 N

EWVDV with charged ends

3. Results

3.1 Ligands with known binding mode

Ligand Success (RMSD)

Cluster rank

FullFitness [kcal/mol] Top Cluster FullFitness [kcal/mol]

Redundancy

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 N (5.1) 3 -5499 -5506 22 %

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-Galactose-

NAG-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.


3.1.2 Peptides

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.


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.

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.

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


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 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

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

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.


Illustration 9: Top 2 resulting clusters from EWVDV docking. They differ only by the placement of Trp2 side chain.

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.

3.2.3 Fragments

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 Reference Ala Gln Lys Met Thr Trp Tyr

G bind [kcal/mol] -79.4 -62.0 -78.0 -47.8 -72.2 -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


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.

other mutants is significantly lower.


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.


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 PSGL-

1 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

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


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.

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.

5. ConclusionThe 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.


6. AcknowledgmentsI 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.

7. Bibliography[1] E. C. Butcher (1991) “Leukocyte-endothelial cell recognition: three (or more) steps to specificity and diversity.” Cell, Vol. 67, No. 6, pp1033-1036.[2] A. Tsujikawa, Y. Ogura, N. Hiroshiba, K. Miyamoto, J. Kiryu, S. J. Tojo, M. Miyasaka, Y. Honda (1999) “Retinal ishemia-reperfusion injury attenuated by blocking of adhesion molecules of vascular endothelium.” Invest. Ophthalmol. Vis. Sci., Vol. 40, No. 6, pp1183-1190.[3] S. D. Robinson, P. S. Frenette, H. Rayburn, M. Cummiskey, M. Ullman-Cullere, D. D. Wagner, R. O. Hynes (1999) “Multiple, Targeted Deficiencies in Selectins Reveal a Predominant Role for P-Selectin in Leukocyte Recruitment”, Proc. Nat. Acad., Vol. 96, No. 20, pp.11452-11457.[4] M. P. Bernimoulin, X.-L. Zeng, C. Abbal, S. Giraud, M. Martinez, O. Michielin, M, Schapira, O. Spertini (2003) “Molecular Basis of Leukocyte Rolling on PSGL-1”, Journ. Bio. Chem., Vol. 278, No.1, pp37-47.[5] J. W. Homeister, M. Zhang, P. S. Frenette, R. O. Hynes, D. D. Wagner, J. B. Lowe, R. M. Marks (1998) “Overlapping Functions of E- and P-Selectin in Neutrophil Recruitment During Acute Inflammation”, Blood, Vol. 92, No. 7, pp. 2345-2352.[6] S. Massberg, G. Enders, R. Leiderer (1998) “Platelet-endothelial cell interactions during ischemia/reperfusion: the role of P-selectin”, Blood, Vol. 92, No. 2, pp507-515.[7] A. D. Luster, R. Alon, U. H. von Andrian (2005) “Immune cell migration in inflamation: present and future therapeutic targets”, Nature Immo., Vol. 6, pp1182-1190.[8] L. Borsig, R. Wong, J. Feramisco, D. R. Nadeau, N. M. Varki, A. Varki (2001) “Heparin and cancer revisited: Mechanistic connections involving platelets, P-selectin, carcinoma mucins, and tumor metastasis” Proc. Nat. Acad., Vol. 98, No. 6, pp.3352-3357.[9] L. Borsig, R. Wong, R. O. Hynes, N. M. Varki, A. Varki (2002) “Synergistic Effects of L- and P-Selectin in Facilitating Tumor Metastasis Can Involve Non-Mucin Ligands and Implicate Leukocytes as Enhancers of Metastasis” Proc. Nat. Acad., Vol. 99, No. 4, pp2193-2198.[10] W. Hanley, O. McCarty, S. Jadhav, Y. Tseng, D. Wirtz, K. Konstantopopoulos. (2003) "Single Molecule Characterization of P-selectin/Ligand Bindings", J. Bio. Chem., Vol. 278, No 2, pp.10556-10561.[11] A. Leppaenen, S. P. White, J. Helin, R. P. McEver. (2000) "Binding of Glycosulfopeptides to P-


http://www.jstor.org/view/00278424/ap001165/00a00960/0?frame=noframe&[email protected]/01cc99334100501e09a91&dpi=3&config=jstor

http://www.jstor.org/view/00278424/ap001165/00a00960/0?frame=noframe&[email protected]/01cc99334100501e09a91&dpi=3&config=jstor

http://www.jstor.org/view/00278424/sp020040/02x4244f/0?frame=noframe&[email protected]/01cc99334100501e09a91&dpi=3&config=jstor



selectin Requires Stereospecific Contribution of Individual Tyrosine Sulfate and Sugar Residues", J. Bio. Chem, Vol. 275, No. 50, pp.39569-39578.[12] P. P. Wilkins, K. L. Moore, R. P. McEver, R. D. Cummings (1995) "Tyrosine Sulfation of P-selectin Glycoprotein Ligand-1 Is Required for High Affinity Binding to P-selectin", J. Bio. Chem., Vol. 270, No. 39, pp.22677-22680.[13] W. S. Somers, J. Tang, G. D. Shaw, R. T. Camphausen.(2000) "Insights into the Molecular Basis of Leucocyte Tethering and Rolling Revealed by structures of P- and E-Selectin Bound to SLeX and PSGL-1", Cell, Vol. 103, pp.467-479.[14] S. Giraud (2002) “Caracterisation du domaine amino-terminal du PSGL-1 et de peptides inhibiteurs de l'interaction de cette proteine avec la p-selectin” Thesis at the university of Lausanne, Faculty of sciences.[15] T. J. M. Molenaar, C. C. M. Appeldoorn, S. A. M. de Haas, I. N. Michon, Arnaud Bonnefoy, M. F. Hoylaerts, H. Pannekoek, T. J. C. van Berkel, J. Kuiper, E. A. L. Biessen. (2002) "Specific inhibition of P-selectin-mediated cell adhesion by phage display-derived peptide antagonists", BLOOD, Vol. 100, No. 10, pp.3570-3577.[16] A. Grosdidier, V. Zoete, O. Michielin (2006) “EADock: A multiobjective Evolutionary Framework for the Docking of Small Molecules into Protein Active Site”, in press.[17] B. R. Brooks, R. E. Bruccoleri, B. D. Olafson, D. J. States, S. Swaminathan and M. Karplus. (1983) “CHARMM: A Program for Macromolecular Energy, Minimization, and Dynamics Calculations”, M J. Comput. Chem., Vol. 4, No.2, pp.187-217.[18] M. S. Lee, F. R. Salsbury Jr., C L. Brooks III (2002) “Novel generalized Born methods” J. Chem. Phys., Vol. 116,pp 10606-10614.[19] M. S. Lee, M. Feig, F. R. Salsbury Jr., C. L. Brooks III (2003) “New Analytic Approximation to the Standard Molecular Volume Definition and Its Application to Generalized Born Calculations”, J. Comput. Chem., Vol. 24, pp1348-1356.[20] H. Gohlke, C. Kiel, D. A. Case (2003) “Insight into Protein-Protein Binding by Binding Free Energy Calculation and Free Enrgy Decomposition for the Ras-RafGDS Complexes”, J. Mol. Biol., Vol. 330, pp891-913.[21] B. D. Bursulaya, M. Totrov, R. Abagyan, C. L. Brooks (2003) “Comparative study of several algorithms for flexible ligand docking” J. Computer-Aided Molecular Design, Vol. 17,pp. 755-763.[22] E. F. Pettersen, T. D. Goddard, C. C. Huang, G. S. Couch, D. M. Greenblatt, E. C. Meng, T. E. Ferrin (2004) "UCSF Chimera - A Visualization System for Exploratory Research and Analysis", J. Comput. Chem., Vol. 25, No. 13, pp1605-1612.[23] J. D. Madura, J. M. Briggs, R. C. Wade, M. E. Davis, B. A. Luty, A. Ilin, J. Antosiewicz, M. K. Gilson, B. Bagheri, L. Ridgway Scott, J. A. McCammon (1995) “Electrostatics and diffusion of molecules in solution: simulation with the University of Houston Brownian Dynamics program”, Comp. Phys. Comm., Vol. 91, pp. 57-95.





















Documents

Modeling of a P-selectin mediated adhesion inhibitor · Studies using P-Selectin-deficient mice have shown that P-selectin, a cell-surface glycoprotein expressed on activated endothelial