Structure-Based Design of Peptidic Inhibitors of the Interaction

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Structure-Based Design of Peptidic Inhibitors of the Interaction between Chemokine 5 (CCL5) and Human Neutrophil Peptides 1 (HNP1) Kanin Wichapong, Jean-Eric Alard, Almudena Ortega-Gomez, Christian Weber, Tilman M. Hackeng, Oliver Soehnlein, and Gerry AF Nicolaes J. Med. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jmedchem.5b01952 • Publication Date (Web): 12 Feb 2016 Downloaded from http://pubs.acs.org on February 17, 2016

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Journal of Medicinal Chemistry

Structure-Based Design of Peptidic Inhibitors of the Interaction between Chemokine 5 (CCL5) and Human Neutrophil Peptides 1 (HNP1)

Kanin Wichapong a,*, Jean-Eric Alardb, Almudena Ortega-Gomezb, Christian Webera,b,c, Tilman M. Hackenga, Oliver Soehnleinb,c,d and Gerry A. F. Nicolaesa

a

b

Institute for Cardiovascular Prevention, Ludwig Maximilians University Munich, Munich, Germany

c

d

Department of Biochemistry, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands

German Centre for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany

Department of Pathology, Academic Medical Center (AMC), University of Amsterdam, Amsterdam, the Netherlands

ACS Paragon Plus Environment


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Abstract Protein-protein interactions (PPIs) are receiving increasing interest, much sparked by the realization they represent druggable targets. Recently, we successfully developed a peptidic inhibitor, RRYGTSKYQ (“SKY” peptide), that shows high potential in vitro and in vivo to interrupt a PPI between the platelet-borne chemokine CCL5 and the neutrophil-derived granule protein HNP1. This PPI plays a vital role in monocyte adhesion, representing a key mechanism in acute and chronic inflammatory diseases. Here, we present extensive and detailed computational methods applied to develop the SKY peptide. We combined experimentally determined binding affinities (KD) of several orthologs of CCL5 with HNP1 with in silico studies to identify the most likely heterodimeric CCL5HNP1 complex which was subsequently used as a starting structure to rationally design peptidic inhibitors. Our method represents a fast and simple approach which can be widely applied to determine other protein-protein complexes and moreover, to design inhibitors or stabilizers of protein-protein interaction.

Keywords; CCL5-HNP1 complex, Binding Free Energy, Protein-Protein Interaction


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Introduction Protein-protein interactions (PPIs) play important roles in many biological pathways such as in signal transduction, trans-membrane transport and maintenance of cellular organization1, 2. There are an estimated 650,000 protein-protein complexes in the human interactome, but only a small number of these have been identified and targeted by means of drug development3. Therefore, PPIs represent an interesting class of drug targets that is increasingly being explored for its potential in drug design and development. Modulation of PPI's can be done in several ways. Mostly explored are compounds or peptides that can stabilize the interaction (sPPI) or those that can inhibit an interaction between protein partners (iPPI). The task of stabilization or inhibition appears very similar, and indeed there are many common features between the two sets of modifiers, yet, recent findings indicate that in particular small molecule stabilizers possess different physicochemical properties than inhibitors4, 5. A protein-protein complex structure provides insight into the detailed atomistic interaction at the interface between two proteins. Therefore, a protein-protein complex can typically be utilized as a starting structure for the design of small molecules or peptides that are able to disrupt an interaction at the interface between those two proteins and prevent the formation of a protein-protein complex6-10. In addition, a protein-protein complex can be used to design stabilizers which function as molecular glue to connect and stabilize the contact between two proteins. Thus, whether the goal is the discovery of a stabilizer or an inhibitor, the identification of the most likely dimeric conformation of a targeted protein-protein complex is a crucial step to successfully develop inhibitors or stabilizers for PPIs. Nevertheless, determination of a likely binding mode between two proteins is a nontrivial task, and protein-protein docking is often used for this purpose. Molecular docking, either protein-ligand or protein-protein, usually generates several possible binding solutions and the selection of a most likely binding pose is frequently judged by comparison of docking scores. With the advent of high performance soft- and hardware, more expensive and rigorous methods such as molecular dynamics



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(MD) simulation and binding free energy (BFE) calculation are now becoming commonly employed to study protein-ligand or protein-protein interactions11, 12. As such, MD and BFE calculation can be used to predict a binding mode of protein-ligand complexes13-15. These computational methods are more accurate than the use of a simple scoring function since the flexibility of a complex is taken into account. In addition, solvation effects are specifically considered by employing simulation in an explicit solvent box or by the use of implicit solvent models (Generalized Born (GB) or Poisson Boltzman (PB) model). Whereas several works have demonstrated the successful application of a binding free energy approach, such as molecular mechanics/Poisson Boltzmann (generalized Born) surface area (MM/PB(GB)SA), to predict a binding mode and binding strength of small molecules at a binding pocket of target proteins16-19, these methods have not yet been extensively applied to predict the binding mode of protein-protein complexes. In this work, we have applied a combination of protein-protein docking and binding free energy calculations (MM/PB(GB)SA) to identify a likely binding mode between two proteins: chemokine 5 (CCL5) and human neutrophil peptides 1 (HNP1). Next, the binding affinity of small peptides which were designed to interrupt the interaction between CCL5 and HNP1 was estimated by the same MM/PB(GB)SA approach. Monocyte recruitment is known to play important roles in a number of acute and chronic inflammatory diseases like myocardial infarction20, atherosclerosis21, sepsis22 and acute lung injury. The cooperation between neutrophils and platelets has been identified as one of the main mechanisms in monocyte recruitment23, 24. We have recently investigated the secretory products of neutrophils and platelets that play a role during monocyte recruitment and we have discovered a synergetic role between neutrophil-borne human neutrophil peptide 1 (HNP1, α-defensin) and platelet-derived CCL525. Thus, disruption of the interaction between these two proteins may provide a promising strategy for the development of drugs to treat inflammatory diseases. Here, we present comprehensive computational details on the identification of a likely binding mode of the CCL5-HNP1 heterodimeric


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complex. This complex structure was consequently used to design peptides that are able to disrupt the interaction between CCL5 and HNP1. The peptides that had been rationally designed by means of our in silico methodology were successfully tested experimentally for their propensity to interrupt the CCL5-HNP1 interaction. The most potent of these peptides exhibits activity in vitro and in an in vivo in a mouse model of myocardial infarction25. Provided that protein structures are available or can be modeled, our approach represents a fast and uncomplicated method which can be applied as a generalized method to determine a binding mode of virtually any protein-protein complex and also to design PPI inhibitors or stabilizers.

Results and Discussion A cooperative role between CCL5 and HNP1 in monocyte adhesion We recently discovered a novel mechanism by which secretory products from neutrophils (HNP1) and platelets (CCL5) play a synergetic role in classical monocyte adhesion25. To study and verify the cooperative role of these proteins in monocyte adhesion, we deposited HNP1 and CCL5 either alone or in combination on human umbilical vein endothelial cells (HUVEC). Monocyte adhesion was assessed by flowing human classical monocytes over the endothelial cells at low shear rates (1.5 dynes/cm2). While monocyte adhesion was not affected in the presence of HNP1 or CCL5 alone, the combination of HNP1 and CCL5 evoked a strong enhancement of classical monocyte arrest as shown in Figure 1. To investigate and confirm a direct binding between CCL5 and HNP1, we performed surface plasmon resonance (SPR) studies by using either immobilized CCL5 or HNP1 as described in the computational section, and a dissociation constant (KD) was experimentally determined (KD = 70 nM and 8.6 nM, respectively)25. A dose-dependent response was also observed in experiments in which HNP1 was immobilized and perfused with different species of CCL5. Consequently, KD values between these CCL5 orthologs and HNP1 were determined (KD values of Human CCL5-HNP1 = 3.87 nM, of Felis



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CCL5-HNP1 = 4.05 nM, of Bos CCL5-HNP1 = 4.29 nM, of Cavia CCL5-HNP1 = 4.29 nM, of Equus CCL5-HNP1 = 7.01, of Mus CCL5-HNP1 = 14.7 nM, of Canis CCL5-HNP1 = 31.5 nM, and of Sus CCL5-HNP1 = 49.0 nM). These results indicate a direct interaction between CCL5 and HNP1 and consequently these two proteins form heteromers and mediate monocyte adhesion. Identification of a likely binding mode of CCL5-HNP1 heterodimeric complex In order to design small peptides which can interrupt the interaction between CCL5 and HNP1, a CCL5-HNP1 heterodimeric complex structure was first identified. We hypothesized that it is possible to prioritize the likeliness of different binding modes by comparison of their different corresponding calculated binding free energies with the experimentally determined binding affinities (dissociation constant, KD). Theoretically, for a given binding mode of the CCL5-HNP1 complex, a correlation between the experimental binding affinities and calculated binding free energies should exist. The more likely the binding mode, the better the correlation should be. Molecular docking of human CCL5 and HNP1 yielded 21 different conformations as shown in Supporting Information Figure S1. Next, the complexes between CCL5 of other species and HNP1 were constructed, based on the poses for each of these 21 different conformations, and their corresponding binding free energies were calculated using snapshots from the position-restrained phase as described in the computational section. Then, the KD values between different species of CCL5 and HNP1, which were obtained from the same experiment, were correlated with calculated binding free energy of each conformation as described in the computational section (Figure 2). Among these 21 conformations, only conformations 8, 9 and 17 showed a correlation between the experimental KD that had been determined for each of the species and their corresponding calculated binding free energy, as summarized in Table 1 and demonstrated in Figure 3. Conformation (conf.) 8 gave a contrasting result, since no correlation was seen when the MM/GBSA method was used (r2 = 0.02) while MM/PBSA yielded a relatively high correlation (r2 = 0.64). Conf. 9 and 17 gave a moderate correlation (r2 = 0.41 and 0.44 for MM/GBSA and MM/PBSA,


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respectively, for conf. 9, and r2 around 0.30 for conf. 17). The binding free energy derived from either MM/GBSA or MM/PBSA of these two conformations (conf. 9 and 17) is more consistent than those obtained from conf. 8. Conf. 13 showed a noteworthy inverse correlation (r2 = 0.44 and 0.67 for MM/GBSA and MM/PBSA, respectively), as shown in Figure 3. The three conformations (conf. 8, 9 and 17) which showed a positive correlation between calculated and experimental binding were selected for subsequent MD simulation (50 ns) in order to examine the stability of these complexes and to more precisely calculate their binding free energies by using an ensemble of 100 MD snapshots extracted from the last 10 ns of the additional 50 ns simulation. All these complexes were stable during the equilibrium phase (40-50ns), as judged from the small RMSD fluctuations observed for the conformations tested (Supporting Information Figure S2). An ensemble of snapshots from the last 10 ns was extracted for binding free energy calculation. Interestingly, conf. 8 and conf. 17 now showed no correlation between the experimental and calculated ∆Gbinding either using MM/GBSA or MM/PBSA as displayed in Figure 4. Only conf. 9 gave a consistent correlation between experimental and calculated ∆Gbinding (r2 = 0.38 and 0.34 for MM/GBSA and MM/PBSA, respectively) with r2 being slightly lower as compared to the previous step. The binding free energies of these 3 conformations were additionally compared as shown in Table 2. It can be observed that most of the binding free energies of conf. 9 are lower than those obtained from conf. 8 and 17, implying that conf. 9 represents a more thermodynamically favorable conformation than the other two conformations. Taken together, conf. 9 represents a likely binding mode of CCL5-HNP1 complex. Investigation of a correlation between experimental binding affinity and calculated binding free energy The correlation for the data set of conformation 9 is relatively low (r2 around 0.4). Therefore, we have investigated the data set in greater detail and we have found that upon removal of 1 or 2 data



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points, which appear likely to be outliers, as judged by their deviation from the linear model, the correlation of the set can drastically increase to r2 = 0.62 as is shown in Supporting Information Figures S3 and S4. To examine whether these data points are true outliers, a z-score (an absolute difference between the predicted and the observed biological values divided by the square root mean square error of the data set) was calculated. A data point was considered an outlier if the corresponding z-score was higher than 2.518. However, the z-score of those data points and the others are lower than 2.5 (Supplementary Material Table S1), which indicates that there are no outliers in all models derived. We then have additionally calculated the absolute entropy change upon binding and added these to the enthalpy term in order to obtain an absolute binding free energy. The derived absolute binding free energy values are closer to the experimental data as shown in Table 3. The correlation between the predicted absolute binding free energy and experimental data is however more or less similar as that obtained from the previous step (where the ∆Gbinding was approximated via calculation of the enthalpy term only (∆G ~∆H)) as demonstrated by Figure 5. Other methods such as free energy perturbation (FEP), thermodynamic integration (TI) or MM/PB(GB)SA using other parameters (e.g. different GB models or force fields) can be applied to investigate a correlation between experimental and calculated ∆Gbinding. However, no “gold standard” method exists for correlating experimental and calculated data and it is uncertain that rigorous approaches like FEP and TI, which require extensive computational time and cost, can significantly improve a correlation. To develop a general method that is able to precisely predict a binding affinity of a given protein-protein interaction, and which results in a high correlation between experimental and calculated ∆Gbinding is however not the aim of this work. We are primarily interested in the identification of that conformation from a pool of possible conformations that represents the most likely binding mode for the interaction between CCL5 and HNP1. The identified conformation should show the highest correlation between experimental and calculated ∆Gbinding among the potential 21 different


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conformations. Even though the correlation for conformation 9 is not high (r2 = 0.44), it was reported earlier that an r2 value of around 0.40 (r = 0.61-0.69)26-28 is acceptable for a protein-protein interaction. Besides the identification of a likely binding mode of the CCL5-HNP1 complex (conformation 9), from all experiments we can additionally conclude that use of refined snapshots from a short MD simulation (100 ps) should be sufficient for application in binding free energy calculation, since prolongation of the simulation time up to 50 ns did not result in improved correlation parameters. Furthermore, we confirm that the entropy change upon binding is negligible and for practical reasons can be ignored when a relative binding free energy is considered. Design of Peptides to interrupt CCL5-HNP1 interaction The conformation number 9 of the complex between human CCL5 and HNP1 was used as a starting structure to design peptidic inhibitors as explained in Figure 6. This complex was utilized to gain insight into the interaction between these two proteins. One hundred snapshots from the equilibrium phase of the MD run (during 40 to 50 ns) of this conformation were extracted for perresidue energy decomposition (DC) analysis by use of MM/GBSA approach29 and results are shown in Figure 7. Residues which show high negative value, such as Arg18, Tyr24 and Gln25, are residues that contribute to a relatively larger extent to the interaction with CCL5 than residues with less negative values. From this result and the determined structure of heterodimeric CCL5-HNP1 complex as shown in

Figure

6(A),

we

concluded

that

residues

from

β-strand

2

–

3

of

HNP1

(17RRYGTCIYQGRLWAFCC33) are the main key residues that govern the interaction with CCL5. Hence, small peptides were designed based on the sequence of HNP1 from residue 17-33 as explained in the computational section. The list of designed peptides and their corresponding predicted binding free energy upon binding to human CCL5 is summarized in Table 4, only the MM/PBSA was considered, as this approach exhibited the highest correlation in the previous step. As can be seen clearly, the exchange of a neutral



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residue (Ile23) to a positively charged residue, such as lysine (K) or arginine (R) can considerably improve the predicted binding free energy (BFE). For instance, seq. 2 where position 23 is an Ile (I) yielded a BFE equal to -9.89 ± 1.56 kcal/mol, while seq. 3 (K23) and seq.4 (R23) gave BFE equal to 21.57 ± 1.96 kcal/mol and -27.27 ± 1.80 kcal/mol, respectively. The same results were also retrieved from other cyclic peptides (seq. 5 (I23), BFE = -15.92 ± 1.58 kcal/mol, seq. 7 (K23) BFE = -26.26 ± 2.05 kcal/mol, and seq.8 (R23) BFE = -22.56 ± 2.03 kcal/mol) as well as from small linear peptides (seq. 11 (I23), BFE = -14.07 ± 1.65 kcal/mol, seq. 12 (K23) BFE = -21.90 ± 2.04 kcal/mol, and seq.14 (R23) BFE = -19.67 ± 1.70 kcal/mol). Modification of cysteine (seq. 1, BFE = -10.04 ± 1.77 kcal/mol) to serine (seq. 5, BFE = -15.92 ± 1.5 kcal/mol) can slightly increase binding free energy. A cyclic peptide containing 11 D-amino acids named NR58.3-14-3 is a broad-spectrum chemokine inhibitor30 and can bind to several chemokines such as CCL2, CCL3, CCL5 and CXCL12. However, in this work we aimed to develop a peptide that specifically interrupts the CCL5-HNP1 interaction. Thus, using the sequence of HNP1 which binds to CCL5 as a template for rational peptide design should be a promising approach to develop peptidic inhibitors to specifically disrupt the CCL5-HNP1 interaction. Since it has been described that NR58.3-14-3 can inhibit several chemokines, we then have investigated the influence of the peptide conformation on binding with CCL5. The stereochemistry of seq. 5 was thus converted from L- to D-amino acids (seq. 6) and the BFE of seq.6 was calculated and found to be rather low (-9.65 ± 1.39 kcal/mol). This BFE is however comparable to the calculated BFE for original sequence of β-strand 2 – 3 of HNP1 BFE = -9.48 ± 1.57) or the original template of peptide (seq. 1, BFE = -10.04 ± 1.77 kcal/mol). This result implies that the seq. 6 probably can bind CCL5 but its binding strength with CCL5 is less than the wild type HNP1. To summarize, seq. 3, 4, 5, 7, 8, 11, 12, 13, 14 and 16 gave lower binding free energy (more negative values) than the original sequence of HNP1 or the template peptide, implying that these peptides are likely to show high binding affinity with CCL5. Therefore, these peptides were selected for further


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synthesis, purchase and functional characterization. Biological activity of designed peptides to interrupt the CCL5-HNP1 interaction and interaction between the SKY peptide and CCL5 To assess the ability of the designed peptides to disturb monocyte adhesion, HNP1 and CCL5 were deposited on endothelial cells in presence of the peptides. Only CCL5+HNP1 were present in the control experiment. In these flow chamber assays, presence of peptides seq. 3, seq.11, seq. 12, seq.13, and seq. 16, resulted in significant reduction of HNP1-CCL5-driven monocyte adhesion (Figure 8). Among these 5 potential peptidic inhibitors, seq. 12 showed the highest potential to reduce monocyte adhesion (% residual monocyte adhesion = 11.8 ± 7.14%). It is worth noting that a scrambled peptide sequence (TYQRRSGKY) instead of sequence 12 (RRYGTSKYQ) was also tested in this assay; however, the scrambled peptide did not show significant inhibitory effect in the adhesion assay25. Therefore, we selected seq. 12 (RRYGTSKYQ), which we called “SKY” peptide according to the key residues that bind to CCL5 as shown in Figure 6(C), for further in vivo experiments, in which methods and results related to in vivo tests are recently published elswhere25. In those experiments, we have shown that interruption of the CCL5-HNP1 interaction by SKY peptide can significantly reduce monocyte adhesion in the cremaster muscle, large arteries and in a mouse model of myocardial ischemia-reperfusion injury. Interestingly, the SKY peptide can only interrupt the CCL5-HNP1 interaction in the mouse models in which human CCL5 and HNP1 were given and overexpressed but not in wild type mice25. These results indicate that the SKY peptide can specifically inhibit the interaction between human CCL5 and HNP1. In order to gain a better understanding of the interactions between CCL5 and the SKY peptide, to enable further rationalized which can be useful for further inhibitor optimization, the CCL5-SKY peptide complex was subjected to free MD simulation (150ns). The last MD snapshot was extracted for investigation of the CCL5-SKY peptide interactions by application of the receptor-ligand interaction



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module implemented in Discovery Studio Visualizer version 4.031. Strong interactions such as H-bond and salt-bridge32 interactions were observed between Ser6, Lys7 and Gln 9 of the SKY peptide and Glu66, Ser68, Arg21 and Arg44 of CCL5 as depicted in Figure 9 (A) and (B). Residues located at the N-terminus of the SKY peptide such as Arg2 and Tyr3 showed pi-sigma and pi-alkyl interaction with Tyr29, Arg59 and Ile62. Other strong interactions, such as polar hydrogen-pi33 and cation-pi34, were not detected between the SKY peptide and any residues of CCL5 as illustrated in Figure 9 (A) and (B).

Conclusion We have developed a protocol to determine a likely conformation between two proteins, CCL5 and HNP1, by a combination of protein-protein docking and binding free energy calculation. In contrast to more established and advanced methods that use large amounts of computer time and cost, our protocol represents a fast, simple and straightforward method since the docked protein-protein complexes were refined by energy minimization and short MD simulation (100 ps). From these simulations, snapshots were extracted for binding free energy calculation. Available binding affinity data (KD) were integrated and correlated with the calculated binding free energy in order to identify a likely binding mode of these two proteins. One out of a total of 21 different predicted CCL5-HNP1 complex conformations showed a moderate correlation between the experimental and predicted binding affinities (r2 = 0.44) while other conformations were all predicted to have lower or no correlations. In addition, we have demonstrated that use of snapshots from a relatively long MD simulation run (50 ns) for binding free energy calculation does not significantly improve a correlation with experimental data. We verified that entropy change upon binding has only limited contribution to the overall binding process and inclusion of entropy did not alter the selection of the most likely complex conformation. We then used the identified CCL5-HNP1 complex as a starting structure to rationally design peptides to interrupt the interaction between hCCL5 and HNP1. Peptides which gave lower binding free energy than the


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original sequence of HNP1 were purchased and tested for their activity in a functional competition assay. Finally, the peptides were tested in several experimental setups (both in vitro and in vivo25) and a peptide we called "SKY peptide" (RRYGTSKYQ) was verified to exhibit a high potency to disrupt the human CCL5-HNP1 interaction. To summarize, we have successfully translated in silico data to the rational design of peptides to interrupt the interaction between human CCL5 and HNP1. The most potent peptide (SKY peptide) shows a high potential to disturb the interaction between these two proteins in vitro and in vivo.

Computational and Experimental Section Protein-Protein Docking and Generation of CCL5-HNP1 complexes A schematic representation of our approach to identify a likely binding mode of CCL5-HNP1 is demonstrated in Figure 2. First, possible potential configurations of human CCL5-HNP1 heterodimeric complex were predicted by application of the protein-protein docking protocol implemented in ICMPro program35. Coordinates of the 3D structures of human CCL5 and HNP1 were obtained from Protein Data Bank (PDB), PDB codes 2L9H and 3HJ2, respectively. The chemokine domains for CCL5 of different species (Bos, Canis, Cavia, Equus, Felis, Mus and Sus) share a very high sequence identity (75-92%) as shown in Supporting Information Figure S5, which implies that these CCL5 orthologs are likely to share a conserved binding mode with HNP1. Next, we built CCL5 homology models for each of these different species by using the SWISS-MODEL WebServer36 and validated the quality of these models with WHATCHECK37. Human CCL5 (2L9H.pdb) was used as a structural template for the construction of the different CCL5 models. Different model structures of the complex between human HNP1 and different species of CCL5 were produced based on the conformations of human CCL5HNP1 complexes (21 different conformations per species) as were derived from protein-protein docking using protein-protein docking module implemented in ICM-Pro program as mentioned above.



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Subsequently, the structure of these derived complexes (168 complexes in total) was further refined by application of energy minimization and molecular dynamics simulations. Finally, the binding free energy of each complex was computed and a correlation between the experimental and calculated binding affinity for each conformation was made, as is described in Figure 2. Molecular Dynamics (MD) Simulations The retrieved structures of the CCL5-HNP1 complexes (168 complexes in total - 21 different conformations for each of the 8 species included) were optimized by energy minimization and by performing a short 100 ps MD simulation using AMBER12 program38. Prior to running each MD simulations, force fields and charges (Amber 99SB force field)39 were assigned to the CCL5-HNP1 complex. Then, the complex was solvated by addition of water (TIP3P model)40 within a 9 Å radius of the molecular surface of the complex. In order to neutralize the system, counter ions (Na+ or Cl-) were subsequently added by replacement of water. To relax the system, energy minimization was carried out in two consecutive steps. First, energy and positions of the solvent were adjusted by using 2000 steps of steepest descent energy minimization, followed by 2000 steps of conjugate gradient algorithm while the CCL5-HNP1 complex was fixed. Next, the energy of the whole system was minimized using the same algorithm as in the previous step, while no force constant was applied in this stage. After finishing the energy minimization phase, a position-restrained phase of MD simulation was carried for 100 ps by application of a weak force constraint (10 kcal/mol) to restrain the complex. During this phase, the temperature of the system was gradually increased from 0 to 300 K during the first few picoseconds and maintained at 300 K by application of Langevin dynamics with a collision frequency of 1 ps–1. After finishing this step, binding free energies were calculated by extracting the last snapshot from this position-restrained phase and then correlated with experimental binding affinities. However, protein flexibility can influence overall complex structure and consequently can affect a binding free energy. Therefore to investigate this effect, 3 different CCL5-HNP1 conformations (in


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total 24 complexes - 3 conformations for each of the 8 species tested), which showed a reasonable correlation in the fast analysis described above, and thus represent a likely binding mode for the CCL5HNP1 complex, were chosen and subjected for further free MD simulation run (50 ns). During this phase, the temperature and pressure were kept constant at 300 K and 1 bar, respectively. A SHAKE algorithm41 together with a time step of 2 fs was applied to satisfy the bond geometry constraint during the MD simulation. Electrostatic interactions were computed by use of the particle-mesh-Ewald (PME)42 method, and non-bonded interactions were calculated by setting the cut-off at 10 Å. Binding Free Energy Calculations Traditionally, binding free energy is computed by using an ensemble of snapshots from the equilibrium phase of MD simulation. To perform MD simulation for 100 ns or longer till all systems (168 complexes) reach their equilibrium phase and then to calculate binding free energy as employed in the conventional approach could take several months in this case, which we regarded as unfeasible. However, several works43-47 have demonstrated that use of MD snapshots (or even a single snapshot) obtained after energy minimization or a short MD simulation can yield the binding free energy even better than can be obtained from the use of an equilibrium phase ensemble. Therefore, to speed up calculations, for all complexes formed (n=168), snapshots extracted from the position-restrained phase of MD simulation were used for binding free energy calculation by application of the MMPBSA.py48 module implemented in AMBER12. Briefly, the binding free energy was calculated based on the thermodynamic equations described below: ∆Gbinding = Gcomplex – Gprotein - Gligand ∆G = ∆H – T∆S and

H = EvdW + Eele + Eint + Gsol

(1) (2) (3)

where ∆H and –T∆S are the binding enthalpy and entropy change upon binding. EvdW, Eele, Eint represent van der Waals, electrostatic interaction and internal energy, respectively. Gsol is the free



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energy of solvation which was calculated by solving generalized Born (GB) or Poisson Boltzmann (PB) model. The GB model 5 was used in this work. For those systems which were subjected to a free MD simulation run, the binding free energies were additionally calculated by processing an ensemble of snapshots (100 frames at time interval of 0.1 ns) as sampled from the last 10 ns of the simulations (during the period of 40-50 ns). In all cases, binding free energy was approximated from the enthalpy term solely (assuming ∆G ~ ∆H) since the relative binding free energies of a series of highly similar complexes was considered. Furthermore, entropy calculations are computationally expensive, and it has been reported44, 49, 50 that the inclusion of entropy does not improve the accuracy of binding free energy calculations considerably. Nonetheless, the absolute entropy was also calculated only for the predicted best binding CCL5-HNP1 complex to investigate the binding free energy of protein-protein interaction when the entropy is included. The entropy was computed by normal mode analysis integrated in the MMPBSA.py module in AMBER12. An ensemble of snapshots extracted during the last 10 ns was first energy minimized for 1000 steps with a gradient convergence criterion of 0.001 kcal/mol•Å and then was applied for normal mode analysis. The total entropy (Stot) was obtained from the equation below: Stot = Strans + Srot + Svib

(4)

where Strans, Srot, Svib are the changes in the degree of freedom of translational, rotational and vibrational entropy upon binding. Design of small peptides A schematic representation of a step-by-step process to design peptides to interrupt the interaction between CCL5 and HNP1 is illustrated in Figure 6. As a starting point, the proposed CCL5-HNP1 complex structure derived from the computational steps as detailed above, was used. Upon investigation of the interaction between CCL5 and HNP1 of this structure we found that residues from β-strands 2 and 3 of HNP1 (17RRYGTCIYQGRLWAFCC33) contain the main key residues that interact


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with CCL5 (Figure 6(A). Thus, this HNP1 fragment was used as a structural template to design inhibitory peptides to prevent a CCL5-HNP1 complex formation. As previously reported, chemically and conformationally stable peptidic chemokine inhibitors can be designed by covalent tethering of the N- and C-terminus via an engineered disulfide bond, such as to obtain a cyclic or a β-hairpin-like conformation51. Hence, a similar strategy was adapted here. We therefore introduced a cysteine residue at the N-terminus to allow formation of a disulfide bond with the cysteine at the C-terminus (position 33) to create a stable cyclic peptide Figure 6(B) By doing so, we obtained the peptide seq. 1, which was a template for design of other peptides. The peptide seq. 1 contained 4 cysteine residues, but only the cysteins at the N- and C-terminus (position 1 and 18) formed one disulfide bond. From the structural analysis of this proposed peptide, we can observe that several neutral residues such as threonine (Thr) and isoleucine (Ile) contact the negatively-charged surface of CCL5 Figure 6(B). Therefore, to improve the binding affinity with CCL5, we modified these residues to positively-charged residues. In order to avoid potential peptide dimerization, the cysteine residue at position 22 was mutated to serine, which has similar properties. Finally, we have designed a total of 8 different cyclic peptides, based on the sequence of the β-strand 2 and 3 of HNP1 and in addition 6 small linear peptides were designed based on the sequence of either β-strand 2 or β-strand 3 of HNP1. A conformation of the designed peptides in complex with CCL5 was derived by using the conformation of β-strands 2 and 3 of HNP1 in complex with CCL5 (Figure 6(B)) as a starting structure and then manually mutating the specific residue to the particular designed residue. These peptides in complex with CCL5 were each subjected to energy minimization, and consequently a short MD simulation (100 ps) and finally binding free energy calculation using the same protocols as described above. However, all snapshots (100 frames at time interval of 1 ps) were applied for calculating binding free energy calculation of the designed peptides. Candidate peptides which exhibited predicted low binding free energy were selected for synthesis and obtained from Pepscan Therapeutics (Lelystad, The Netherlands) and subsequently tested for biological



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activity in different experiments. Peptides were synthesized by standard Fmoc chemistry solid phase peptide chemistry, after cleavage peptides were purified and analyzed using C18 reversed phase HPLC, and mass spectrometry. Yields and purity based on HPLC are indicated in Table S2. With the exception of Sequences 16 (89.4%), all peptides reached very high purities from 95 to > 99%. Surface Plasmon Resonance and Flow Chamber Assay Surface plasmon resonance on a Biacore X100 system (GE Healthcare) was used to investigate interaction between CCL5 and HNP1. Three different experiments were carried out. In a first experiment, biotinylated CCL5 was immobilized on a streptavidin sensor chip and HNP1 diluted in HBS-EP+ was used as analyte. In a reverse experiment, HNP1 was immobilized on a CM3 chip and perfused with CCL5. Finally, to better characterize the interaction between CCL5 and HNP1, a direct binding between different species of CCL5 (Mus, Canis, Felis, Equus, Bos, Cavia, and Sus,) and HNP1 was additionally investigated. In this experiment, HNP1 was immobilized and CCL5s were used as analytes. A flow rate was set to 10 µl/min with running buffer. BIAevalution software was used to analyze results. Human umbilical vein endothelial cells (HUVEC) were grown to confluence and preincubated with CCL5 (1 µg/ml) and HNP1 (10 µg/ml) in the presence of HNP1-CCL5 disrupting peptides (100 µg/ml) for 5 minutes prior to perfusion of monocyte over the adhered endothelial layer. Freshly isolated human classical monocytes were labeled with green calcein, suspended in flow adhesion buffer (10 mM HEPES, 0.5% BSA, 1 mM MgCl2, 1 mM CaCl2) and perfused in a parallel wall flow chamber at a concentration of 106 monocytes/ml at a shear rate of 1.5 dyne/cm2. The adhesion of monocytes under different conditions was quantified as adherent cells per field of view.


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Associated Content Supporting Information The Supporting Information includes Table S1. binding free energy (kcal/mol), residual values and zscore of different complexes of conformation number 9, Figure S1. 21 different binding modes of CCL5-HNP1 complex, Figure S2, percent purity of each synthesized peptides, Figure S2. RMSD values (Å) obtained from the MD simulations of different species of CCL5 in complex with HNP1 of conformation number 8, 9 and 17, Figure S3 and S4. A correlation between experimental and calculated ∆Gbinding before and after removing outliers using averaging snapshot from the position-restrained phase (100 ps) or from the equilibrium phase (40-50 ns), respectively, and Figure S5. Sequence alignment and percent sequent identity between different species of CCL5. This material is available free of charge via the Internet at http://pubs.acs.org.

Author Information Corresponding Author Kanin Wichapong, Ph.D. Phone: +31-43-3881539; Fax: +31-43-3884159 E-mail: [email protected], [email protected] Notes The authors declare no competing financial interest.

Acknowledgements J.-E.A. was supported by a postdoctoral scholarship from the Alexander von Humboldt Foundation. C.W. was funded by Deutsche Forschungsgemeinschaft (SFB914-B08, SFB1123-A01), European Research Council (ERC AdG°249929) and NWO (VICI project 918.10.616). O.S. received funding



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from the German Research Foundation (SO876/6-1, SFB914 B08, SFB1123 A06 & B05), the Else Kröner Fresenius Stiftung, the NWO (VIDI project 91712303), and the LMUexellent program. This work was supported by grants from Cyttron II (FES0908 to G.A.F.N. and T.M.H.). G.A.F.N. is additionally funded by The Netherlands Organisation for Scientific Research (Medium Investment Grant 91112016). References 1. Braun, P.; Gingras, A. C., History of Protein-Protein Interactions: From Egg-White to Complex Networks. Proteomics 2012, 12 (10), 1478-1498. 2. Krull, F.; Korff, G.; Elghobashi-Meinhardt, N.; Knapp, E. W., ProPairs: A Data Set for ProteinProtein Docking. J. Chem. Inf. Model. 2015, 55 (7), 1495–1507. 3. Rognan, D., Rational Design of Protein-Protein Interaction Inhibitors. MedChemComm 2015, 6 (1), 51-60. 4. Zarzycka, B.; Kuenemann, M. A.; Miteva, M. A.; Nicolaes, G. A. F.; Vriend, G.; Sperandio, O., Stabilization of Protein-Protein Interaction Complexes Through Small Molecules. Drug Discovery Today 2016, 21 (1), 48-57. 5. Labbe, C. M.; Kuenemann, M. A.; Zarzycka, B.; Vriend, G.; Nicolaes, G. A.; Lagorce, D.; Miteva, M. A.; Villoutreix, B. O.; Sperandio, O., iPPI-DB: An Online Database of Modulators of ProteinProtein Interactions. Nucleic Acids Res. 2016, 44 (D1), D542-547. 6. Mori, M.; Vignaroli, G.; Cau, Y.; Dinic, J.; Hill, R.; Rossi, M.; Colecchia, D.; Pesic, M.; Link, W.; Chiariello, M.; Ottmann, C.; Botta, M., Discovery of 14-3-3 Protein- Protein Interaction Inhibitors that Sensitize Multidrug-Resistant Cancer Cells to Doxorubicin and the Akt Inhibitor GSK690693. ChemMedChem 2014, 9 (5), 973-983.


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Table 1. A correlation (r2) between experimental and calculated binding free energy (MM/GBSA or MM/PBSA) of each individual conformation of different species of CCL5 in complex with human HNP1 Conformation number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

r2 value between exp. and cal. binding free energy MM/GBSA MM/PBSA 0.03 0.06 0.25 0.13 0.01 0 0.06 0.04 0 0.03 0.28 0.15 0.05 0.12 0.02 0.64 0.41 0.44 0 0.09 0 0.14 0.11 0.15 0.44* 0.67* 0.11 0.04 0 0.08 0.01 0 0.32 0.29 0.02 0.07 0.01 0.04 0.04 0.03 0 0.01

* inverse correlation



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Table 2. Comparison binding free energy (kcal/mol) derived from conformation number 8, 9 and 17 by using average snapshots from 40-50 ns of MD simulations Different conf. 8 conf. 9 conf. 17 species KD exp. CCL5-HNP1 (nM) ∆Gbinding MM/GBSA MM/PBSA MM/GBSA MM/PBSA MM/GBSA MM/PBSA complex Human 3.87 -11.55 -11.52 ± 6.72 -15.88 ± 6.73 -65.49 ± 8.95 -63.41 ± 8.73 -37.66 ± 5.22 -45.87 ± 6.16 Felis 4.05 -11.51 -19.97 ± 3.76 -32.69 ± 4.54 -51.97 ± 7.01 -52.72 ± 7.30 -24.15 ± 4.10 -29.47 ± 4.64 Bos 4.29 -11.49 -21.52 ± 7.72 -27.28 ± 9.55 -39.29 ± 5.38 -44.04 ± 6.17 -48.42 ± 5.06 -62.54 ± 5.68 Cavia 4.29 -11.49 -40.67 ± 6.15 -48.22 ± 7.52 -42.76 ± 4.09 -42.62 ± 5.24 -41.84 ± 4.51 -42.57 ± 5.66 Equus 7.01 -11.19 -21.54 ± 4.97 -29.65 ± 6.08 -51.72 ± 5.85 -54.60 ± 6.31 -22.66 ± 3.59 -34.19 ± 4.22 Mus 14.7 -10.75 -19.65 ± 9.08 -26.09 ± 8.24 -40.01 ± 7.92 -43.21 ± 8.44 -42.59 ± 4.10 -46.41 ± 5.31 Canis 31.5 -10.30 -11.41 ± 5.66 -19.98 ± 7.29 -41.78 ± 4.17 -47.23 ± 4.87 -28.06 ± 4.42 -34.32 ± 4.97 Sus 49.0 -10.30 -33.46 ± 5.76 -44.00 ± 6.79 -33.67 ± 6.17 -35.35 ± 5.48 -39.64 ± 4.71 -48.05 ± 4.77 Table 3. Enthalpy (∆H) values (kcal/mol) derived from MM/GBSA and MM/PBSA, entropy (T∆S) values (kcal/mol) and absolute binding free energy (abs. ∆G, kcal/mol) of conformation number 9 obtained by averaging snapshots from 40-50 ns of MD simulations, and the different between exp. and cal. ∆Gbinding approximated from the enthalpy term only, and also between exp. and abs. ∆Gbinding including the entropy change upon binding |exp. ∆G – ∆H Different Enthalpy (∆H), (~ cal. ∆G) abs. ∆Gbinding |exp. ∆G – abs. ∆G| (~ cal. ∆G) | exp. Entropy species CCL5KD (T∆S) (nM) ∆Gbinding HNP1 MM/ MM/ MM/ MM/ MM/ MM/ MM/GBSA MM/PBSA complex GBSA PBSA GBSA PBSA GBSA PBSA Human 3.87 -11.55 -65.49 ± 8.95 -63.41 ± 8.73 -40.64 ± 5.82 -24.85 -22.77 53.94 51.86 13.30 11.22 Felis 4.05 -11.51 -51.97 ± 7.01 -52.72 ± 7.30 -34.74 ± 5.86 -17.23 -17.98 40.46 41.21 5.72 6.47 Bos 4.29 -11.49 -39.29 ± 5.38 -44.04 ± 6.17 -31.06 ± 5.66 -8.20 -12.94 27.80 32.55 3.29 1.45 Cavia 4.29 -11.49 -42.76 ± 4.09 -42.62 ± 5.24 -34.31 ± 6.07 -8.45 -8.30 31.27 31.13 3.04 3.19 Equus 7.01 -11.19 -51.72 ± 5.85 -54.60 ± 6.31 -42.04 ± 6.30 -9.68 -12.56 40.53 43.41 1.51 1.37 Mus 14.7 -10.75 -40.01 ± 7.92 -43.21 ± 8.44 -35.48 ± 7.34 -4.52 -7.72 29.26 32.46 6.23 3.03 Canis 31.5 -10.30 -41.78 ± 4.17 -47.23 ± 4.87 -33.39 ± 5.71 -8.39 -13.83 31.48 36.93 1.91 3.53 Sus 49.0 -10.30 -33.67 ± 6.17 -35.35 ± 5.48 -30.08 ± 6.51 -3.60 -5.27 23.37 25.05 6.70 5.03


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Table 4. The sequence of designed peptides and their predicted binding free energy (MM/PBSA). Residues which are highlighted in red are residues that were mutated from the original sequence of HNP1. Binding Free Energy (MM/PBSA) (kcal/mol)

Seq. num. HNP-1 seq

17

RRYGTCIYQGRLWAFCC33

1

CRRYGTCIYQGRLWAFCC (SS bond Cys1-Cys18)

-10.04 ± 1.77

2

CRRYGTAIYQGRLWAFAC (SS bond Cys1-Cys18)

-9.89 ± 1.56

3

CRRYGTAKYQGRLWAFAC (SS bond Cys1-Cys18)

-21.57 ± 1.96

4

CRRYGTARYQGRLWAFAC (SS bond Cys1-Cys18)

-27.27 ± 1.80

5

CRRYGTSIYQGRLWAFSC (SS bond Cys1-Cys18)

-15.92 ± 1.58

6

CRRYGTSIYQGRLWAFSC (SS bond Cys1-Cys18) (D-amino)

-9.65 ± 1.39

7

CRRYGTSKYQGRLWAFSC (SS bond Cys1-Cys18)

-26.26 ± 2.05

8

CRRYGTSRYQGRLWAFSC (SS bond Cys1-Cys18)

-22.56 ± 2.03

-9.48 ± 1.57

9

RRYGTCIYQ (beta-strand 2)

-10.95 ± 1.75

10

RRYGTAIYQ

-5.12 ± 1.72

11

RRYGTSIYQ

-14.07 ± 1.65

12

RRYGTSKYQ

-21.90 ± 2.04

13

RRYGTSRYQ

-19.67 ± 1.70

14

YQGRLWAFCC (beta-strand 3)

-15.24 ± 1.28

15

YQGRLWAFAA

-12.47 ± 1.07

16

YQGRLWAFSS

-14.59 ± 1.11



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Figure Captions Figure 1. HNP1 and CCL5 cooperate during adhesion of classical monocytes. Human umbilical vein endothelial cells (HUVEC) were grown to confluence and preincubated with CCL5 (1 µg/ml) and HNP1 (10 µg/ml) as indicated. Human classical monocytes labelled with calcein were perfused at a shear rate of 1.5 dyne/cm2. The adhesion of monocytes was quantified as adherent cells per field of view. n = 6 per group; * indicates p < 0.05 vs. ctrl as assessed by Kruskal Wallis test. Figure 2. Schematic representation of the workflow used to identify a likely binding mode of the hCCL5-HNP1 complex. (a.) Protein-protein docking. An ensemble of 21 potential binding modes between human CCL5 (secondary structure colors, red=α-helix, and yellow=β-strand) and HNP1 (magenta ribbon) were derived via protein-protein docking, 6 representative examples are shown. (b) Homology modelling. Homology models of 7 different species of CCL5 (different colors) were constructed based on the structure of human CCL5 (hCCL5). (c) Structure superpositioning. Homology models of CCL5s were superimposed on each of 21 possible conformations of human CCL5 in complex with HNP1, as were obtained from step a. resulting in different orthologs of CCL5 in complex with HNP1. (d) Molecular Dynamics Simulation and Binding Free Energy calculation. The complex between each species of CCL5 in complex with HNP1 was subjected to MD simulation and binding free energy calculation and the correlation between experimental (exp.) and calculated (cal.) binding free energy for each conformation was investigated. A total of 8 * 21 (orthologs * conformations) was subjected to binding free energy calculation. A likely binding mode should show a correlation for each of the 8 tested orthologs as close as possible to the ideal graph (d,top) whereas a wrong binding mode should exhibit no correlation (d,bottom). Figure 3. A correlation between exp. and cal. ∆Gbinding of conf. 8, 9, 13 and 17 in which binding free energy was calculated by using a snapshot from the position-restrained phase (100 ps) of MD simulations


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Figure 4. A correlation between exp. and cal. ∆Gbinding of conf. 8, 9 and 17 in which binding free energy was calculated by using an ensemble of snapshots during 40-50 ns of MD simulations Figure 5. Comparison a correlation between exp. and cal. ∆Gbinding approximated from the enthalpy term only (∆G ~ ∆H), and between exp. and absolute ∆Gbinding including the entropy change upon binding Figure 6. The flowchart of an approach to design peptide to interrupt the CCL5-HNP1 interaction. (a.) A likely binding mode between human CCL5 (green ribbon) and HNP1 (magenta ribbon) was used as a starting structure. (b) A starting designed peptide (a cyclic peptide with a disulfide bond connecting the N- and C- terminal) was projected on electrostatic molecular surface of human CCL5 (red = negatively-charged surface and blue = positively-charged surface). (c). The starting conformation of the SKY peptide (the most potent peptide) in complex with human CCL5. Figure 7. Decomposition (DC) Analysis of HNP1 with CCL5 of conformation number 9 Figure 8. Designed peptides disturb HNP1-CCL5-mediated monocyte adhesion. Human umbilical vein endothelial cells (HUVEC) were grown to confluence and preincubated with CCL5 (1 µg/ml) and HNP1 (10 µg/ml) in the presence or absence of indicated peptides. Human classical monocytes labelled with calcein were perfused at a shear rate of 1.5 dyne/cm2. The adhesion of monocytes was quantified as adherent cells per field of view. The control (treatment with peptide vehicle) was set to 100% and adhesion in presence of peptides is expressed relative to control. n = 4 per group; * indicates p < 0.05 vs. ctrl as assessed by Kruskal Wallis test. Figure 9. Predicted interactions between CCL5 and the SKY peptide. (A) The sky peptide (magenta and cyan (SKY residues) stick) projected on molecular surface of CCL5 (gray), and (B) Schematic representation of the interactions between the SKY peptide (chemical structure as shown) with residues at the pocket of CCL5. Amino acid residues of CCL5 are in black whereas for the SKY are in red.



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Figure 1.


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Figure 2.



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Figure 3.


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Figure 4.



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Figure 5.


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Figure 6.



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Figure 7.

Figure 8.


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Figure 9 (A)

Figure 9 (B)



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Table of Contents Graphic


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Structure-Based Design of Peptidic Inhibitors of the Interaction

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