Dr Alexandre G De Brevern
email: alexandre.debrevern@univ-paris-diderot.fr

Ludovic Autin #

Yves Colin

Olivier Bertrand

Catherine Etchebest

To be published in Infectious Diseases -Drug Targets (2009) in silico studies on DARC

Keywords: Duffy Antigen / Receptor for Chemokine; CXCL-8, Duffy Binding Protein, chemokines, malaria, Plasmodium vivax. in silico DARC

The Duffy Antigen/Receptor for Chemokine (DARC) is a seven segment transmembrane protein. It was firstly discovered as a blood group antigen and was the first specific gene locus assigned to a specific autosome in man. It became more famous as an erythrocyte receptor for malaria parasites (Plasmodium vivax and Plasmodium knowlesi), and finally for chemokines. DARC is an unorthodox chemokine receptor as (i) it binds chemokines of both CC and CXC classes and (ii) it lacks the Asp-Arg-Tyr consensus motif in its second cytoplasmic loop hence cannot couple to G proteins and activate their signaling pathways. DARC had also been associated to cancer progression, numerous inflammatory diseases, and possibly to AIDS.

In this review, we will summarize important biological data on DARC. Then we shall focus on recent development of the elaboration and analyzes of structural models of DARC.

We underline the difficulty to propose pertinent structural models of transmembrane protein using comparative modeling process, and other dedicated approaches as the Protein Blocks.

The chosen structural models encompass most of the biochemical data known to date. Finally, we present recent development of protein -protein docking between DARC structural models and CXCL-8 structures. We propose a hierarchal search based on separated rigid and flexible docking.

Biology and Pathology of the DARC

The Duffy antigen receptor for chemokines (DARC) is a protein which has a long history. It was discovered at the beginning of the 50"s as a blood group antigen, after the identification of antibodies against the protein in the serum of a polytransfused haemophilic named Duffy. DARC carries the antithetic antigens Fy a and Fy b in humans [START_REF] Cutbush | The Duffy blood group system[END_REF][START_REF] Ikin | Discovery of the expected haemagglutinin, anti-Fyb[END_REF]. DARC, as a blood group, was extensively studied and its mode of inheritance defined [START_REF] Albrey | A new antibody, anti-Fy3, in the Duffy blood group system[END_REF][START_REF] Chown | The Duffy Blood Group System in Caucasians: Evidence for a New Allele[END_REF][START_REF] Compton | The duffy blood group system in transfusion reactions: a reviw of the literature and report of four cases[END_REF][START_REF] Gawrzewski | Studies on the Duffy blood group system (FYa) in the population of Cracow (Poland)[END_REF][START_REF] Lewis | The Duffy blood group system in Caucasians. A further population sample[END_REF][START_REF] Marsh | Present status of the Duffy blood group system[END_REF][START_REF] Marsh | The Duffy blood group system: a review of recent developments[END_REF][START_REF] Mohr | Note on the inheritance of the Duffy blood-group system and its possible interaction with the Rhesus groups[END_REF][START_REF] Oberdorfer | A second example of anti-Fy3 in the Duffy blood group system[END_REF][START_REF] Beattie | A review: the Duffy blood group system[END_REF].

Interestingly, it was the first specific gene locus assigned to a specific autosome in man [START_REF] Donahue | Probable assignment of the Duffy blood group locus to chromosome 1 in man[END_REF].

A milestone in DARC studies was the discovery that it is an erythrocyte receptor for malaria parasites. Indeed, in vitro and in vivo studies performed on American volunteering detainees have shown that erythrocytes of Fy(a -b -) individuals that do not express Duffy cannot be invaded by Plasmodium knowlesi [START_REF] Miller | Erythrocyte receptors for (Plasmodium knowlesi) malaria: Duffy blood group determinants[END_REF] and Plasmodium vivax [START_REF] Miller | The resistance factor to Plasmodium vivax in blacks. The Duffy-blood-group genotype, FyFy[END_REF]. The Duffy negative phenotype is due to homozygosity for a promoter polymorphism (-46C) that disrupts the binding site for a transcription factor (GATA-1) required for the DARC chemokine receptor to be expressed on the cell surface of erythrocytes [START_REF] Tournamille | Disruption of a GATA motif in the Duffy gene promoter abolishes erythroid gene expression in Duffy-negative individuals[END_REF]. This mutation is ubiquitous in West Africa proving that it was due to a strong selection pressure. An important point to be stressed is that this mutation abolishes expression of DARC in the erythrocytic lineage but does not hinder expression in other cell types in which another promoter is operative.

It must be noticed that Plasmodium vivax malaria [START_REF] Carlton | Comparative genomics of the neglected human malaria parasite Plasmodium vivax[END_REF] is the most geographically widespread and the second prevalent cause of malaria. Among 2.6 billion people at risk of malaria infection, annual estimates of P. vivax cases range from 130 to 435 million [START_REF] Guerra | Mapping the global extent of malaria in 2005[END_REF]. 90% of these infections occur outside Africa [START_REF] Mendis | The neglected burden of Plasmodium vivax malaria[END_REF]. P. vivax transmission agents to humans are Anopheles of various species [START_REF] Sattabongkot | Plasmodium vivax transmission: chances for control?[END_REF]. Chloroquine was one of the first and still the most commonly used drug for treatment for P. vivax malaria. However, chloroquine resistance P. vivax becomes a threatening problem [START_REF] Talib | Chloroquine-resistant Plasmodium vivax malaria in infancy and childhood[END_REF][START_REF] Whitby | Chloroquine-resistant Plasmodium vivax[END_REF][START_REF] De | Chloroquine-resistant Plasmodium vivax, Brazilian Amazon[END_REF][START_REF] Dua | Chloroquine resistant Plasmodium vivax malaria in India[END_REF][START_REF] Teka | Chloroquine-resistant Plasmodium vivax malaria in Debre Zeit, Ethiopia[END_REF][START_REF] Kurcer | The decreasing efficacy of chloroquine in the treatment of Plasmodium vivax malaria, in Sanliurfa, south-eastern Turkey[END_REF][START_REF] Kurcer | Efficacy of chloroquine in the treatment of Plasmodium vivax malaria in Turkey[END_REF][START_REF] Sumawinata | Very high risk of therapeutic failure with chloroquine for uncomplicated Plasmodium falciparum and P. vivax malaria in Indonesian Papua[END_REF][START_REF] Girod | Anopheles darlingi bionomics and transmission of Plasmodium falciparum, Plasmodium vivax and Plasmodium malariae in Amerindian villages of the Upper-Maroni Amazonian forest, French Guiana[END_REF].

Searching for a vaccine eliciting antibodies able to interfere with interaction of DARC with its malarial partner the Duffy Binding Protein (or DBP) is an active research field [START_REF] Beeson | Towards a vaccine against Plasmodium vivax malaria[END_REF][START_REF] Grimberg | Plasmodium vivax invasion of human erythrocytes inhibited by antibodies directed against the Duffy binding protein[END_REF][START_REF] Moreno | Preclinical assessment of the receptor-binding domain of Plasmodium vivax Duffy-binding protein as a vaccine candidate in rhesus macaques[END_REF][START_REF] King | Naturally acquired Duffy-binding proteinspecific binding inhibitory antibodies confer protection from blood-stage Plasmodium vivax infection[END_REF]. However some recent data were issued in the literature suggesting that P. vivax might infest Duffy negative people in East Africa and Brazil [START_REF] Ryan | Evidence for transmission of Plasmodium vivax among a duffy antigen negative population in Western Kenya[END_REF][START_REF] Rosenberg | Plasmodium vivax in Africa: hidden in plain sight?[END_REF][START_REF] Culleton | Failure to detect Plasmodium vivax in West and Central Africa by PCR species typing[END_REF][START_REF] Cavasini | Duffy blood group gene polymorphisms among malaria vivax patients in four areas of the Brazilian Amazon region[END_REF] DARC, while it was well known as a receptor for P. vivax was more recently identified as a chemokine receptor because Duffy-positive but not Duffy-null erythrocytes can bind CXCL8 (IL-8) [START_REF] Darbonne | Red blood cells are a sink for interleukin 8, a leukocyte chemotaxin[END_REF][START_REF] Horuk | A receptor for the malarial parasite Plasmodium vivax: the erythrocyte chemokine receptor[END_REF] and is involved in physiological chemiotactism [START_REF] Murdoch | Chemokine receptors and their role in inflammation and infectious diseases[END_REF]. Contrary to other chemokine receptors, it binds members of both the CC and CXC chemokine families. It binds angiogenic ELR + (i.e. with the Glutamine-Leucine-Arginine motif at the N-terminus) CXC chemokines, as well as some CC chemokines [START_REF] Neote | Functional and biochemical analysis of the cloned Duffy antigen: identity with the red blood cell chemokine receptor[END_REF][START_REF] Neote | Molecular cloning, functional expression, and signaling characteristics of a C-C chemokine receptor[END_REF][START_REF] Chaudhuri | Expression of the Duffy antigen in K562 cells. Evidence that it is the human erythrocyte chemokine receptor[END_REF], but it does not bind ELR -CXC chemokines and the C chemokine [START_REF] Szabo | Chemokine class differences in binding to the Duffy antigen-erythrocyte chemokine receptor[END_REF]. DARC ligands with high affinity include CXCL1 (GRO ), CXCL2 (GRO ), CXCL3 (GRO ), CXCL4 (PF4), CXCL5 (ENA-78), CXCL7 (NAP-2), CXCL8 (IL-8), CCL2 (MCP-1, MCAF, JE), CCL5 (RANTES), CCL7 (MCP-3), CCL11 (Eotaxin), CCL13 (MCP-4), CCL14 (HCC-1) and CCL17 (TARC) [START_REF] Comerford | Post-translational control of chemokines: a role for decoy receptors?[END_REF].

The DARC receptor plays a complex role in regulating chemokine levels, at least in part due to its ability to bind chemokines and adsorb them onto the red cell surface, thereby acting as a chemokine reservoir; it is so considered as typical decoy receptor or a sink [START_REF] Darbonne | Red blood cells are a sink for interleukin 8, a leukocyte chemotaxin[END_REF].

Chemokines binding to DARC can largely be categorized as inflammatory. Chemokine binding to DARC does not lead to intracellular signaling. Because DARC lacks the Asp-Arg-Tyr consensus motif in its second cytoplasmic loop, it cannot couple to G proteins and activate the subsequent signaling pathways [START_REF] Hadley | From malaria to chemokine receptor: the emerging physiologic role of the Duffy blood group antigen[END_REF]. DARC was associated to numerous physiological and pathological responses [START_REF] Wasniowska | The Duffy blood group antigen: an update[END_REF] as the other atypical chemokine receptor D6 [START_REF] Graham | D6 and the atypical chemokine receptor family: novel regulators of immune and inflammatory processes[END_REF]. DARC is thought to play a part in homeostasis either through clearance of inflammatory chemokines from the circulation or maintenance of plasma levels by subsequent release from the red cell surface [START_REF] Pruenster | Throwing light on DARC[END_REF]. But the mechanisms by which DARC is implicated in inflammation are far to be completely understood. It has been reported that DARC might heterodimerize with CXCR-5 and so interfere with function of this important effector [START_REF] Chakera | The duffy antigen/receptor for chemokines exists in an oligomeric form in living cells and functionally antagonizes CCR5 signaling through hetero-oligomerization[END_REF].

DARC expressed in endothelial cells of post capillary venules might assist leukocyte migration to sites of inflammation [START_REF] Hadley | From malaria to chemokine receptor: the emerging physiologic role of the Duffy blood group antigen[END_REF], Noticeably it has been suggested that DARC might ensure transcytosis of chemokines from the intravascular to the extravascular spaces hence favor migration of leucocytes involved in inflammation [START_REF] Pruenster | The Duffy antigen receptor for chemokines transports chemokines and supports their promigratory activity[END_REF]. This mechanism clearly is operative even in individuals lacking Duffy antigen expression on red cells. Interactions of chemokines with DARC support their activity on apposing leukocytes in vitro and in vivo [START_REF] Pruenster | Throwing light on DARC[END_REF]. The up-regulation of DARC expression by endothelial cells has been demonstrated in many human inflammatory and infectious diseases [START_REF] Bruhl | Expression of DARC, CXCR3 and CCR5 in giant cell arteritis[END_REF][START_REF] Gardner | Temporal expression pattern of Duffy antigen in rheumatoid arthritis: up-regulation in early disease[END_REF][START_REF] Lappin | Light from DARCness[END_REF][START_REF] Lee | Duffy antigen facilitates movement of chemokine across the endothelium in vitro and promotes neutrophil transmigration in vitro and in vivo[END_REF][START_REF] Liu | Up-regulation of Duffy antigen receptor expression in children with renal disease[END_REF][START_REF] Patterson | Expression of the duffy antigen/receptor for chemokines (DARC) by the inflamed synovial endothelium[END_REF][START_REF] Segerer | The Duffy antigen receptor for chemokines is up-regulated during acute renal transplant rejection and crescentic glomerulonephritis[END_REF][START_REF] Vandercappellen | The role of CXC chemokines and their receptors in cancer[END_REF]. For instance, DARC is implicated in numerous pathologies implicating "classical" chemokine receptors like in giant cell arteritis [START_REF] Bruhl | Expression of DARC, CXCR3 and CCR5 in giant cell arteritis[END_REF]. Besides its role in inflammation, definitely DARC seems to play a role in cancer.

Epidemiological data suggest that lack of DARC on erythrocytes favors early onset and aggressiveness of prostate cancer observed in African Americans [START_REF] Lentsch | The Duffy antigen/receptor for chemokines (DARC) and prostate cancer. A role as clear as black and white?[END_REF]. These data are backed up by experimental models using genetically modified mice [START_REF] Shen | The Duffy antigen/receptor for chemokines (DARC) regulates prostate tumor growth[END_REF]. On the other hand expression of DARC on malignant lung or breast cancer cells might be associated with less growth and metastatic potential this being possibly related to sequestration of angiogenic chemokines and inhibition of tumor vascularisation [START_REF] Addison | Overexpression of the duffy antigen receptor for chemokines (DARC) by NSCLC tumor cells results in increased tumor necrosis[END_REF][START_REF] Wang | Enhanced expression of Duffy antigen receptor for chemokines by breast cancer cells attenuates growth and metastasis potential[END_REF]. Susceptibility to asthma and atopy among certain populations of African descent was correlated also to absence of DARC expression on red cells [START_REF] Vergara | Gene encoding Duffy antigen/receptor for chemokines is associated with asthma and IgE in three populations[END_REF].

A recent study suggests that DARC influences HIV/AIDS susceptibility by mediating trans-infection of HIV-1 and by affecting both chemokine-HIV interactions and chemokinedriven inflammation [START_REF] He | Duffy antigen receptor for chemokines mediates trans-infection of HIV-1 from red blood cells to target cells and affects HIV-AIDS susceptibility[END_REF]. The almost universal presence of the HIV-susceptible DARC genotype in Africans might be an important contribution to understanding the massive extent of the HIV-1 epidemic in Africa [START_REF] Walton | HIV and chemokine binding to red blood cells--DARC matters[END_REF][START_REF] Team | Study links genetic polymorphism to susceptibility to HIV infection[END_REF]. Worth of notice This results was considered as one of the eight most important discovery of the year in human and medical genetics by the Annals of New York Academy of Sciences [START_REF] Smith | The year in human and medical genetics. Highlights of 2007-2008[END_REF].

Expression in cell lines of mutant DARC molecules and studies of their affinity for CXCL8 and various antibodies [START_REF] Van Kim | The 1.35-kb and 7.5-kb Duffy mRNA isoforms are differently regulated in various regions of brain, differ by the length of their 5' untranslated sequence, but encode the same polypeptide[END_REF][START_REF] Tournamille | Molecular basis and structure-activity relationships of the Duffy blood group antigens: chemokine and Plasmodium vivax receptors[END_REF][START_REF] Tournamille | Sequence, evolution and ligand binding properties of mammalian Duffy antigen/receptor for chemokines[END_REF][START_REF] Tournamille | Structure-function analysis of the extracellular domains of the Duffy antigen/receptor for chemokines: characterization of antibody and chemokine binding sites[END_REF][START_REF] Tournamille | Close association of the first and fourth extracellular domains of the Duffy antigen/receptor for chemokines by a disulfide bond is required for ligand binding[END_REF][START_REF] Tournamille | Arg89Cys substitution results in very low membrane expression of the Duffy antigen/receptor for chemokines in Fy(x) individuals[END_REF][START_REF] Wasniowska | The Fya, Fy6 and Fy3 epitopes of the Duffy blood group system recognized by new monoclonal antibodies: identification of a linear Fy3 epitope[END_REF][START_REF] Czerwinski | Mutational analysis of the N-glycosylation sites of Duffy antigen/receptor for chemokines[END_REF][START_REF] Wasniowska | Identification of the Fy6 epitope recognized by two monoclonal antibodies in the N-terminal extracellular portion of the Duffy antigen receptor for chemokines[END_REF][START_REF] Hadley | Postcapillary venule endothelial cells in kidney express a multispecific chemokine receptor that is structurally and functionally identical to the erythroid isoform, which is the Duffy blood group antigen[END_REF][START_REF] Sim | Receptor and ligand domains for invasion of erythrocytes by Plasmodium falciparum[END_REF] helped to define the coarse topology of DARC [START_REF] Tournamille | Fine mapping of the Duffy antigen binding site for the Plasmodium vivax Duffy-binding protein[END_REF],

i.e., a 7 transmembrane protein with four extracellular loops (named Extra Cellular Domains or ECDs) and four intracellular loops (named Intra Cellular Domains or ICDs). These experimental data coupled with sophisticated molecular modeling had allowed proposing pertinent structural models [START_REF] De Brevern | A structural model of a seven-transmembrane helix receptor: the Duffy antigen/receptor for chemokine (DARC)[END_REF] able to explain the docking of DARC with CXCL-8 [START_REF] Singh | Structure of the DBL3x domain of pregnancy-associated malaria protein VAR2CSA complexed with chondroitin sulfate A[END_REF] and in the future hopefully with the malaria protein DBP. Transmembrane protein represents about ~25% of proteins coded by genomes. They are composed of two major classes: all-, e.g. rhodopsin and all-, e.g., Outer Membrane Proteins. They are the support of essential biological functions acting as receptors, transporters or channels. Moreover, 2/3 of the marketed drugs targets a transmembrane protein and 50% specifically a GPCR [START_REF] Klabunde | Drug design strategies for targeting G-protein-coupled receptors[END_REF], obtaining atomic structures becomes a major axis of research. Yet, these proteins are embedded in a lipid membrane that constitutes a very specific environment. Because the proteins are strongly destabilized when extracted from their natural medium, 3D transmembrane structures are difficult to obtain experimentally. On that account, the total number of transmembrane proteins in the Protein DataBank [START_REF] Berman | The Protein Data Bank[END_REF] is limited, representing ~1% of all the available structures [START_REF] Fleishman | Transmembrane protein structures without X-rays[END_REF]. Thus alternative approaches are required to obtain structural information. Consequently methods aiming at constructing 3D structural models are become an important research fields for understanding biological mechanisms and interactions [START_REF] Radestock | Homology model-based virtual screening for GPCR ligands using docking and target-biased scoring[END_REF].

Principle of the in silico approach

Figure 1 shows the principle of the in silico approach adopted to build structural DARC models and to dock its ligands. It can be roughly divided into three steps. Firstly, pertinent structural models were built using comparative modeling. Then, after refinement of different models, docking of the DARC natural ligand, i.e. CXC-L8, was performed by sophisticated docking techniques using the most relevant structural models. Finally, similar docking approaches can be applied to the malarial protein DBP. Building a structural model of a transmembrane protein related to GPCR can be decomposed in simple tasks (cf. Figure 2 and [START_REF] Paterlini | Structure modeling of the chemokine receptor CCR5: implications for ligand binding and selectivity[END_REF]). Once the helical transmembrane regions are located with dedicated prediction software, these predicted helices are aligned with the corresponding TM helices of available GPCR structures, e.g. rhodopsin (PDB code 1F88 [START_REF] Palczewski | Crystal structure of rhodopsin: A G protein-coupled receptor[END_REF]). However few transmembrane proteins can easily fit into this idealistic pipeline. For two main reasons: (i) the sequence similarity with rhodopsin (as well with beta2-adrenergic receptor as A2A adenosine receptor) is often low and (ii) prediction of transmembrane segments requires special attention and caution.

The structural models

From experimental evidence, DARC protein can be divided into three main domains:

(i) one central domain encompassing the transmembrane helices and connection loops, (ii) the long N terminus region, namely ECD1 and (iii) the short C terminus region, namely ICD4.

For none of these domains, neither PSI-BLAST [START_REF] Altschul | Gapped BLAST and PSI-BLAST: a new generation of protein database search programs[END_REF] nor FASTA [START_REF] Pearson | Improved tools for biological sequence comparison[END_REF], the most known sequence similarity search tools, retrieve protein structures related to DARC in the Protein Data Bank Moreover DARC family has only at most 25% of identity with CXCR-2 family.

Phylogenetic analyses show that DARC family is highly conserved but very different from other chemokine receptor sequences (see Figure 3, [START_REF] Tournamille | Sequence, evolution and ligand binding properties of mammalian Duffy antigen/receptor for chemokines[END_REF][START_REF] Thompson | improving the sensitivity of progressive multiple sequence alignment through sequence weighting, positionspecific gap penalties and weight matrix choice[END_REF][START_REF] Perriere | WWW-query: an on-line retrieval system for biological sequence banks[END_REF][START_REF] Iwamoto | Identification of a novel exon and spliced form of Duffy mRNA that is the predominant transcript in both erythroid and postcapillary venule endothelium[END_REF]). The prediction of transmembrane segments is also quite a complex problem. Numerous methods are at present available. They differ on the learning data used and on the kind of learning and prediction algorithms. For instance, HMMTOP [START_REF] Tusnady | Principles governing amino acid composition of integral membrane proteins: application to topology prediction[END_REF][START_REF] Tusnady | The HMMTOP transmembrane topology prediction server[END_REF] and TMHMM [START_REF] Krogh | Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes[END_REF] are based on hidden Markov models. Yet, they give different results, even if the expected prediction rate -in regards to the literature-is supposed to be quite impressive. We have tested a dozen of these methods: DAS [START_REF] Cserzo | Prediction of transmembrane alpha-helices in prokaryotic membrane proteins: the dense alignment surface method[END_REF], TopPred 2 [START_REF] Von Heijne | Membrane protein structure prediction. Hydrophobicity analysis and the positive-inside rule[END_REF][START_REF] Claros | TopPred II: an improved software for membrane protein structure predictions[END_REF], HMMTOP [START_REF] Tusnady | Principles governing amino acid composition of integral membrane proteins: application to topology prediction[END_REF][START_REF] Tusnady | The HMMTOP transmembrane topology prediction server[END_REF], TMHMM 1.0 and 2.0 [START_REF] Krogh | Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes[END_REF], PHDtm [START_REF] Rost | Transmembrane helices predicted at 95% accuracy[END_REF][START_REF] Rost | Topology prediction for helical transmembrane proteins at 86% accuracy[END_REF], TMpred [START_REF] Hofmann | TMbase -A database of membrane spanning proteins segments[END_REF], SOSUI [START_REF] Hirokawa | SOSUI: classification and secondary structure prediction system for membrane proteins[END_REF][START_REF] Mitaku | Physicochemical factors for discriminating between soluble and membrane proteins: hydrophobicity of helical segments and protein length[END_REF][START_REF] Mitaku | Amphiphilicity index of polar amino acids as an aid in the characterization of amino acid preference at membrane-water interfaces[END_REF], SPLIT [START_REF] Juretic | Conformational preference functions for predicting helices in membrane proteins[END_REF][START_REF] Juretic | Basic charge clusters and predictions of membrane protein topology[END_REF],

Pred-TMR 1.0 and 2.0 [START_REF] Pasquier | An hierarchical artificial neural network system for the classification of transmembrane proteins[END_REF][START_REF] Pasquier | A novel method for predicting transmembrane segments in proteins based on a statistical analysis of the SwissProt database: the PRED-TMR algorithm[END_REF], TMAP [START_REF] Persson | Prediction of transmembrane segments in proteins utilising multiple sequence alignments[END_REF][START_REF] Persson | Topology prediction of membrane proteins[END_REF], TSEG [START_REF] Kihara | Prediction of membrane proteins based on classification of transmembrane segments[END_REF], TM-FINDER [START_REF] Deber | TM Finder: a prediction program for transmembrane protein segments using a combination of hydrophobicity and nonpolar phase helicity scales[END_REF],

UMDHMM TMHP [START_REF] Zhou | Predicting the topology of transmembrane helical proteins using mean burial propensity and a hidden-Markov-model-based method[END_REF], MEMSAT [START_REF] Jones | A model recognition approach to the prediction of all-helical membrane protein structure and topology[END_REF][START_REF] Jones | Do transmembrane protein superfolds exist?[END_REF], PRODIV-TMHMM [START_REF] Viklund | Best alpha-helical transmembrane protein topology predictions are achieved using hidden Markov models and evolutionary information[END_REF] and MemBrain [START_REF] Shen | MemBrain: improving the accuracy of predicting transmembrane helices[END_REF]. Figure 4 summarizes the results obtained with most of them for DARC sequence [START_REF] Ihaka | R: a language for data analysis and graphics[END_REF].

Only the first helix is predicted with a large consensus while the fourth helix has a huge range of definitions, diverging sometimes by 15 residues. Are shown the predictions done using HMMTOP [START_REF] Tusnady | Principles governing amino acid composition of integral membrane proteins: application to topology prediction[END_REF][START_REF] Tusnady | The HMMTOP transmembrane topology prediction server[END_REF], TMHMM 2.0 [START_REF] Krogh | Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes[END_REF], TMAP [START_REF] Persson | Topology prediction of membrane proteins[END_REF], MEMSAT [START_REF] Jones | A model recognition approach to the prediction of all-helical membrane protein structure and topology[END_REF], SOSUI [START_REF] Mitaku | Amphiphilicity index of polar amino acids as an aid in the characterization of amino acid preference at membrane-water interfaces[END_REF], TMpred [START_REF] Hofmann | TMbase -A database of membrane spanning proteins segments[END_REF], TSEG [START_REF] Kihara | Prediction of membrane proteins based on classification of transmembrane segments[END_REF], TM-FINDER [START_REF] Deber | TM Finder: a prediction program for transmembrane protein segments using a combination of hydrophobicity and nonpolar phase helicity scales[END_REF], Pred-TMR 2.0 [START_REF] Pasquier | A novel method for predicting transmembrane segments in proteins based on a statistical analysis of the SwissProt database: the PRED-TMR algorithm[END_REF], SPLIT [START_REF] Juretic | Basic charge clusters and predictions of membrane protein topology[END_REF] and DAS [START_REF] Cserzo | Prediction of transmembrane alpha-helices in prokaryotic membrane proteins: the dense alignment surface method[END_REF]. The final selection corresponds to the positions finally obtained after multiple tests and evaluations. The plot was done using R software [START_REF] Ihaka | R: a language for data analysis and graphics[END_REF].

So, we designed a supervised approach to obtain pertinent structural models. This approach benefited from a crucial element, i.e. the availability of experimental data that guided the whole procedure. These data consisted in the measure of the CXCL8 binding rate with 40 mutants. These measures underline the potential accessibility of some residues.

Without these experimental data, assessment of the resulting structural models would be extremely difficult. Data were collected from the literature and our lab [START_REF] Tournamille | Sequence, evolution and ligand binding properties of mammalian Duffy antigen/receptor for chemokines[END_REF][START_REF] Tournamille | Structure-function analysis of the extracellular domains of the Duffy antigen/receptor for chemokines: characterization of antibody and chemokine binding sites[END_REF][START_REF] Wasniowska | Structural characterization of the epitope recognized by the new anti-Fy6 monoclonal antibody NaM 185-2C3[END_REF]. Figure 3 describes the methodology we have used to obtain pertinent structural models of DARC. A rough localization of the DARC transmembrane helical segments is done with different prediction algorithms presented in Figure 4 (cf. Figure 5a). The predicted -helices of DARC are aligned to the assigned helices of the rhodopsin protein (PDB code: 1F88, chain A [START_REF] Palczewski | Crystal structure of rhodopsin: A G protein-coupled receptor[END_REF]). Secondary structure assignment was done using DSSP software [START_REF] Kabsch | Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features[END_REF] (cf. Figure 5b).

Similarly, predicted interconnecting loops of DARC were assigned systematically to their counterparts of rhodopsin. Potential structural homologues of ECD1 and ICD4 were searched with sophisticated approaches (Figure 5c) and the resulting propositions were added as additional templates (Figure 5d, see below).

The alignments with the sequences of the structural templates were done with Clustalw software [START_REF] Thompson | improving the sensitivity of progressive multiple sequence alignment through sequence weighting, positionspecific gap penalties and weight matrix choice[END_REF] and manually modified. Hundred models were generated using Modeller software [START_REF] Sali | Definition of general topological equivalence in protein structures. A procedure involving comparison of properties and relationships through simulated annealing and dynamic programming[END_REF][START_REF] Sali | Comparative protein modelling by satisfaction of spatial restraints[END_REF][START_REF] Marti-Renom | Comparative protein structure modeling of genes and genomes[END_REF]. Each model was then refined, e.g., using SCWRL [START_REF] Canutescu | A graph-theory algorithm for rapid protein side-chain prediction[END_REF] and GROMACS [START_REF] Kutzner | Speeding up parallel GROMACS on high-latency networks[END_REF][START_REF] Van Der Spoel | GROMACS: fast, flexible, and free[END_REF] (cf. Figure 5e) and the residue accessibility was computed with Naccess software [START_REF] Hubbard | NACCESS[END_REF] (cf. Figure 5f). The alignment was modified in the light of the biological data known.

Hence, different alternative locations of helical regions were tested and the corresponding alignments were modified accordingly (cf. Figure 5g). More than ten iterations of steps 5e to 5g were done and finally, models corresponding at best to the biological data were selected (cf. Figure 5h).

As notified in Figure 5c, we made a specific research for the longest extra-membranous domains of the protein, namely ECD1 and ICD4. These two regions are exposed to solvent and consequently, they exhibit physico-chemical features similar to those of globular proteins.

Secondary structure prediction methods, e.g. [START_REF] Pollastri | Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles[END_REF] predicted them as coil region with a medium confidence index. Turns prediction underlined many turns in both fragments [START_REF] Fuchs | High accuracy prediction of beta-turns and their types using propensities and multiple alignments[END_REF].

HMMSTR, which is based on a more complex approach, predicted a short -strand of 4-5 residues in ECD1, and a helical structure in ICD4, but the confidence index was poor [START_REF] Bystroff | HMMSTR: a hidden Markov model for local sequence-structure correlations in proteins[END_REF].

Threading, ab initio and de novo methods were so required to find potential related protein fragments. The solutions were different but systematically, a high -strand propensity was indicated in the first part of ECD1 and a helical structure at its end, close to the connection with the TM1 segment. In this research, we also used our own prediction approach based on Protein Blocks.

Protein Blocks

Figure 6. Encoding of the protein structures (3D) in terms of Protein Blocks.

The classical description of protein structures involves two regular states, the -helices and the -strands and one non-regular and variable state, the coil. Nonetheless, this simple definition of secondary structures masks numerous limitations. Indeed, three states may oversimplify the description of protein structure; 50% of all residues, i.e., the coil, are not described even if it encompasses recurrent and similar local protein structures. Description of local protein structures have hence focused on the elaboration of complete sets of small prototypes or "structural alphabets" (SAs), that help to analyze local protein structures and to approximate every part of the protein backbone [START_REF] Offmann | Local Protein Structures[END_REF][START_REF] De Brevern | Protein structural alphabets: beyond the secondary structure description[END_REF][START_REF] Tyagi | Description of the local protein structure. II. Novel approaches[END_REF][START_REF] Benros | Description of the local protein structure. I. Classical approaches[END_REF]. The principle of a structural alphabet firstly consists in designing a set of average recurrent local protein structures, able to approximate efficiently, every part of known structures (see Figure 6). Each residue being associated to one of these prototypes, then so, the 3D information of the protein structures can be translated as a series of prototypes (letters) in 1D, as the amino acid sequence.

Our structural alphabet is composed of 16 structurally averaged protein fragments that are 5 residues in length, called Protein Blocks (PBs, [START_REF] De Brevern | Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks[END_REF]). They have been used both to describe the 3D protein backbones [START_REF] De Brevern | New assessment of a structural alphabet[END_REF] and to perform a local structure prediction [START_REF] De Brevern | Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks[END_REF][START_REF] De Brevern | Extension of a local backbone description using a structural alphabet: a new approach to the sequence-structure relationship[END_REF][START_REF] De Brevern | Local backbone structure prediction of proteins[END_REF][START_REF] Etchebest | A structural alphabet for local protein structures: improved prediction methods[END_REF][START_REF] De Brevern | Pinning strategy": a novel approach for predicting the backbone structure in terms of protein blocks from sequence[END_REF]. Our works on PBs have proven their efficiency in the description and the prediction of long fragments [START_REF] De Brevern | Extension of a local backbone description using a structural alphabet: a new approach to the sequence-structure relationship[END_REF][START_REF] De Brevern | Compacting local protein folds with a "hybrid protein model[END_REF][START_REF] De Brevern | Hybrid protein model' for optimally defining 3D protein structure fragments[END_REF][START_REF] Benros | Assessing a novel approach for predicting local 3D protein structures from sequence[END_REF][START_REF] Benros | Analyzing the sequence-structure relationship of a library of local structural prototypes[END_REF]] and short loops [START_REF] Fourrier | Use of a structural alphabet for analysis of short loops connecting repetitive structures[END_REF], to define a reduced amino acid alphabet dedicated for mutation design [START_REF] Etchebest | A reduced amino acid alphabet for understanding and designing protein adaptation to mutation[END_REF], to analyze protein contacts [START_REF] Faure | Protein contacts, inter-residue interactions and side-chain modelling[END_REF] or in the building of a transmembrane protein [START_REF] De Brevern | A structural model of a seven-transmembrane helix receptor: the Duffy antigen/receptor for chemokine (DARC)[END_REF]. We have also used protein blocks to compare / superimpose protein structures [START_REF] Tyagi | A substitution matrix for structural alphabet based on structural alignment of homologous proteins and its applications[END_REF][START_REF] Tyagi | Protein Block Expert (PBE): a web-based protein structure analysis server using a structural alphabet[END_REF][START_REF] Tyagi | Protein structure mining using a structural alphabet[END_REF] (see Figure 1b).

Developments made in other laboratories, using PBs, have focused on the reconstruction of globular protein structures [START_REF] Dong | Prediction of protein local structures and folding fragments based on building-block library[END_REF], the design of peptides [START_REF] Thomas | Prediction of peptide structure: how far are we?[END_REF], the definition of binding site signatures [START_REF] Dudev | Discovering structural motifs using a structural alphabet: application to magnesium-binding sites[END_REF], novel prediction methodologies [START_REF] Li | Fragment-based local statistical potentials derived by combining an alphabet of protein local structures with secondary structures and solvent accessibilities[END_REF][START_REF] Zimmermann | LOCUSTRA: accurate prediction of local protein structure using a two-layer support vector machine approach[END_REF] and fragmentbased local statistical potentials [START_REF] Li | Fragment-based local statistical potentials derived by combining an alphabet of protein local structures with secondary structures and solvent accessibilities[END_REF]. The features of this alphabet have been compared by Karchin et al. [START_REF] Karchin | Hidden Markov models that use predicted local structure for fold recognition: alphabets of backbone geometry[END_REF] with those of 8 other structural alphabets showing that our PB alphabet is highly informative, with the best predictive ability of those tested. It is nowadays the most widely used SA in the world.

ECD1 prediction done with Protein Blocks method confirmed that the sequence is weakly informative. Nonetheless, it helped to distinguish 5 zones corresponding to (i) a first short region of extended structures, (ii) a second region related to extended structures following a short kink (iii) a third poorly informative region with a turn, confirmed by [START_REF] Fuchs | High accuracy prediction of beta-turns and their types using propensities and multiple alignments[END_REF],

(iv) a fourth fuzzy non-helical region (v) a fifth region predicted as mainly helical, with high confidence index, followed by a short loop. Finally, two models were selected (see Figure 7). Interestingly, the final positions of transmembrane helices gave a better sequence identity with rhodopsin structure than initially computed using classical alignment tools, i.e. 25% of sequence identity for the helices. The two models were quite different; in the first one, ECD1 looked like a C letter and was far from the rest of the protein. This conformation was qualified as "open" (see Figures 7a and7b). In contrast, in the second model, ECD1 was more compact with a position closer to the rest of the protein. The conformation of the second model was denominated "closed" (see Figures 7c and7d). Both models encompassed most of the 27 amino acids supposed to be exposed. Exceptions were related to two amino acids found buried while not involved in disulfide bridges. The first one is a Phenylalanine (F65) located in the beginning of the first helix. The side chain was stacked with other aromatic amino acids. The second one is an Aspartate (D263) not close to the surface. These discrepancies may be related to the quality of the models but also to the crude interpretation we made of the experimental data.

Two selected models

To explore the flexibility of the ECD loops, we performed simulated annealing simulations [START_REF] Berendsen | GROMACS: A messagepassing parallel molecular dynamics implementation[END_REF]. Firstly, it explains well the importance of D263 which interacts directly with the end of the ECD3 . Secondly, when we analysed all the simulated structures with the use of Protein Blocks, we observed that some regions of ECD1 tent to be more helical and other ones to be more extended. Interestingly, these regions corresponded to those predicted to be more extended or more helical with the prediction tools. Experimental data show that ECD1 is involved in the binding with ligands. The quite different position of ECD1 between the two models led us to perform a preliminary study aiming at analyzing domain motions. This study could lead to alternative positions of ECD1 domain. For that purpose, normal mode analysis is well adapted. We use the WEBnm@ server developed by Reuter group [START_REF] Hollup | WEBnm@: a web application for normal mode analyses of proteins[END_REF], a service that performs an analysis of the first Normal Mode with an efficient approach [START_REF] Reuter | Transconformations of the SERCA1 Ca-ATPase: a normal mode study[END_REF][START_REF] Valadie | Dynamical properties of the MscL of Escherichia coli: a normal mode analysis[END_REF]. It uses MMTK library [START_REF] Hinsen | The molecular modeling toolkit: A new approach to molecular simulations[END_REF] and an appropriate force field [START_REF] Hinsen | Harmonicity in slow protein dynamics[END_REF]. The three first lower modes, i.e., modes 7 to 9, showed clearly that ECD1, is involved in large domain motions with the largest contributions to the amplitude of motions (see Figure 8) with the largest contributions. The directions of motions indicated that ECD1 tends to come closer to the other ECDs. Interestingly, this approach highlights the distinction in three regions already observed with a first structured zone, a transition region and a last structured zone. The median region seems to play a role of hinge between the two extremities of ECD1 (see Figure 8b). However, these results even if quite interesting has to be considered with caution. Indeed, normal mode analysis strongly depends on the 3D structure. So, further analyses have to be performed on alternative models to confirm the ECD1 motion tendencies. Electrostatic potentials of DARC model were calculated using the finite-difference

Poisson-Boltzmann (FDPB) method [START_REF] Rocchia | Extending the applicability of the nonlinear Poisson-Boltzmann equation: Multiple dielectric constants and multivalent ions[END_REF]. Figure 9 present the electrostatic potential mapped onto the molecular surface of the DARC model. Two distinct zones can be observed for the potential interaction zones of CXCL8. The first one is highly negative, the second one is highly positive. The residues in the core of the negative zone are the F22-E23 (corresponding to important epitope Fy6). Interestingly, electrostatics analysis performed for the CXCL8 dimer and monomer showed a complementary with this distribution: two major regions in terms of electrostatic potential can be delineated, one positive and the remainder negative, encompassing the loop 40s. Electrostatic potential computed for CCL5 (RANTES) showed less contrasted results; moreover the charge repartition was very different from CXCL8.

These analyses corroborate other studies that highlighted the importance of electrostatics in the binding of chemokines [START_REF] Crump | Solution structure and basis for functional activity of stromal cell-derived factor-1; dissociation of CXCR4 activation from binding and inhibition of HIV-1[END_REF][START_REF] Baysal | Elucidating the structural mechanisms for biological activity of the chemokine family[END_REF]. For instance, the region of interaction of CXCR-1 which has been partially co-crystallized is positively charged, as the ECD1 Nterminus, and interacts with the loop 40s, negatively charged [START_REF] Skelton | Structure of a CXC chemokine-receptor fragment in complex with interleukin-8[END_REF]. So, these results are coherent with all the available data.

Overall, these results show that the models we proposed provide some interesting clues for locating the binding site for natural ligands and the forces that could govern the interaction.

However, a precise location, i.e. the ensemble of residues that participate to the interaction, necessitates more sophisticated methods such as docking methods. Docking algorithms allow scoring of each position of the ligand rolling onto the entire surface of the receptor. It is necessary to remind that, in case of membrane proteins, the membrane hides numerous sites of the protein. So, a systematic exploration of the receptor surface will waste a lot of time in non relevant locations. Most docking tools first consider the partners as rigid bodies and eventually introduce flexibility at the end of the procedure and generally only for some selected conformations of the complex. Eventually, the ligand (the smallest partner) can be considered as flexible. Flexibility is often limited to side chains repositioning. In some cases, the polypeptide backbone is optimized by few steps of energy minimization. Thus, taking profit from the ICM software (reference) that allows introducing flexibility from the very first stages, we develop an original strategy to predict binding sites and complex conformations of DARC with natural ligands, i.e. CXCL8. As shown in the previous sections, DARC is a highly complex protein. To take into account the high flexibility of ECD1, we have designed a dedicated docking approach that combines rigid and flexible docking. First, a rigid-body docking was performed between CXCL8 and DARC without ECD1, i.e., Transmembrane Domain (TMD) encompassing the seven transmembrane helices, the extracellular connecting loops ECD2, ECD3, ECD4 and all the ICDs intracellular connecting loops (see Figure 10a), even if these last ones do not participate in the binding. The presence of all the ECDs is very important. Indeed, the shape of the receptor molecular surface is dependent on their presence.

The docking approach

In parallel, a flexible docking was performed considering ECD1 domain as flexible rolling onto the surface of CXCL8 structures, maintained rigid (see Figure 10b). As no clear experimental evidence asserts if the interaction concerns mono-or dimeric form of CXCL8, both hypotheses were tested for rigid and flexible docking. The best results of each approach were then combined and a flexible docking is done where only ECD1 is flexible (see Figure 10c). Then the best results are selected and analyzed (see Figure 10d). To screen the different potential interaction conformations between TMD and CXCL8, rigid docking simulations were performed. Two different tools, namely ClusPro [START_REF] Comeau | ClusPro: a fully automated algorithm for protein-protein docking[END_REF][START_REF] Comeau | ClusPro: an automated docking and discrimination method for the prediction of protein complexes[END_REF] and ICM [START_REF] Fernandez-Recio | ICM-DISCO docking by global energy optimization with fully flexible side-chains[END_REF]176] 12). Moreover, in this conformation CXCL8 is able to bind glycosaminoglycans (GAG). GAGs -chemokine interaction play a key role in the aggregation of chemokines, in the gradient-generating release of the chemokines in the circulation, and their presentation to the receptors [START_REF] Hoogewerf | Glycosaminoglycans mediate cell surface oligomerization of chemokines[END_REF][START_REF] Plater-Zyberk | Effect of a CC chemokine receptor antagonist on collagen induced arthritis in DBA/1 mice[END_REF][START_REF] Kuschert | Glycosaminoglycans interact selectively with chemokines and modulate receptor binding and cellular responses[END_REF]. The modulation of such interactions may represent a therapeutic approach in inflammatory disease [START_REF] Ali | Multimerization of monocyte chemoattractant protein-1 is not required for glycosaminoglycan-dependent transendothelial chemotaxis[END_REF]. Around 400 different conformations were analyzed during this step. Results for CXCL8 monomer and dimer were both compatible. In parallel to the rigid docking, a docking was done where ECD1 was allowed to adapt its conformation when interacting with CXCL8. This flexible docking was done using the biased Monte-Carlo (MC) search of ICM [START_REF] Fernandez-Recio | ICM-DISCO docking by global energy optimization with fully flexible side-chains[END_REF]176]based on a. ECD1 is firstly placed by a preliminary rigid body docking. Then, a random sampling is done in the -space, i.e., one couple -is chosen in the favored Ramachandran regions and affected to one residue. The corresponding energy is computed, and then the Metropolis criterion is applied to keep or discard the conformation so generated. One million of random has been performed and 250.000 MC steps. Privileged conformations were clustered, leading to 50 clusters (see Figure 13). Three clusters were in accordance with the biological data, i.e. residues of CXCL8 and of DARC supposed to be implicated in the interaction. The contacts observed between ECD1 and CXCL8, were analyzed using Protein Blocks approach [START_REF] De Brevern | Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks[END_REF][START_REF] De Brevern | New assessment of a structural alphabet[END_REF]. This simulation generates ~120.000 conformations which were grouped and filtered according to the biological data. For examples, 62 clusters of distinct conformations were selected for DARC Tyrosine 30 with CXCL8. Three conformations were associated to the lowest energy.

Rigid docking

Complete assembly

Figure 14 shows the most representative conformation (the most frequently observed during the simulation) and that presenting the best compromise between favorable energy versus number of occurrences (see Figure 14).

Overall, even if the results are preliminary, the strategy described here yields interesting information that complement the biological data available.

Future works

The selected models need to be deeply and carefully analyzed and especially at the level of the amino acids involved at the interface between DARC and CXCL8. Concerning the DBP, the structure is incomplete but available [START_REF] Singh | Structural basis for Duffy recognition by the malaria parasite Duffy-binding-like domain[END_REF]. This structure brings some hints [START_REF] Hans | Mapping binding residues in the Plasmodium vivax domain that binds Duffy antigen during red cell invasion[END_REF][START_REF] Mchenry | The crystal structure of P. knowlesi DBPalpha DBL domain and its implications for immune evasion[END_REF] to understand the invasion mechanism. A preliminary study performed for DARC-DBP complex, using a similar approach to that described above give remarkable results highly compatible with the main interaction hypothesis [START_REF] Mchenry | The crystal structure of P. knowlesi DBPalpha DBL domain and its implications for immune evasion[END_REF]. Models need to be refined using classical molecular modeling. Moreover, we have identified different pockets interesting for the interaction; further deeper analyses would help to understand their roles and their importance in the binding. Using these different results, it will be possible to propose designed mutants that can be tested first in silico. HMMTOP [START_REF] Tusnady | Principles governing amino acid composition of integral membrane proteins: application to topology prediction[END_REF][START_REF] Tusnady | The HMMTOP transmembrane topology prediction server[END_REF], TMHMM 2.0 [START_REF] Krogh | Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes[END_REF], TMAP [START_REF] Persson | Topology prediction of membrane proteins[END_REF], MEMSAT [START_REF] Jones | A model recognition approach to the prediction of all-helical membrane protein structure and topology[END_REF], SOSUI [START_REF] Mitaku | Amphiphilicity index of polar amino acids as an aid in the characterization of amino acid preference at membrane-water interfaces[END_REF], TMpred [START_REF] Hofmann | TMbase -A database of membrane spanning proteins segments[END_REF], TSEG [START_REF] Kihara | Prediction of membrane proteins based on classification of transmembrane segments[END_REF], TM-FINDER [START_REF] Deber | TM Finder: a prediction program for transmembrane protein segments using a combination of hydrophobicity and nonpolar phase helicity scales[END_REF], Pred-TMR 2.0 [START_REF] Pasquier | A novel method for predicting transmembrane segments in proteins based on a statistical analysis of the SwissProt database: the PRED-TMR algorithm[END_REF], SPLIT [START_REF] Juretic | Basic charge clusters and predictions of membrane protein topology[END_REF] and DAS [START_REF] Cserzo | Prediction of transmembrane alpha-helices in prokaryotic membrane proteins: the dense alignment surface method[END_REF]. The final selection corresponds to the positions finally obtained after multiple tests and evaluations. The plot was done using R software [START_REF] Ihaka | R: a language for data analysis and graphics[END_REF].

Figure 1 .

 1 Figure 1. The three steps. (1) by comparative modeling, structural models are building; (2) docking approach is performed with the natural ligand structure, i.e., CXC-L8 and (3) with the Duffy Binding Protein (DBP).

Figure 2 .

 2 Figure 2. Principles of homology modeling for a transmembrane protein.

Figure 3 .

 3 Figure 3. Phylogenetic analysis of DARC. Different sequences of DARC were aligned using CLUSTALW [91].The dendogram is plotted with Njplot software [92]. Bootstrap values computed by CLUSTALW are shown at the different nodes. Sequences were extracted from UniProt/UniParc ID [94]: the major 336 aa [93] human (DUFF_HUMAN, Q16570 [95]) was used, gorilla (Gorilla gorilla), AF311914; marmoset (Callithrix jacchus), AF311915; tamarin (Saguinus oedipus), AF311916; night monkey (Aotus trivirgatus), AF311917; squirrel monkey (Samiri boliviensis), AF311918; brown capuchin (Cebus apella), AF311919; chimpanzee (Pan troglodytes), AF311920; rhesus monkey (Macaca mulatta), AF311921; baboon (Papio papio), AF303532;gibbon (Hylobates lar) AF303533 (all the last sequences are from[START_REF] Tournamille | Sequence, evolution and ligand binding properties of mammalian Duffy antigen/receptor for chemokines[END_REF]).

Figure 4 .

 4 Figure 4. Prediction of transmembrane segments of DARC. Are shown the predictions done using HMMTOP[START_REF] Tusnady | Principles governing amino acid composition of integral membrane proteins: application to topology prediction[END_REF][START_REF] Tusnady | The HMMTOP transmembrane topology prediction server[END_REF], TMHMM 2.0[START_REF] Krogh | Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes[END_REF], TMAP[START_REF] Persson | Topology prediction of membrane proteins[END_REF], MEMSAT[START_REF] Jones | A model recognition approach to the prediction of all-helical membrane protein structure and topology[END_REF], SOSUI[START_REF] Mitaku | Amphiphilicity index of polar amino acids as an aid in the characterization of amino acid preference at membrane-water interfaces[END_REF], TMpred[START_REF] Hofmann | TMbase -A database of membrane spanning proteins segments[END_REF], TSEG[START_REF] Kihara | Prediction of membrane proteins based on classification of transmembrane segments[END_REF], TM-FINDER[START_REF] Deber | TM Finder: a prediction program for transmembrane protein segments using a combination of hydrophobicity and nonpolar phase helicity scales[END_REF], Pred-TMR 2.0[START_REF] Pasquier | A novel method for predicting transmembrane segments in proteins based on a statistical analysis of the SwissProt database: the PRED-TMR algorithm[END_REF], SPLIT[START_REF] Juretic | Basic charge clusters and predictions of membrane protein topology[END_REF] and DAS[START_REF] Cserzo | Prediction of transmembrane alpha-helices in prokaryotic membrane proteins: the dense alignment surface method[END_REF]. The final selection corresponds to the positions finally obtained after multiple tests and evaluations. The plot was done using R software[START_REF] Ihaka | R: a language for data analysis and graphics[END_REF].

Figure 5 .

 5 Figure 5. Building structural models of DARC. (a) Prediction of transmembrane helices. (b) Alignment of helical regions with corresponding regions of rhodopsin structure. (c) Potential structural templates for .ECD1 and ICD4 prediction of (d) Addition of these results to the complete alignment for comparative modeling. (e) Structural model generation and refinement of these models. (f) Accessibility computation of amino acids and known to be exposed. (g) In regards to the results, the alignment is modified. (h) At last, some models are selected.

Figure 7 .

 7 Figure 7. The two DARC selected models. Two views of (a-b) open form structural model and (c-d) closed structural model. Visualization done with PyMol software [160].

Figure 8 .

 8 Figure 8. Normal Mode Analysis of DARC 'open' model. (a) (y-axis) Modes 7 to 9 for (x-axis) the whole sequence. (b) Visualization of the modes 7 to 9 on the "open" model. The cross highlights the hinge region of ECD1. Visualization done with VMD software [162, 163].

Figure 9 .

 9 Figure 9. Visualization of electrostatics potential on the surface of DARC 'open' model. Visualization done with PyMol software [160].

Figure 10 .

 10 Figure 10. A divide-and-conquer approach. The ECD1 (N terminus of DARC) and the Transmembrane Domain (TMD) are considered separately. (a) A rigid docking is performed on the TMD while (b) a flexible docking is done for ECD1. Then (c) a flexible docking is done to combine both results. (d) the most interesting results are analyzed.

Figure 11 .

 11 Figure 11. Rigid docking examples. (a -c) Three different examples of rigid docking results between TMD and CXCL8 (monomer form). (d -f) Three different views of a complex obtained by rigid body between TMD and CXCL8 (dimer form).

Figure 12 .Figure 13 .

 1213 Figure 12. Selected monomer CXCL8 -DARC TMD docking. Some important residues are highlighted.

Figure 14 .

 14 Figure 14. A good conformation. ECD1, CXCL8 and TD are shown with different grey tones.

Figure 1 .

 1 Figure 1. The three steps. (1) by comparative modeling, structural models are building; (2) docking approach is performed with the natural ligand structure, i.e., CXC-L8 and (3) with the Duffy Binding Protein (DBP).

Figure 2 .

 2 Figure 2. Principles of homology modeling for a transmembrane protein.

Figure 3 .

 3 Figure 3. Phylogenetic analysis of DARC. Different sequences of DARC were aligned usingCLUSTALW[START_REF] Thompson | improving the sensitivity of progressive multiple sequence alignment through sequence weighting, positionspecific gap penalties and weight matrix choice[END_REF].The dendogram is plotted with Njplot software[START_REF] Perriere | WWW-query: an on-line retrieval system for biological sequence banks[END_REF]. Bootstrap values computed by CLUSTALW are shown at the different nodes. Sequences were extracted from UniProt/UniParc ID[START_REF] Apweiler | UniProt: the Universal Protein knowledgebase[END_REF]: the major 336 aa[START_REF] Iwamoto | Identification of a novel exon and spliced form of Duffy mRNA that is the predominant transcript in both erythroid and postcapillary venule endothelium[END_REF] human (DUFF_HUMAN, Q16570[95]) was used, gorilla (Gorilla gorilla), AF311914; marmoset (Callithrix jacchus), AF311915; tamarin (Saguinus oedipus), AF311916; night monkey (Aotus trivirgatus), AF311917; squirrel monkey (Samiri boliviensis), AF311918; brown capuchin (Cebus apella), AF311919; chimpanzee (Pan troglodytes), AF311920; rhesus monkey (Macaca mulatta), AF311921; baboon (Papio papio), AF303532; gibbon (Hylobates lar) AF303533 (all the last sequences are from[START_REF] Tournamille | Sequence, evolution and ligand binding properties of mammalian Duffy antigen/receptor for chemokines[END_REF]).

Figure 4 .

 4 Figure 4. Prediction of transmembrane segments of DARC. Are shown the predictions done using

Figure 5 .

 5 Figure 5. Building structural models of DARC. (a) Prediction of transmembrane helices. (b) Alignment of helical regions with corresponding regions of rhodopsin structure. (c) Potential structural templates for .ECD1 and ICD4 prediction of (d) Addition of these results to the complete alignment for comparative modeling. (e) Structural model generation and refinement of these models. (f) Accessibility computation of amino acids and known to be exposed. (g) In regards to the results, the alignment is modified. (h) At last, some models are selected.

Figure 6 .

 6 Figure 6. Encoding of the protein structures (3D) in terms of Protein Blocks.

Figure 7 .

 7 Figure 7. The two DARC selected models. Two views of (a-b) open form structural model and (c-d) closed structural model. Visualization done with PyMol software [160].

Figure 8 .

 8 Figure 8. Normal Mode Analysis of DARC 'open' model. (a) (y-axis) Modes 7 to 9 for (x-axis) the whole sequence. (b) Visualization of the modes 7 to 9 on the "open" model. The cross highlights the hinge region of ECD1. Visualization done with VMD software [162, 163].

Figure 9 .

 9 Figure 9. Visualization of electrostatics potential on the surface of DARC 'open' model.Visualization done with PyMol software[START_REF] Delano | The PyMOL Molecular Graphics System DeLano Scientific[END_REF].

Figure 10 .

 10 Figure 10. A divide-and-conquer approach. The ECD1 (N terminus of DARC) and the Transmembrane Domain (TMD) are considered separately. (a) A rigid docking is performed on the TMD while (b) a flexible docking is done for ECD1. Then (c) a flexible docking is done to combine both results. (d) the most interesting results are analyzed.

Figure 11 .

 11 Figure 11. Rigid docking examples. (a -c) Three different examples of rigid docking results between TMD and CXCL8 (monomer form). (d -f) Three different views of a complex obtained by rigid body between TMD and CXCL8 (dimer form).

Figure 12 .

 12 Figure 12. Selected monomer CXCL8 -DARC TMD docking. Some important residues are

Figure 13 .

 13 Figure 13. Flexible docking. Some snapshots of the flexible search of ECD1 around the rigid structure of CXCL8 monomer are shown.

Figure 14 .

 14 Figure 14. A good conformation. ECD1, CXCL8 and TD are shown with different grey tones.

  , were used. As numerous solutions are generated by each method, clusters of equivalent solutions have been done. Then different filters were applied to select the best solution. A first filter consists in selecting clusters where the ligand is located on the extracellular side, i.e. discarding conformations where ligand was in contact with ICDs or with the TMD. Indeed, it was not possible to introduce constraints to avoid a priori these positioning. Then, we applied experimental filters, i.e. the residues 51, 276, 129, 195 … are known to be potentially involved directly in the interaction between DARC and CXCL8 (see Figures11a to 11dfor some examples). One position of CXCL8 was in good agreement with the available biological data (see Figure

Acknowledgments

This work was supported by National Institute for Health and Medical Care (INSERM), University Paris 7 -Denis Diderot and National Institute for Blood Transfusion (INTS). LA benefits from a grant of the Région Ile-De-France. AdB and OB acknowledge Polonium grant 14287VM "Refinement of the studies on specificity of monoclonal antibody 2C3 directed against Duffy/DARC protein".