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Dharma Teja Varapula

CHEM 680: Computer­Aided Drug Design

Instructor: Dr. Dora Schnur

Final Project Report

Homology Modeling of CCR3 Template used: CCR5

Dharma Teja Varapula | CHEM 680 Computer­Aided Drug Design | Project Report | Dr. Dora M Schnur

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Table of Contents

Introduction 3

Methods 4

Results and Discussion 7

Conclusion and Future Work 13

References 14

Dharma Teja Varapula | CHEM 680 Computer­Aided Drug Design | Project Report | Dr. Dora M Schnur

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Introduction Chemokines play a key role in recruitment of immune cells. Failure of healthy chemokine signaling has been reported to cause tumor regression, impairment in pathogen clearance, tumor development, tissue damage, and autoimmunity [1]. Due to their role in cellular recruitment, chemokine systems (chemokine receptors) can be targeted for inflammatory diseases.

Chemokine receptors are members of the GPCR family and there are 19 distinct receptors. Among them the CC chemokine receptors are 10 in number and are potential targets to several diseases, such as Asthma, Acute Macular Degeneration, Atherosclerosis, Atopic Dermatitis, Multiple Sclerosis, Pancreatitis, etc. [1]

Chemokines are classified according to the relative position of the first cysteine group found in the primary a.a. sequence. In CC chemokines, the two cysteine a.a. are located right beside each other. In CXC and CX3C chemokines, the two cysteines are separated by one other a.a. and 3 other a.a. respectively. In C chemokines, there is only one cysteine. [2]

CCR3 receptors are potential targets since blocking these receptors can prevent the release of various other cellular recruiters that eventually contribute to the inflammatory response. [3]

Small­molecule recognizing GPCRs have been studied well and several drugs are in the market already. However, large­molecule recognizing receptors have not been studied extensively due to the complexity of a peptide­protein interaction. Finding drugs for these protein­protein interface (PPI) targets has been very difficult due to increasing complexity with the molecular weight of the binding peptide ligand. [4]

Although chemokines have unique primary sequences they are promiscuous within the chemokine family and bind to several chemokine receptors. This non­specificity makes it difficult to develop chemokine receptor­specific leads. [5] Peptide­protein inhibitor discovery is also very challenging compared to small­molecule protein systems. Also, it has been noted that lack of receptor structures in the CCR subfamily also limited the development of new leads. [4]

The above limitation can be overcome using receptor homology modeling. A creative approach to the present problem would be to consider small­molecule antagonists to develop drugs. These small­molecule antagonists bind to both the orthosteric and allosteric sites in the chemokine receptors. It has been argued by Carter et al, 2009, that allosteric antagonism is function­selective and have the additional benefit of modulation over orthosteric antagonists. [5]

Dharma Teja Varapula | CHEM 680 Computer­Aided Drug Design | Project Report | Dr. Dora M Schnur

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Methods To create a homology model of CCR3 receptor, the most optimum template structure was chosen. After the homology model was developed, docking was performed with a ligand set from Jain et al, 2011 [7] to check for the docking scores and suggest which ligands are best suited for developing a good lead for CCR3.

Choice of Template

The only crystal structures of chemokine receptors available currently are of CXCR4, CXCR1, and CCR5, CCR5 being the most recently discovered in 2013. [6] Earlier, CCR3 homology modeling was done using bovine rhodopsin [7] which has a very low identity score of 25% (BLAST) compared to that with the CC chemokine receptors, CXCR4 (33%), CXCR1 (34%), and CCR5 (46%). Membrane­proteins are relatively hard to do a crystallographic study with, which is the reason why chemokine receptor structure discovery lags behind other non­membrane proteins. [8] CCR5 receptor was chosen based on the following criteria [8]:

(1) Large sequence of target requires lower identity match with the template.

(2) Larger regions of similarity is preferred over smaller fragmented ones ­ while both may have close identity scores.

(3) Sequence sizes should match.

Homology modeling was done using the module, Prime, part of the Schrodinger Suite (2013) that was used throughout this study. The primary sequence of CCR3 chosen was “P51677 CCR3_HUMAN” and was downloaded as a .fasta file obtained from www.uniprot.org. The CCR3 sequence was uploaded from the Prime panel and was identified with the family: “7tm_1 transmembrane receptor (rhodopsin family)”. The search for template sequences was done using BLAST.

The following illustrations, Fig 1, 2, and 3, show the matching sequences between CCR3, the target sequence, and CCR5, CXCR1, and CXCR4 respectively. It can be observed that CCR5 template gives the maximum sequence matching and longer matched sequences compared to CXCR1 and CXCR4. The sequence sizes also match unlike in the case of CXCR4 where the template was longer than the target. Due to such high identity, multiple templates (piecewise modeling) were not required for homology modeling. It can be noted that the signature groups for CC/GPCR family of receptors, the DRY and NPxxY were observed during sequence alignment.

Dharma Teja Varapula | CHEM 680 Computer­Aided Drug Design | Project Report | Dr. Dora M Schnur

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Fig 1: Matching sequences between CCR3 (target ­ top line) and CCR5 (template ­ bottom line)

Fig 2: Matching sequences between CCR3 (target ­ top line) and CXCR1 (template ­ bottom line)

Dharma Teja Varapula | CHEM 680 Computer­Aided Drug Design | Project Report | Dr. Dora M Schnur

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Fig 3: Matching sequences between CCR3 (target ­ top line) and CXCR4 (template ­ bottom line)

Sequence Alignment

After the secondary sequence is aligned using the in­built alignment tool in PRIME, Prime STA (with the GPCR­specific alignment), the sequences look as represented in Fig 4. Also, the DRY and NPxxY sequences can be observed from Fig 4.

Fig 4: CCR3 (top line) and CCR5 (bottom line) sequences after SSA (Secondary Sequence Alignment)

Dharma Teja Varapula | CHEM 680 Computer­Aided Drug Design | Project Report | Dr. Dora M Schnur

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The homology model was built with Maraviroc from the crystal structure of CCR5 as the ligand. The sequence matching score was 936.0, with a revised identity of 43%, and 13% gaps. Pfam was 13/13, meaning all the signature groups of the GPCR family, that CCR3 belongs to, are present. Pre­alignment, the score was 330.1, with 46% identity and 14% gaps.

Loop Refinement

Loop refinement was performed for two non­template loops, 96­100 and 228­232. It took roughly 8 mins with an Intel Core i5 second generation processor with 8 GB memory. After this, a dedicated energy minimization application was run in Maestro, with increased target minimization range (from 7.5 to 8.5 Angstrom).

After this, the CCR3 protein structure was exported and saved into a .pdb file, which is available for use here:

(https://drive.google.com/file/d/0B6J1ZFxFHrQLVTJaN0daWE1XNVU/edit?usp=sharing)

A Ramachandran Plot was generated for the CCR3 homology model using an online tool at [9]:

(http://mordred.bioc.cam.ac.uk/~rapper/rampage.php)

Ligand Set for Docking

A list of 22 compounds were used for investigating their docking scores with the CCR3 receptor. The compounds were borrowed from the study by Jain et al, 2011. [7] These compounds were built using ChemSketch freeware and saved as .mol files, which were later uploaded into a Maestro project. The complete list of compounds are shown in Fig 5. These 22 compounds and Maraviroc have been docked using GLIDE sp protocol with default parameters. Before docking them, the ligands were prepared using the LigPrep module, with pH,7.4 +/­ 0.0, and “determine chiralities from 3D structure” checked.

Results and Discussion The resulting homology model of CCR3 (ribbon view) with maraviroc at the binding site is shown in Fig 6. The binding site of maraviroc was speculated to be an allosteric site [5] and if that is so, it is highly likely that it is on the cytoplasmic side or in the interior engulfed by the transmembrane helices.

The CCR3 homology model was validated using a Ramachandran Plot which is shown in Fig 7. Three residues, A25 GLU, A178 PHE, A265 GLN were observed to be outliers and it is to be examined if they’re at the active site or not. The total number of residues in the favoured region was 281 (96.2 %), and the total number of residues in the allowed region was 8 (2.7 %), and the three outliers contributed to 1% of the total number. This is indication that the model is well­built

Dharma Teja Varapula | CHEM 680 Computer­Aided Drug Design | Project Report | Dr. Dora M Schnur

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however, it is important to examine if the three outliers affect the binding of the ligand in anyway.

For this, the outliers should not be in the way of the ligand access to the binding site, and the outliers should not be part of the binding site. Both these were verified and are represented in Fig 8 and Fig 9. Fig 8 shows the active­site boundary residues and the outlier residues as can be seen are not present in the binding­site. Hence, it can be concluded that the outlier residues do not affect the binding of ligand at the binding site. Fig 9 shows the ribbon model of CCR3 with the outlier residues highlighted. From this illustration it is clear that these outlier residues are sufficiently away from the binding site.

Fig 5: List of 22 compounds used to dock into the homology model of CCR3 (Jain et al, 2011) [7]

Dharma Teja Varapula | CHEM 680 Computer­Aided Drug Design | Project Report | Dr. Dora M Schnur

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Fig 6: Ribbon Structure of CCR3 homology model with maraviroc at its binding site

To investigate if the outlier residues are in way of access of ligand to the binding site, another view of the model shown in Fig 10. This view is from under the pocket created by the alpha helices of the GPCR protein.

Dharma Teja Varapula | CHEM 680 Computer­Aided Drug Design | Project Report | Dr. Dora M Schnur

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Fig 7: Ramachandran Plot for the homology model of CCR3 [9]

Fig 8: Active­site ligand interaction diagram for maraviroc and homology model of CCR3

Dharma Teja Varapula | CHEM 680 Computer­Aided Drug Design | Project Report | Dr. Dora M Schnur

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Fig 9: The outlier residues (highlighted in yellow/with full structures) are way from the binding site.

Fig 10: The outlier residues (highlighted in yellow/with full structures) are not in way of access of the ligand

Docking Study

The 22 compounds along with maraviroc have been docked and their GLIDE docking scores are shown in Table 1. Maraviroc, compound 22, and compound 14, have shown maximum docking scores of ­8.96, ­8.13, ­8.02.

A conformation of compound 10 has shown the least docking score at ­5.17. The order of energies calculated using GLIDE, are also in agreement for most part with the docking scores.

Dharma Teja Varapula | CHEM 680 Computer­Aided Drug Design | Project Report | Dr. Dora M Schnur

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Table 1: List of Ligands, their docking scores and energies

Ligand Name GLIDE score GLIDE Energy Ligand Name GLIDE score GLIDE Energy

Maraviroc ­8.958 ­67.17 comp11.1 ­6.536 ­50.938

comp22.1 ­8.131 ­63.732 comp3.1 ­6.408 ­54.925

comp14.1 ­8.021 ­65.358 comp8.1 ­6.35 ­52.907

comp5.1 ­7.959 ­59.124 comp14.1 ­6.288 ­55.873

comp15.1 ­7.856 ­59.357 comp20.1 ­6.214 ­46.219

comp22.1 ­7.855 ­62.247 comp10.1 ­6.209 ­41.552

comp18.1 ­7.511 ­59.772 comp6.1 ­6.083 ­51.548

comp12.1 ­7.237 ­59.045 comp21.1 ­6.079 ­46.661

comp14.1 ­7.184 ­60.623 comp17.1 ­6.004 ­47.124

comp18.1 ­7.044 ­56.849 comp16.1 ­5.993 ­48.286

comp14.1 ­6.981 ­55.081 comp1.1 ­5.963 ­59.709

comp7.1 ­6.772 ­52.599 comp10.1 ­5.957 ­41.44

comp4.1 ­6.745 ­61.125 comp9.1 ­5.92 ­55.241

comp1.1 ­6.709 ­63.445 comp17.1 ­5.593 ­47.718

comp2.1 ­6.638 ­53.103 comp13.1 ­5.319 ­46.757

comp19.1 ­6.56 ­54.303 comp10.1 ­5.3 ­43.059

comp8.1 ­6.542 ­59.253 comp10.1 ­5.169 ­42.597

Dharma Teja Varapula | CHEM 680 Computer­Aided Drug Design | Project Report | Dr. Dora M Schnur

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Conclusion and Future Work The homology model of CCR3 will enable the development of antagonists that might find use in the therapy for inflammatory diseases. It is important to have selective drugs targeting CCR3, otherwise, there will be undesired side­effects. This approach of using small­molecule antagonists for modulating or inhibiting protein­protein or peptide­protein systems, is interesting and would be pursued further, possibly for other systems.

Maraviroc is a known CCR5 antagonist, and to avoid non­specific binding a more CCR3­selective ligand should be developed and for which the above compounds can be used. A further study of a Pharmacophore model and QSAR based on the structures of maraviroc, compound 22, and compound 14 would follow suite for lead optimization.

Dharma Teja Varapula | CHEM 680 Computer­Aided Drug Design | Project Report | Dr. Dora M Schnur

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Homology Modeling of CCR3 | References

[1] Alexandre Garin, Amanda E.I. Proudfoot, Chemokines as targets for therapy, Experimental Cell Research, Volume 317, Issue 5, 10 March 2011, pages 602­612.

[2] Angelo Vedani, Max Dobler, Horst Dollinger, Kai­Malte Hasselbach, Franz Birke, and Markus A. Lill. Novel Ligands for the Chemokine Receptor­3 (CCR3):  A Receptor­Modeling Study Based on 5D­QSAR. Journal of Medicinal Chemistry, 2005, vol. 48 (5), pg. 1515­1527.

[3] Aiko Nitta, Yosuke Iura, Hideki Inoue, Ippei Sato, Koichiro Morihira, Hirokazu Kubota, Tatsuaki Morokata, Makoto Takeuchi, Mitsuaki Ohta, Shin­ichi Tsukamoto, Takayuki Imaoka, Toshiya Takahashi. Pyrrolidinyl phenylurea derivatives as novel CCR3 antagonists. Bioorganic & Medicinal Chemistry Letters, Volume 22, Issue 22, 2012, Pages 6876­6881.

[4] Michael M. Mysinger, Dahlia R. Weiss, Joshua J. Ziarek, Stéphanie Gravel, Allison K. Doak, Joel Karpiak, Nikolaus Heveker, Brian K. Shoichet, and Brian F. Volkman. Structure­based ligand discovery for the protein–protein interface of chemokine receptor CXCR4. PNAS, 2012, vol. 109 (14), pages 5517­5522.

[5] Percy H. Carter, Andrew J. Tebben, Chapter 12 The Use of Receptor Homology Modeling to Facilitate the Design of Selective Chemokine Receptor Antagonists, In: Tracy M. Handel and Damon J. Hamel, Editor(s), Methods in Enzymology, Academic Press, 2009, Volume 461, Pages 249­279.

[6] Qiuxiang Tan, Ya Zhu, Jian Li, Zhuxi Chen, Gye Won Han, Irina Kufareva, Tingting Li, Limin Ma, Gustavo Fenalti, Jing Li, Wenru Zhang, Xin Xie, Huaiyu Yang, Hualiang Jiang, Vadim Cherezov, Hong Liu, Raymond C. Stevens, Qiang Zhao, and Beili Wu. Structure of the CCR5 Chemokine Receptor–HIV Entry Inhibitor Maraviroc Complex. Science, 2013, vol. 341 (6152), pg. 1387­1390.

[7] Jain V, Saravanan P, Arvind A, Mohan CG., First pharmacophore model of CCR3 receptor antagonists and its homology model­assisted, stepwise virtual screening, Chem Biol Drug Des. 2011 May;77(5):373­87.

[8] Young, D. C., Chapter 9 Homology Model Building, In: David C. Young, Computational Drug Design, John Wiley and Sons, 2009, pages 105­118.

[9] S.C. Lovell, I.W. Davis, W.B. Arendall III, P.I.W. de Bakker, J.M. Word, M.G. Prisant, J.S. Richardson and D.C. Richardson (2002) Structure validation by Calpha geometry: phi,psi and Cbeta deviation. Proteins: Structure, Function & Genetics. 50: 437­450.

Dharma Teja Varapula | CHEM 680 Computer­Aided Drug Design | Project Report | Dr. Dora M Schnur