AIMS Biophysics, 4(4): 543-556. DOI: 10.3934/biophy.2017.4.543 Received: 04 August 2017 Accepted: 17 September 2017 Published: 26 September 2017 http://www.aimspress.com/journal/biophysics

Research article Common evolutionary binding mode of -like GPCRs: Insights from structural bioinformatics

Eda Suku 1 and Alejandro Giorgetti 2,*

1 Department of Biotechnology, University of Verona, Ca’ Vignal 1, Strada le Grazie 15, 37134 Verona, Italy 2 Computational Biomedicine, Institute for Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich, 52425 Jülich, Germany

* Correspondence: Email: [email protected].

2

Supplementary Information

Supplementary Figure 1. Cumulative mutual information (cMI) network of rhodopsin- like GPCRs class, calculated using the MISTIC server [1]. Data are mapped on the human β2 adrenoceptor, violet circles represent high values of cMI, light brown and yellow circles represent low and very low values of cMI, respectively. The ten extracted positions from the GPCRs binding cavities are highlighted with red circles.

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Supplementary Figure 2. Logo representation [2] of the rhodopsin-like GPCRs alignment (1618 sequences) downloaded from the GPCRdb file [3]. The most conserved motifs are represented with bigger letters.

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Supplementary Figure 3. Alignment of human rhodopsin-like GPCRs peptide receptors that have a negatively charged residue in position 3.32. The 3.32 position in shown with a red square and for the sake of clearness only the TM3 alignment is shown. The alignment was retrieved from the GPCRdb [3].

Supplementary Figure 4. Representative figure of the hierarchical clustering method. For each level the number of common positions and the corresponding number of clusters are shown. For each position, aromatic residues are shown in cyan, positively charged residues are shown in blue, negatively charged residues are shown in yellow, polar residues are shown in dark green, aromatic residues are shown in red and sulfur- containing residues are shown in light green.

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Supplementary Table 1. PDB code and receptor name of all the unique solved rhodopsin-like GPCRs complexes analyzed in this paper. The name of the ligands solved with the receptors their type are indicated in the third column as well.

PDB code Receptor type Ligand name Ligand type 1f88 Rhodopsin RET Inverse agonist 2rh1 2 adrenergic CAU Inverse agonist 2vt4 1 adrenergic P32 Antagonist 2y00 1 adrenergic Y00 Agonist (partial) 2y02 1 adrenergic WHJ Agonist 2y03 1 adrenergic 5FW Agonist 2y04 1 adrenergic 68H Agonist (partial) 2ycw 1 adrenergic CAU Inverse agonist 2ycz 1 adrenergic I32 Antagonist 2ydo Adenosine a2a ADN Agonist 2ydv Adenosine a2a NEC Agonist 3d4s 2 adrenergic TIM Inverse agonist 3eml Adenosine a2a ZMA Antagonist 3ny8 2 adrenergic JRZ Inverse agonist 3ny9 2 adrenergic JSZ Inverse agonist 3nya 2 adrenergic JTZ Antagonist 3odu CXCR4 ITD Antagonist 3p0g 2 adrenergic P0G Agonist 3pbl D3 ETQ Antagonist 3pds 2 adrenergic ERC Agonist 3pwh Adenosine a2a ZMA Inverse agonist 3qak Adenosine a2a UKA Agonist 3rey Adenosine a2a XAC Antagonist 3rfm Adenosine a2a CFF Antagonist 3rze Histidine H1 D7V Antagonist 3uon M2 muscarinic QNB Antagonist 3uza Adenosine a2a T4G Antagonist 3uzc Adenosine a2a T4E Antagonist 3v2w Sphingosine 1 phosphate ML5 Antagonist 4grv Neurotensin neurotensin Agonist 3zpq 1 adrenergic XF5 Antagonist 4ami 1 adrenergic G90 Agonist 4amj 1 adrenergic CVD Inverse agonist 4daj M3 muscarinic 0HK Inverse agonist 4djh κ-opioid JDC Antagonist 4dkl μ-opioid BF0 Antagonist 4ea3 N/OFO opioid 0NN Antagonist 4eiy Adenosine a2a ZMA Antagonist 4ej4 δ-opioid EJ4 Antagonist 4iaq 5ht1B 2GM Agonist 4iar 5ht1B ERM Agonist

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4ib4 5ht2B ERM Agonist 4j4q BOG Inverse agonist 4k5y Corticotropin (CRF1R) 1Q5 Antagonist 4ldl 2 adrenergic XQC Agonist 4ldo 2 adrenergic ALE Agonist 4mbs Ccr5 chemochine MRV Antagonist 4mqs M2 muscarnic IXO Agonist 4mqt M2 muscarnic 2CU Agonist 4ntj AZJ Antagonist 4phu GPR40 (FFA1) 2YB Agonist 4pxz 2py12 6AD Agonist 4py0 2py12 6AT Agonist (partial) 4qkx 2 adrenergic 35V Agonist 4s0v OX2 SUV Antagonist 4u14 M3 muscarinic 0HK Antagonist 4u16 M3 muscarinic 3C0 Antagonist 4ug2 Adenosine a2a NGI Agonist 4yay Angiotensin 1 ZD7 Antagonist 4z9g CFR1R 1Q5 Antagonist 4z34 Phospholipid acid (LPAR1) ON7 Antagonist 4z35 Phospholipid acid ON9 Antagonist 4z36 Phospholipid acid ON3 Antagonist 4zj8 OX1 receptor SUV Inverse agonist 4zjc OX1 receptor 4OT Inverse agonist 4zud Angiotensin1 OLM Inverse agonist 5a8e 1 adrenergic XTK Inverse agonist 5c1m μ-opioid 4VO Agonist 5cxv M1 muscarinic 0HK Antagonist 5d5a 2 adrenergic CAU Inverse agonist 5dhg Nociceptin DGV Inverse agonist 5dhh Nociceptin DGW Inverse agonist 5dsg M4 muscarinic 0HK Antagonist 5dsg M4 muscarinic P6G Antagonist 5f8u 1 adrenergic P32 Antagonist 5g53 Adenosine a2a NEC Agonist 5iu7 Adenosine a2a 6DY Agonist 5iua Adenosine a2a 6DX Agonist 5iub Adenosine a2a 6DV Agonist 5tgz CB1 ZDG Agonist 4xt1 US28 Peptide Antagonist 5uen Adenosine A1 DU1 Antagonist 5t1a CCR2 73R Antagonist 3vw7 PAR1 VPX Antagonist 4xnw P2Y1 21D Antagonist 5glh Endothelin ETB receptor Peptide Agonist 5vbl Apelin Peptide Agonist

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Supplementary Table 2. GPCRs families and their endogenous ligands analyzed in this manuscript. Ligands were downloaded from the ZINC database [4], the charge of each ligand was calculated using MarvinSketch tool (www.chemaxon.com).

GPCRs receptor family Endogenous ligand Calculated charge 5-Hydroxytryptamine Serotonin +1 Acetylcholine (muscarinic) Acetylcholine +1 Adrenoceptors Adrenaline +1 Adrenoceptors Noradrenaline +1 Dopamine receptors Dopamine +1 Histamine receptors Histamine +1 Trace amine receptors Tyramine +1 Trace amine receptors β-phenylethylamine +1 Trace amine receptors Octopamine +1 Trace amine receptors Dopamine +1 FFAR1,4 Palmitic –1 FFAR1,4 Myristic –1 FFAR1,4 Oleic –1 FFAR1,4 Lynoleic –1 FFAR2,3 Acetic acid 0 FFAR2,3 Butyric acid 0 BLT1 Leukotriene B4 –1 BLT2 12-Hydroxyeicosatetraenoic –1 acid CLTR1 Leukotriene C4 –1 CLTR2 Leukotriene C4 –1 OXER1 5-oxo-ETE –1 Lysophospholipid receptors –1 Sphingosine 1-phosphate Sphingosine 1-phosphate 0 receptors Cannabinoid receptors Anandamide 0 Platelet activating factor Platelet activating factor 0 receptors Prostanoid receptors Prostaglandine 0 Melatonin receptors Melatonin 0 Adenosine Adenosine receptors 0 P2Y receptors ATP, ADP –2 Bile acid Lithocholic acid 0 Estrogen receptors Estrogen 0 Hydroxycarboxylic acid L-lactic acid –1 Oxoglutarate α-ketoglutaric acid 0 Succinate Succinic acid –1 Opsin Retinol 0

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Supplementary Table 3. Human GPCRs peptide receptors that have a negatively charged residue in position 3.32. The non-endogenous ligands that interact with these receptors were retrieved from the GLASS database [5] and their charge were calculated with MarvinSketch tool (www.chemaxon.com).

Receptor Ligands Charge MCH1 3894 +1 MCH2 297 +1 NPBW1 488 +1 NPBW2 No available ligands ------OPRD 5772 +1 OPRK 5493 +1 OPRM 5870 +1 OPRX No ligands available ------SSR1 903 +1 SSR2 1059 +1 SSR3 1138 +1 SSR4 971 +1 SSR5 1206 +1 UR 521 +1

Agonist-antagonist Differential Bioinformatic Analysis

We performed a detailed and distinct bioinformatic analysis for agonist, antagonists and inverse agonists that interact with the solved receptors studied in the manuscript with the aim to investigate residues of the binding cavities that can contribute in activation or inactivation of receptors. For agonist binding it turned out to be, together with the ten previously identified residues, one more residue specific for this type of interaction i.e. 5.38 (GPCRdb numbering); for antagonist binding we found two more specific residues that can contribute in the recognition of only antagonists i.e 5.39 and 5.42 (GPCRdb numbering) and for inverse agonists we found only one more residues with respect to the ten calculated residues i.e. 5.42 (the same as for antagonists’ interactions). We searched for experimental data involving residues in these positions. For all the three positions, there are mutations that change the state of the receptors from active to inactive or viceversa [6,7,8]. However no particular value of conservation was observed for these positions. Indeed we believe that they can play a major role in the receptor selectivity.

References

1. Simonetti FL, Teppa E, Chernomoretz A, et al. (2013) MISTIC: Mutual information server to infer coevolution. Nucleic Acids Res 41: W8–W14. 2. Crooks GE, Hon G, Chandonia JM, et al. (2004) WebLogo: a sequence logo generator. Genome Res 14: 1188–1190. 3. Isberg V, Mordalski S, Munk C, et al. (2016) GPCRdb: an information system for G - coupled receptors. Nucleic Acids Res 44: D356–D364.

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4. Irwin JJ, Shoichet BK (2005) ZINC—a free database of commercially available compounds for virtual screening. J Chem Inf Model 45: 177–182. 5. Chan WK, Zhang H, Yang J, et al. (2015) GLASS: a comprehensive database for experimentally validated GPCR-ligand associations. Bioinformatics 31: 3035–3042. 6. Van RAM, Jacobson KA (1996) Molecular architecture of G protein-coupled receptors. Drug Dev Res 37: 1–38. 7. Marco E, Foucaud M, Langer I, et al. (2007) Mechanism of activation of a G protein-coupled receptor, the human cholecystokinin-2 receptor. J Biol Chem 282: 28779–28790. 8. Lee SM, Booe JM, Pioszak AA (2015) Structural insights into ligand recognition and selectivity for classes A, B, and C GPCRs. Eur J Pharmacol 763: 196–205.

© 2017 Alejandro Giorgetti, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)

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