Opioid Receptors Interaction Profile


This paper describes an exciting big data analysis compiled in a freely available database, which can be applied to characterize the coupling of different G-Protein coupled receptors (GPCRs) families with their intracellular partners. Opioid receptor (OR) family was used as case study in order to gain further insights into the physiological properties of these important drug targets, known to be associated with the opioid crisis, a huge socio-economic issue directly related to drug abuse. An extensive characterization of all members of the ORs family (μ (MOR), δ (DOR), κ (KOR), nociceptin (NOP)) and their corresponding binding partners (ARRs: Arr2, Arr3; G-protein: Gi1, Gi2, Gi3, Go, Gob, Gz, Gq, G11, G14, G15, G12, Gssh, Gslo) was carried out. A multi-step approach including models’ construction (multiple sequence alignment, homology modeling), complex assembling (protein complex refinement with HADDOCK and complex equilibration), and protein-protein interface (PPI) characterization (including both structural and dynamics analysis) were performed. Our database can be easily applied to several GPCR sub-families, to determine the key structural and dynamical determinants involved in GPCR coupling selectivity.

Cite us:

Carlos A.V. Barreto*, Salete J. Baptista*, A. J. Preto, Daniel Silvério, Rita Melo, Irina S. Moreira. Decoding partner specificity of opioid receptor family, Frontiers in Molecular Biosciences, 2021.

*joint first authors

A: TM3-TM6 distances in the G-protein complexes range between 14 and 16 Å and TM3-TM7 distances range between 12 and 14 Å. Complexes are grouped by receptors across the TM3-TM7 axis with DOR and NOP less open (12-13 Å) followed by MOR and finally KOR with the biggest distances shown between TM3 and TM7. On the TM3-TM6 axis, all complexes are well clustered with MOR complexes showing the lowest distance, except the MOR-Gs complexes that show the highest TM3-TM6 distance.

B: Arrestin complexes have higher variations of the distance between complexes when compared to G-protein complexes. TM3-TM6 distance ranges between 15 and 18 Å and TM3-TM7 distances range between 11 and 16 Å. Complexes modelled with 6PWC show consistently lower TM3-TM6 distances as well as TM3-TM7 distances.

All complexes were analysed using normal mode analysis (NMA) and fluctuation values were calculated for all relevant structural motifs. The average fluctuation fold changes were calculated as the fold change between the receptor in complex and as a monomeric template. Considering that fluctuation can be seen as a proxy for protein motility, an increase in the average fold change indicates that, upon binding to its partner, the DxR undergoes conformational changes that give it more motility for the relevant structural motif. These values allow us to see distinct changes between relevant complex groups, most relevantly between DxR-Arrestin complexes, for which the average fluctuation value increases the least for all TM segments and even decreases in H8. Also relevant is how DxR-Gs complexes show a lower fold change in fluctuation when compared to other DxR-Gprot complexes (in a few cases it is possible to see a decrease in fluctuation upon binding). What is also fairly easy to observe is that D1R-like receptors will show fairly lower increases in fluctuation when compared with D2R-like receptors, and within the latter group D3R is the receptor showing the highest fold change in general.

The flexibility changes of relevant substructures were calculated as the Bhattacharya coefficients (BC) between monomeric and complex structures. This acts as a coefficient of similarity where high similarity is indicated by values close to 1 (Preto et al., 2020). OR-Arrs and OR-Gs have slightly higher BC values than the rest of the complexes, particularly at TM4, TM5 and TM6. Gi/o, q/11 and G12 complexes, showed lower values of BC, especially at TM1, TM5 and TM6. KOR-Gi/o and KOR-G12 are the exception, with high BC values at TM1. H8 structure has the highest BC values for all complexes. The multidimensional scaling map showed a very clear distinction between OR-Arrs/OR-Gs and OR-Gi/o/OR-Gq/11. KOR-Gi/o and KOR-G12 complexes are isolated from the rest of these partners’ clusters. DOR-Arrs 6U1N were also completely distant from their respective group.

Normal Mode Analysis, using only the Cα atoms, was performed in monomeric and complex structures of all OR. Changes in flexibility and fold changes in fluctuation were computed for all relevant substructures as described in Preto et al. (Preto et al., 2020). OR-Arrs and OR-Gs showed distinctively low average fluctuations values when compared with Gi/o, Gq and G12 complexes. The latter showed high average fluctuation values, in particular for TM1 through TM4 and TM7.

Models Construction

Protein sequence retrieval: UNIPROT [1]

Multiple Sequence Alignments: Clustal Omega [2]

Homology Modelling: Modeller [3,4] and SWISS-Model [5]

Complex assembling

Protein complex refinement: HADDOCK [6]

Complex equilibration: GROMACS [2]

The final models can be downloaded below.

Download All Complexes

Protein-protein interaction Analysis

Structure analysis: Python [7], COCOMAPS [8], InterProsurf [9]

Normal Mode Analysis: R [10]

Server Architecture

Webserver Setup: R [10] and Shiny [11]


[1] The UniProt Consortium, Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Agivetova, R., et al. (2021). UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Research 49, D480–D489. doi:10.1093/nar/gkaa1100

[2] Preto, A. J., Barreto, C. A. V., Baptista, S. J., Almeida, J. G. d., Lemos, A., Melo, A., et al. (2020). Understanding the Binding Specificity of G-Protein Coupled Receptors toward G-Proteins and Arrestins: Application to the Dopamine Receptor Family. Journal of Chemical Information and Modeling 60, 3969–3984. doi:10.1021/acs.jcim.0c00371

[3] Sali, A. and Blundell, T.L.(1993).Comparative Protein Modelling by Satisfaction of Spatial Restraints. Journal of Molecular Biology 234, 779–815. doi:10.1006/jmbi.1993.1626

[4] Webb, B. and Sali, A. (2016). Comparative Protein Structure Modeling Using MODELLER. Current Protocols in Bioinformatics 54, 5.6.1–5.6.37. doi:10.1002/cpbi.3

[5] Waterhouse, A., Bertoni, M., Bienert, S., Studer, G., Tauriello, G., Gumienny, R., et al. (2018). SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Research 46, W296–W303. doi:10.1093/nar/gky427

[6] van Zundert, G. C. P., Rodrigues, J. P. G. L. M., Trellet, M., Schmitz, C., Kastritis, P. L., Karaca, E., et al. (2016). The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes. Journal of Molecular Biology 428, 720–725. doi:10.1016/j.jmb.2015.09.014

[7] Van Rossum, G. and Drake Jr, F. L. (1995). Python tutorial (Centrum voor Wiskunde en Informatica Amsterdam, The Netherlands)

[8] Vangone A, S. V. C. L. O. R., Spinelli R (2011). Cocomaps: a web application to analyze and visualize contacts at the interface of biomolecular complexes. Bioinformatics 27, 2915–2916. doi:bioinformatics/btr484

[9] Negi SS, O. N. P. T. B. W., Schein CH (2007). Interprosurf: a web server for predicting interacting sites on protein surfaces. Bioinformatics 23, 3397–3399. doi:bioinformatics/btm474

[10] R Core Team (2017). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria

[11] Chang, W., Cheng, J., Allaire, J., Sievert, C., Schloerke, B., Xie, Y., et al. (2021). shiny: Web Application Framework for R. R package version 1.6.0