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High-density lipoprotein (HDL) nanoparticles promote endothelial cell (EC) function and suppress inflammation, but their utility in treating EC dysfunction has not been fully explored. Here, we describe a fusion protein named ApoA1-ApoM (A1M) consisting of apolipoprotein A1 (ApoA1), the principal structural protein of HDL that forms lipid nanoparticles, and ApoM, a chaperone for the bioactive lipid sphingosine 1-phosphate (S1P). A1M forms HDL-like particles, binds to S1P, and is signaling competent. Molecular dynamics simulations showed that the S1P-bound ApoM moiety in A1M efficiently activated EC surface receptors. Treatment of human umbilical vein ECs with A1M-S1P stimulated barrier function either alone or cooperatively with other barrier-enhancing molecules, including the stable prostacyclin analog iloprost, and suppressed cytokine-induced inflammation. A1M-S1P injection into mice during sterile inflammation suppressed neutrophil influx and inflammatory mediator secretion. Moreover, systemic A1M administration led to a sustained increase in circulating HDL-bound S1P and suppressed inflammation in a murine model of LPS-induced endotoxemia. We propose that A1M administration may enhance vascular endothelial barrier function, suppress cytokine storm, and promote resilience of the vascular endothelium. Editor's summary: Endothelial cell function is enhanced by bioactive lipids such as prostacyclin and sphingosine 1-phosphate (S1P) when bound to its chaperone apolipoprotein M (ApoM). Lin et al. generated a fusion protein called A1M to promote both S1P and prostacyclin signaling. A1M consisted of ApoM and ApoA1, a structural component of high-density lipoprotein (HDL) that stabilizes prostacyclin. When bound to S1P or a prostacyclin analog, A1M suppressed inflammation and increased the barrier function of cultured endothelial cells. In mouse models of acute or systemic inflammation, S1P-loaded A1M suppressed neutrophil infiltration, increases in circulating proinflammatory factors, and cytokine storm. The ApoA1 moiety of A1M therefore provides additional benefits over previously engineered S1P chaperones. —Wei Wong [ABSTRACT FROM AUTHOR]

Motivation Cancer is currently one of the most notorious diseases, with over 1 million deaths in the European Union alone in 2022. As each tumor can be composed of diverse cell types with distinct genotypes, cancer cells can acquire resistance to different compounds. Moreover, anticancer drugs can display severe side effects, compromising patient well-being. Therefore, novel strategies for identifying the optimal set of compounds to treat each tumor have become an important research topic in recent decades. Results To address this challenge, we developed a novel drug response prediction algorithm called Drug Efficacy Leveraging Forked and Specialized networks (DELFOS). Our model learns from multi-omics data from over 65 cancer cell lines, as well as structural data from over 200 compounds, for the prediction of drug sensitivity. We also evaluated the benefits of incorporating single-cell expression data to predict drug response. DELFOS was validated using datasets with unseen cell lines or drugs and compared with other state-of-the-art algorithms, achieving a high prediction performance on several correlation and error metrics. Overall, DELFOS can effectively leverage multi-omics data for the prediction of drug responses in thousands of drug–cell line pairs. Availability and implementation The DELFOS pipeline and associated data are available at github.com/MoreiraLAB/delfos. [ABSTRACT FROM AUTHOR]

A sequencing batch reactor (SBR) inoculated with activated sludge and bioaugmented with a dye-decolorizing yeast strain—Yarrowia lipolytica (HOMOGST27AB) was assembled to form yeast-bioaugmented aerobic granular sludge (AGS). The bioaugmented AGS-SBR was operated for the treatment of synthetic saline wastewater (12 g L−1) intermittently fed with a reactive textile dye (Navy Everzol ED) at 25, 15, and 7.5 mg L−1. Dye degradation did not occur, although some dye adsorbed to the granules. AGS-SBR performance in removing carbon and nitrogen was good and was not affected by the dye addition. Bioaugmentation with the yeast Y. lipolytica (HOMOGST27AB) occurred with success, proved by sequencing samples from granules throughout the reactor operation. The AGS core microbiome gathered essentially microorganisms from the Proteobacteria and Bacteroidetes phyla. The microbial profile showed a dynamic microbiome established at Phase I of the operation, with a high decrease in the abundance of Ignavibacterium from the initial biomass to the granules formed and an increase in Actinobacteria, Cytophagia, Flavobacteria, and Alphaproteobacteria in the remaining phases of the bioreactor operation. [ABSTRACT FROM AUTHOR]

Electroactive smart materials play an important role for tissue regenerative applications. Poly(vinylidene fluoride) (PVDF) is a specific subtype of piezoelectric electroactive material that generates electrical potential upon mechanical stimulation. This work focuses on the application of piezoelectric PVDF films for neural differentiation. Human neural precursor cells (hNPCs) are cultured on piezoelectric poled and non‐poled β‐PVDF films with or without a pre‐coating step of poly‐d‐lysine and laminin (PDL/L). Subsequently, hNPCs differentiation into the neuronal lineage is assessed (MAP2+ and DCX+) under static or dynamic (piezoelectric stimulation) culture conditions. The results demonstrate that poled and coated β‐PVDF films induce neuronal differentiation under static culture conditions which is further enhanced with mechanical stimulation. In silico calculations of the electrostatic potential of different domains of laminin, highlight the high polarity of those domains, which shows a clear preference to interact with the varying surface electric field of the piezoelectric material under mechanical stimulation. These interactions might explain the higher neuronal differentiation induced by poled β‐PVDF films pre‐coated with PDL/L under dynamic conditions. Our results suggest that electromechanical stimuli, such as the ones induced by piezoelectric β‐PVDF films, are suitable to promote neuronal differentiation and hold great promise for the development of neuroregenerative therapies. [ABSTRACT FROM AUTHOR]

Thiamethoxam (TMX) is an effective neonicotinoid insecticide. However, its widespread use is detrimental to non-targeted organisms and water systems. This study investigates the biodegradation of this insecticide by Labrys portucalensis F11. After 30 days of incubation in mineral salt medium, L. portucalensis F11 was able to remove 41%, 35% and 100% of a supplied amount of TMX (10.8 mg L−1) provided as the sole carbon and nitrogen source, the sole carbon and sulfur source and as the sole carbon source, respectively. Periodic feeding with sodium acetate as the supplementary carbon source resulted in faster degradation of TMX (10.8 mg L−1); more than 90% was removed in 3 days. The detection and identification of biodegradation intermediates was performed by UPLC-QTOF/MS/MS. The chemical structure of 12 metabolites is proposed. Nitro reduction, oxadiazine ring cleavage and dechlorination are the main degradation pathways proposed. After biodegradation, toxicity was removed as indicated using Aliivibrio fischeri and by assessing the synthesis of an inducible β-galactosidase by an E. coli mutant (Toxi-Chromo test). L. portucalensis F11 was able to degrade TMX under different conditions and could be effective in bioremediation strategies. [ABSTRACT FROM AUTHOR]

Diclofenac is a worldwide consumed drug included in the watch list of substances to be monitored according to the European Union Water Framework Directive (Directive 2013/39/EU). Aerobic granular sludge sequencing batch reactors (AGS-SBR) are increasingly used for wastewater treatment but there is scant information on the fate and effect of micropollutants to nutrient removal processes. An AGS-SBR fed with synthetic wastewater containing diclofenac was bioaugmented with a diclofenac degrading bacterial strain and performance and microbial community dynamics was analysed. Chemical oxygen demand, phosphate and ammonia removal were not affected by the micropollutant at 0.03 mM (9.54 mg L–1). The AGS was able to retain the degrading strain, which was detected in the sludge throughout after augmentation. Nevertheless, besides some adsorption to the biomass, diclofenac was not degraded by the augmented sludge given the short operating cycles and even if batch degradation assays confirmed that the bioaugmented AGS was able to biodegrade the compound. The exposure to the pharmaceutical affected the microbial community of the sludge, separating the two first phases of reactor operation (acclimatization and granulation) from subsequent phases. The AGS was able to keep the bioaugmented strain and to maintain the main functions of nutrient removal even through the long exposure to the pharmaceutical, but combined strategies are needed to reduce the spread of micropollutants in the environment. [ABSTRACT FROM AUTHOR]

Endocrine disrupting compounds (EDCs) in the environment are considered a motif of concern, due to the widespread occurrence and potential adverse ecological and human health effects. The natural estrogen, 17β-estradiol (E2), is frequently detected in receiving water bodies after not being efficiently removed in conventional wastewater treatment plants (WWTPs), promoting a negative impact for both the aquatic ecosystem and human health. In this study, the biodegradation of E2 by Rhodococcus sp. ED55, a bacterial strain isolated from sediments of a discharge point of WWTP in Coloane, Macau, was investigated. Rhodococcus sp. ED55 was able to completely degrade 5 mg/L of E2 in 4 h in a synthetic medium. A similar degradation pattern was observed when the bacterial strain was used in wastewater collected from a WWTP, where a significant improvement in the degradation of the compound occurred. The detection and identification of 17 metabolites was achieved by means of UPLC/ESI/HRMS, which proposed a degradation pathway of E2. The acute test with luminescent marine bacterium Aliivibrio fischeri revealed the elimination of the toxicity of the treated effluent and the standardized yeast estrogenic (S-YES) assay with the recombinant strain of Saccharomyces cerevisiae revealed a decrease in the estrogenic activity of wastewater samples after biodegradation. [ABSTRACT FROM AUTHOR]

Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2) is composed of four structural proteins and several accessory non-structural proteins. SARS-CoV-2's most abundant structural protein, Membrane (M) protein, has a pivotal role both during viral infection cycle and host interferon antagonism. This is a highly conserved viral protein, thus an interesting and suitable target for drug discovery. In this paper, we explain the structural nature of M protein homodimer. To do so, we developed and applied a detailed and robust in silico workflow to predict M protein dimeric structure, membrane orientation, and interface characterization. Single Nucleotide Polymorphisms (SNPs) in M protein were retrieved from over 1.2 M SARS-CoV-2 genomes and proteins from the Global Initiative on Sharing All Influenza Data (GISAID) database, 91 of which were located at the predicted dimer interface. Among those, we identified SNPs in Variants of Concern (VOC) and Variants of Interest (VOI). Binding free energy differences were evaluated for dimer interfacial SNPs to infer mutant protein stabilities. A few high-prevalent mutated residues were found to be especially relevant in VOC and VOI. This realization may be a game-changer to structure-driven formulation of new therapeutics for SARS-CoV-2. [ABSTRACT FROM AUTHOR]

A study investigated the aerobic metabolism of fluorobenzene by Rhizobiales sp. strain F11. Liquid chromatography-mass spectrometry analysis showed that 4-fluorocatechol and catechol are formed as intermediates during fluorobenzene degradation by cell suspensions.

The application of the improved MM-PBSA approach that combining molecular mechanics and continuum solvent permits to calculate the free energy differences through alanine mutation is described to identify the binding determinants of the complex formed between the IgG1 (immunoglobulin-binding protein G) and protein G. This method is improved for the detection of an important amino acid residue that frequently constitutes a hot spot-tryptophan.

Background In cancer research, high-throughput screening technologies produce large amounts of multiomics data from different populations and cell types. However, analysis of such data encounters difficulties due to disease heterogeneity, further exacerbated by human biological complexity and genomic variability. The specific profile of cancer as a disease (or, more realistically, a set of diseases) urges the development of approaches that maximize the effect while minimizing the dosage of drugs. Now is the time to redefine the approach to drug discovery, bringing an artificial intelligence (AI)–powered informational view that integrates the relevant scientific fields and explores new territories. Results Here, we show SYNPRED, an interdisciplinary approach that leverages specifically designed ensembles of AI algorithms, as well as links omics and biophysical traits to predict anticancer drug synergy. It uses 5 reference models (Bliss, Highest Single Agent, Loewe, Zero Interaction Potency, and Combination Sensitivity Score), which, coupled with AI algorithms, allowed us to attain the ones with the best predictive performance and pinpoint the most appropriate reference model for synergy prediction, often overlooked in similar studies. By using an independent test set, SYNPRED exhibits state-of-the-art performance metrics either in the classification (accuracy, 0.85; precision, 0.91; recall, 0.90; area under the receiver operating characteristic, 0.80; and F1-score, 0.91) or in the regression models, mainly when using the Combination Sensitivity Score synergy reference model (root mean square error, 11.07; mean squared error, 122.61; Pearson, 0.86; mean absolute error, 7.43; Spearman, 0.87). Moreover, data interpretability was achieved by deploying the most current and robust feature importance approaches. A simple web-based application was constructed, allowing easy access by nonexpert researchers. Conclusions The performance of SYNPRED rivals that of the existing methods that tackle the same problem, yielding unbiased results trained with one of the most comprehensive datasets available (NCI ALMANAC). The leveraging of different reference models allowed deeper insights into which of them can be more appropriately used for synergy prediction. The Combination Sensitivity Score clearly stood out with improved performance among the full scope of surveyed approaches and synergy reference models. Furthermore, SYNPRED takes a particular focus on data interpretability, which has been in the spotlight lately when using the most advanced AI techniques. [ABSTRACT FROM AUTHOR]

Membrane proteins (MPs) are key players in a variety of different cellular processes and constitute the target of around 60% of all Food and Drug Administration–approved drugs. Despite their importance, there is still a massive lack of relevant structural, biochemical and mechanistic information mainly due to their localization within the lipid bilayer. To help fulfil this gap, we developed the MEmbrane protein dimer Novel Structure Analyser database (MENSAdb). This interactive web application summarizes the evolutionary and physicochemical properties of dimeric MPs to expand the available knowledge on the fundamental principles underlying their formation. Currently, MENSAdb contains features of 167 unique MPs (63% homo- and 37% heterodimers) and brings insights into the conservation of residues, accessible solvent area descriptors, average B -factors, intermolecular contacts at 2.5 Å and 4.0 Å distance cut-offs, hydrophobic contacts, hydrogen bonds, salt bridges, π–π stacking, T-stacking and cation–π interactions. The regular update and organization of all these data into a unique platform will allow a broad community of researchers to collect and analyse a large number of features efficiently, thus facilitating their use in the development of prediction models associated with MPs. Database URL : http://www.moreiralab.com/resources/mensadb. [ABSTRACT FROM AUTHOR]

The occurrence of pharmaceuticals in the environment is a topic of concern. Carbamazepine (CBZ) is a widespread antiepileptic drug and due to its physical-chemical characteristics minimal removal is achieved in conventional water treatments, and thus has been suggested as a molecular marker of wastewater contamination in surface water and groundwater. The present study reports the biotransformation of CBZ by the bacterial strain Labrys portucalensis F11. When supplied as a sole carbon source, a 95.4% biotransformation of 42.69 μM CBZ was achieved in 30 days. In co-metabolism with acetate, complete biotransformation was attained at a faster rate. Following a target approach, the detection and identification of 14 intermediary metabolites was achieved through UPLC-QTOF/MS/MS. Biotransformation of CBZ by the bacterial strain is mostly based on oxidation, loss of -CHNO group and ketone formation reactions; a biotransformation pathway with two routes is proposed. The toxicity of untreated and treated CBZ solutions was assessed using Vibrio Fischeri and Lepidium sativum acute toxicity tests and Toxi-Chromo Test. The presence of CBZ and/or its degradations products in solution resulted in moderate toxic effect on Vibrio Fischeri , whereas the other organisms were not affected. To the best of our knowledge this is the first report that proposes the metabolic degradation pathway of CBZ by a single bacterial strain. Unlabelled Image • Biotransformation of carbamazepine by L. portucalensis strain F11 was achieved. • The kinetics and pathway of carbamazepine degradation was proposed. • The supplementation with acetate resulted in increased biodegradation rates. • The chemical structure of fourteen degradation intermediates was proposed. • Carbamazepine biotransformation products revealed toxicity to V. fischeri. [ABSTRACT FROM AUTHOR]

By employing pangenomic analysis, the authors identified not only the core gene clusters responsible for PET degradation but also other plastic- and plasticizer-degrading genes, suggesting the potential for the biodegradation of mixed plastic waste. This special edition intends to highlight how omics approaches have been used in biodegradation studies to understand the mechanisms involved and improve biodegradation processes. [Extracted from the article]

A computational alanine-scanning mutagenesis method is used for decomposing the binding free energy difference in its different energetic factors in order to obtain a microscopic level, atomistic picture of hot spot solvation to verify the O-ring hypothesis. It is inferred that within a flexible, dynamic protein model the warm/hot spot residues are kept sheltered from the bulk solvent during the whole simulation, and all the outcomes confirm the O-ring hypothesis.

Diclofenac (DCF) is a widely used non-steroidal anti-inflammatory pharmaceutical which is detected in the environment at concentrations which can pose a threat to living organisms. In this study, biodegradation of DCF was assessed using the bacterial strain Labrys portucalensis F11. Biotransformation of 70% of DCF (1.7–34 μM), supplied as the sole carbon source, was achieved in 30 days. Complete degradation was reached via co-metabolism with acetate, over a period of 6 days for 1.7 µM and 25 days for 34 μM of DCF. The detection and identification of biodegradation intermediates was performed by UPLC-QTOF/MS/MS. The chemical structure of 12 metabolites is proposed. DCF degradation by strain F11 proceeds mainly by hydroxylation reactions; the formation of benzoquinone imine species seems to be a central step in the degradation pathway. Moreover, this is the first report that identified conjugated metabolites, resulting from sulfation reactions of DCF by bacteria. Stoichiometric liberation of chlorine and no detection of metabolites at the end of the experiments are strong indications of complete degradation of DCF by strain F11. To the best of our knowledge this is the first report that points to complete degradation of DCF by a single bacterial strain isolated from the environment. [ABSTRACT FROM AUTHOR]

We present the performance of HADDOCK, our information-driven docking software, in the second edition of the D3R Grand Challenge. In this blind experiment, participants were requested to predict the structures and binding affinities of complexes between the Farnesoid X nuclear receptor and 102 different ligands. The models obtained in Stage1 with HADDOCK and ligand-specific protocol show an average ligand RMSD of 5.1 Å from the crystal structure. Only 6/35 targets were within 2.5 Å RMSD from the reference, which prompted us to investigate the limiting factors and revise our protocol for Stage2. The choice of the receptor conformation appeared to have the strongest influence on the results. Our Stage2 models were of higher quality (13 out of 35 were within 2.5 Å), with an average RMSD of 4.1 Å. The docking protocol was applied to all 102 ligands to generate poses for binding affinity prediction. We developed a modified version of our contact-based binding affinity predictor PRODIGY, using the number of interatomic contacts classified by their type and the intermolecular electrostatic energy. This simple structure-based binding affinity predictor shows a Kendall's Tau correlation of 0.37 in ranking the ligands (7th best out of 77 methods, 5th/25 groups). Those results were obtained from the average prediction over the top10 poses, irrespective of their similarity/correctness, underscoring the robustness of our simple predictor. This results in an enrichment factor of 2.5 compared to a random predictor for ranking ligands within the top 25%, making it a promising approach to identify lead compounds in virtual screening. [ABSTRACT FROM AUTHOR]

An aerobic granular sludge-sequencing batch reactor (AGS-SBR) was fed for 28-days with a simulated wastewater containing a mixture of chiral pharmaceuticals (CPs) (alprenolol, bisoprolol, metoprolol, propranolol, venlafaxine, salbutamol, fluoxetine and norfluoxetine), each at 1.3 μg L −1 . AGS-SBR exhibited the highest removal efficiency for norfluoxetine, with preferential removal of the ( R )-enantiomer indicating that biological-mediated processes occurred. For all other CPs, removal was non-enantioselective, occurring through biosorption onto AGS. A gradual decline of CPs removal was observed, probably related to the decrease of AGS adsorption capacity. Moreover, chemical oxygen demand (COD) content in the bulk liquid after anaerobic feeding increased, and P-release dropped, probably because the polyphosphate-accumulating organism's activity was affected. Nitrification was also affected as indicated by the ammonium effluent concentration increase. Moreover, CPs exposure promoted AGS disintegration, with decreasing granule size. After stopping CPs feeding, the AGS started to recover its compact structure, and the system returned its normal performance concerning COD- and P-removal. N-removal seemed to be a more sensitive process, as while the ammonium levels were fully restored at the end of operation, nitrite reduction was only partially restored. Results provide useful information on AGS performance during the treatment of wastewater containing pharmaceuticals, a frequent scenario in WWTP. [ABSTRACT FROM AUTHOR]

Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural- and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM), for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with different cost functions. The best model was achieved by the use of the conditional inference random forest (c-forest) algorithm with a dataset pre-processed by the normalization of features and with up-sampling of the minor class. The method has an overall accuracy of 0.80, an F1-score of 0.73, a sensitivity of 0.76 and a specificity of 0.82 for the independent test set. [ABSTRACT FROM AUTHOR]

Protein–protein (P–P) 3D structures are fundamental to structural biology and drug discovery. However, most of them have never been determined. Many docking algorithms were developed for that purpose, but they have a very limited accuracy in generating native-like structures and identifying the most correct one, in particular when a single answer is asked for. With such a low success rate it is difficult to point out one docked structure as being native-like. Here we present a new, high accuracy, scoring method to identify the 3D structure of P–P complexes among a set of trial poses. It incorporates alanine scanning mutagenesis experimental data that need to be obtained a priori. The scoring scheme works by matching the computational and the experimental alanine scanning mutagenesis results. The size of the trial P–P interface area is also taken into account. We show that the method ranks the trial structures and identifies the native-like structures with unprecedented accuracy (∼94%), providing the correct P–P 3D structures that biochemists and molecular biologists need to pursue their studies. With such a success rate, the bottleneck of protein–protein docking moves from the scoring to searching algorithms. [ABSTRACT FROM AUTHOR]

Fluoxetine (FLX) is a chiral fluorinated pharmaceutical indicated mainly for the treatment of depression and is one of the most dispensed drugs in the world. There is clear evidence of environmental contamination with this drug and its active metabolite norfluoxetine (NFLX). In this study the enantioselective biodegradation of racemic FLX and of its enantiomers by Labrys portucalensis strain F11 was assessed. When 2 μM of racemic FLX was supplemented as sole carbon source, complete removal of both enantiomers, with stoichiometric liberation of fluoride, was achieved in 30 d. For racemic FLX concentration of 4 and 9 μM, partial degradation of the enantiomers was obtained. In the presence of acetate as an additional carbon source, at 4, 9 and 21 μM of racemic FLX and at 25 μM of racemic FLX, (S)-FLX or (R)-FLX, complete degradation of the two enantiomers occurred. At higher concentrations of 45 and 89 μM of racemic FLX, partial degradation was achieved. Preferential degradation of the (R)-enantiomer was observed in all experiments. To our knowledge, this is the first time that enantioselective biodegradation of FLX by a single bacterium is reported. [ABSTRACT FROM AUTHOR]

ABSTRACT A detailed comprehension of protein-based interfaces is essential for the rational drug development. One of the key features of these interfaces is their solvent accessible surface area profile. With that in mind, we tested a group of 12 SASA-based features for their ability to correlate and differentiate hot- and null-spots. These were tested in three different data sets, explicit water MD, implicit water MD, and static PDB structure. We found no discernible improvement with the use of more comprehensive data sets obtained from molecular dynamics. The features tested were shown to be capable of discerning between hot- and null-spots, while presenting low correlations. Residue standardization such as relSASA i or rel/resSASA i, improved the features as a tool to predict ΔΔ Gbinding values. A new method using support machine learning algorithms was developed: SBHD (Sasa-Based Hot-spot Detection). This method presents a precision, recall, and F1 score of 0.72, 0.81, and 0.76 for the training set and 0.91, 0.73, and 0.81 for an independent test set. Proteins 2014; 82:479-490. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Abstract: Background: The details of the functional interaction between G proteins and the G protein coupled receptors (GPCRs) have long been subjected to extensive investigations with structural and functional assays and a large number of computational studies. Scope of review: The nature and sites of interaction in the G-protein/GPCR complexes, and the specificities of these interactions selecting coupling partners among the large number of families of GPCRs and G protein forms, are still poorly defined. Major conclusions: Many of the contact sites between the two proteins in specific complexes have been identified, but the three dimensional molecular architecture of a receptor-Gα interface is only known for one pair. Consequently, many fundamental questions regarding this macromolecular assembly and its mechanism remain unanswered. General significance: In the context of current structural data we review the structural details of the interfaces and recognition sites in complexes of sub-family A GPCRs with cognate G-proteins, with special emphasis on the consequences of activation on GPCR structure, the prevalence of preassembled GPCR/G-protein complexes, the key structural determinants for selective coupling and the possible involvement of GPCR oligomerization in this process. [Copyright &y& Elsevier]