Computational design for thermostabilization of GPCRs

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ScienceDirect Computational design for thermostabilization of GPCRs Petr Popov1,2, Igor Kozlovskii2 and Vsevolod Katritch2,3 GPCR superfamily is the largest clinically relevant family of targets in human genome; however, low thermostability and high conformational plasticity of these integral membrane proteins make them notoriously hard to handle in biochemical, biophysical, and structural experiments. Here, we describe the recent advances in computational approaches to design stabilizing mutations for GPCR that take advantage of the structural and sequence conservation properties of the receptors, and employ machine learning on accumulated mutation data for the superfamily. The fast and effective computational tools can provide a viable alternative to existing experimental mutation screening and are poised for further improvements with expansion of thermostability datasets for training the machine learning models. The rapidly growing practical applications of computational stability design streamline GPCR structure determination and may contribute to more efficient drug discovery. Addresses 1 Skolkovo Institute of Science and Technology, Moscow, Russia 2 Moscow Institute of Physics and Technology, Dolgoprudny, Russia 3 Departments of Biological Sciences and Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA, USA Corresponding author: Katritch, Vsevolod ([email protected])

challenging for biochemical, biophysical and structural biology experiments when they require the protein to be removed from their native membrane environment with detergents. These challenges precluded the determination of GPCR structures until year 2000, when the structure of the bovine rhodopsin was solved [4], and then it took another 7 years to solve the structure of the adrenergic receptor b2AR — the first GPCR with a diffusible ligand [5]. Several key technologies have been developed to stabilize GPCRs [6], including improved protocols for reliable BV expression [7], new detergents for protein extraction/purification [8], and lipidic cubic phase (LCP) for crystallization [9]. Equally important have been technologies for rational protein engineering for GPCRs [10], which include truncation of the long unstructured termini and/or extracellular domains, as well as fusion of small soluble domains such as T4lysozyme (T4L) or cytochrome b562RIL (BRIL) in place of intracellular loops [11] or at the N-terminus [12]. In most cases, only combination of all these tools, as well as further stabilization of GPCR complexes by very high affinity ligands [13] – and sometimes also high-affinity antibodies or nanobodies [14] – made structure determination possible [15].

Current Opinion in Structural Biology 2019, 55:25–33 This review comes from a themed issue on Theory and simulation: demystifying GPCRs Edited by Tom Blundell and Shoba Ranganathan

https://doi.org/10.1016/j.sbi.2019.02.010 0959-440X/ã 2018 Elsevier Inc. All rights reserved.

Introduction G protein-coupled receptors (GPCRs) comprise the most populous protein family in human genome, involved in all major systems of our bodies [1,2]. GPCRs are the most prominent targets for drug development, with about 40% of drugs acting via these receptors [3]. At the same time, high hydrophobicity and conformational plasticity of their 7-transmembrane (7TM) domains make GPCRs notoriously challenging to work with. Many GPCRs are hard to express and to fold correctly in heterologous expression systems, and they are often unstable and sensitive to storage and handling conditions. This is especially www.sciencedirect.com

Stabilizing the point mutations in 7TM domains of GPCR has emerged as one of the key approaches in GPCR studies, being implemented first for adenosine A2A [16] and b1AR [17] receptors. Since then, stabilizing mutations helped to solve structures for more than 30 GPCRs, often in combinations with a soluble domain fusion. Two key experimental techniques have been developed so far to identify stabilizing mutations: (i) systematic alanine scanning of the 7TM residues [18] and (ii) direct protein evolution [19]. While both experimental technologies proved to be useful in stabilization and determination of structures for multiple GPCRs [20], they are rather costly and have intrinsic limitations. Thus, alanine scanning would miss many beneficial mutations in those positions where alanine does not improve stability of the wild type (WT), as well as double mutations. For the direct evolution, too many mutations are often generated, some of them not actually contributing to stability [21,22]. The challenges for GPCR thermostabilization are highlighted by the fact that a combination of as many as six direct evolution and four alanine scanningderived mutations was needed recently to solve PTH1R structure [23]. Ability to predict stabilizing mutations computationally, based on analysis of GPCR sequence, structure and Current Opinion in Structural Biology 2019, 55:25–33

26 Theory and simulation: demystifying GPCRs

accumulated information on mutations in the whole family would have multiple benefits by (i) helping to reduce the number of mutations to test, (ii) better understanding the conformational and functional consequences of each mutation and their combinations (iii) enabling an iterative cycle where knowledge generated for mutations can be used to train next generation models, thus achieving better predictions at each step. This would greatly facilitate and streamline design of optimal constructs not only for structural biology including crystallography and cryoEM, but also for biophysical studies, high throughput screening (HTS) assays and ultimately for structurebased drug design [20]. A major progress toward developing of such predictive computational models has been made in the last few years, and an ongoing large-scale experimental validation is adding new stabilized receptors and enabling structure determination breakthroughs for GPCRs.

function that classify point mutations as stabilizing and de-stabilizing (or neutral) [34–37]. Features may include physicochemical properties of amino acid residues, local geometrical properties of the mutation site, secondary structure type, pharmacophores, global protein properties, forces acting on residues, specific energy terms, statistical potentials, etc. The learning approaches can employ support vector machines, neural networks, random forest, regression, Bayesian classifiers, and others supervised learning methods.

General thermostabilization approaches

GPCRs require specific tools for stabilization

Numerous computational approaches, including sequence-based, physics-based, and machine learning approaches have been developed to predict thermostable mutants for soluble proteins, as recently reviewed for example in Ref. [24].

Approaches to GPCR thermostabilization need to consider several distinct features of the family as recently reviewed in Refs. [20,39]. First, GPCR 7TM domains are embedded in lipidic environment of cell membrane; therefore, they lack hydrophobic protein core, but have patches of highly hydrophobic, hydrophilic and charged regions on the protein surface. Secondly, GPCRs evolved to maintain high conformational flexibility as a part of their signaling function, which involves large-scale conformational changes throughout the 7TM domain. Stabilization for structural studies, therefore, should also reduce conformational heterogeneity, confining receptor to a specific conformational state — active-like or inactive-like.

Sequence-based approaches rely on amino acid sequence comparison within a closely related family of proteins, usually prioritizing residues from more stable homologues in thermophilic organisms [25,26]. While such strategies work great for bacterial proteins, GPCRs do not have closely related bacterial counterparts. Still, some animals are known to have higher body temperature, for example, domestic turkey or chicken (up to 43 C), and a turkey b1AR homologue was selected as the starting point for further optimization by alanine scanning due to its higher thermostability [17]. This strategy though has had limited success in other GPCRs, and in general, crystallization of human version of the protein, or its minimally modified construct, is preferable for drug discovery application. Physics-based methods employ direct estimation of differences in residue interactions and/or free energy of the protein folding for mutants, DDG, using either classical force-fields or knowledge-based/statistical potentials [27– 31]. It is also common to use ensemble of several physicsbased methods to assess point mutation effects on protein stability [32,33]. Accuracy of physics-based methods, especially those that use detailed atomistic force fields, however, relies on accurate structural information for the protein target, preferably a crystal structure or a close homology model, and, therefore, their predictive power quickly deteriorates when homology is distant. Machine learning-based approaches use various types of molecular features that may be related to thermostability, and they attempt to reconstruct discriminative Current Opinion in Structural Biology 2019, 55:25–33

Training and benchmarking of existing knowledge-based potentials and machine learning models are usually based on comprehensive databases of mutations in globular proteins such as ProTherm database [38]. The ProTherm database, however, has little information for thermostability of membrane proteins; therefore, their applications to GPCR is limited.

At the same time, common properties and common 7TM topology shared by GPCRs open an opportunity for thermostabilization tools specific for this superfamily. Thus, the common 7TM topology makes it possible to define accurate correspondence between positions in 7TM helices for all GPCRs by aligning their sequences [40] or structures [41]. Careful analysis suggests that not only positions, but the general interaction networks can be conserved across many receptors [42]. One obvious idea would be that such family similarity would make stabilizing mutations, experimentally discovered in a specific receptor transferrable to other GPCRs. Initial analysis of available data, showed that such transferability is limited only to very closely related subfamily members [43], but usually not transferable to other GPCRs [20,39]. However, there have been several notable exceptions documented, where specific mutations in certain key positions do show broader transferability [44,45], which can be exploited for design. This approach is used by GPCRdb database construct design tool [46], which aims to transfer accumulated knowledge about stabilizing point mutations in GPCR structures as a set of rules, as well in knowledge-based module in CompoMug method [47] as described in the next Section. www.sciencedirect.com

Computational design for thermostabilization of GPCRs Popov, Kozlovskii and Katritch 27

Several GPCR specific physics-based methods employing conformational modeling and molecular dynamics simulations of receptors have been developed recently. One of such methods include LITiConDesign [48] which is based on ‘ab initio’ conformational sampling of GPCR helices and side chains using fast LITiCon approach. This method was benchmarked using previously identified mutations on three receptors, b1AR, A2A, and NTSR1. Interestingly, the retrospective benchmark suggested that the ‘predictability’ rates obtained with fast sampling predictions are similar to those obtained using conformational ensembles from long atomistic MD simulations [49]. Both fast LITiCon and a slower MD approaches though showed relatively modest performance on this benchmark, with AUC values in 0.64–0.67 range. Another recently developed physics-based method [45], used energetic and entropic terms to predict a potentially transferrable thermostabilizing mutation in A2A, M2R, and EP4 receptors. Because GPCRs evolved with more selective pressure for certain functional features than for thermostability, it is natural to expect that some variations in their sequence are not stability-optimal, which can be exploited via combination of structure-based and bioinformatics approaches. Such a combination has been employed by Chen et al. to design thermostable mutants in b1AR [51]. The authors used evolutionary-based and physics-based approaches to determine poorly conserved and poorly packed amino acid residues in the b1AR using its thermostabilized structure b1AR-M23 [51], and identified several additional mutations further improving stability of this construct. These studies represent an important progress in computer assisted GPCR thermostabilization. However, a comprehensive computational tool that would consistently show a broad practical applicability to GPCRs has remained in high demand, as reviewed in Ref. [20].

CompoMug approach Some of the key requirements for a predictive tool with practical utility for prospective discovery of stabilized mutants are (i) Immediate applicability to a broad range of GPCR targets, including different classes of receptors. (ii) Ability to generate reasonable number of stabilizing point mutations (>10) from a manageable number of candidates (iii) Ability to iteratively improve with newly acquired experimental data on GPCR structure and mutations. Since any single method is unlikely to achieve these goals alone, a modular CompoMug design was proposed [47], which combines four conceptually distinct complementary approaches (Figure 1).

Knowledge-based module The knowledge-based module in CompoMug relies on a short list of mutations that have proven transferability between two or more GPCRs. As mentioned above, www.sciencedirect.com

majority of stabilizing point mutations are not transferable between different GPCRs [20,39]. However, a few structurally or functionally conserved sites have emerged, where specific mutations consistently show improved stability and allowed crystallization of multiple receptors. Thus, mutation in residue position of 3.41 (Ballesteros-Weinstein numbering nomenclature [40]) to Trp has been shown to stabilize several GPCRs, presumably by forming an additional stacking to the conserved P5.50 residue and a hydrogen bond to the backbone carbonyl [44]. The list also includes mutations in the sodium binding pocket, for example, D2.50N(G), S3.39A, and D7.49N, which can decouple ligand binding from conformational changes in the intracellular side of the receptor and reduce conformational heterogeneity of the receptors [52]. Although currently only a few mutations in class A can be classified as transferrable ‘knowledge-based’, the list may grow. For example, double mutation of residues in positions D6.33 and E7.59, which improved stability and reduced aggregation in CCR5 [53] and more recently in CCR3 chemokine receptors [54], supposedly by creating a new network of salt bridges between TM3, TM6, and TM7 helices [54]. Another transferable mutation has emerged as S3.39R, which introduces basic R3.39 side chain capable of blocking the sodium pocket by creating a new salt bridge with the acidic D2.50 side chain [50,55].

Sequence-based module The sequence-based module identifies residues of the receptor that deviate from a standard conservation pattern in multiple sequence alignment (MSA) in an evolutionarily related group of GPCRs. Such residues are more likely to be destabilizing, so reverting the sequence to the most common amino acids in these positions can increase thermostability. The CompoMug implementation captures residue deviations at 5 different levels of GPCR hierarchy: (1) ortholog sequences corresponding to the species variations of the target receptor, (2) sequences corresponding to the common sub-family (sequence identity for the TM regions >40%), (3) sequences corresponding to the common GPCR branch (sequence identity for the TM regions >30%), (4) sequences corresponding to the whole non-olfactory class A GPCR [56], and (5) sequences corresponding to the crystallized receptors. For each group, the MSA can be generated using the structure-based alignment feature in the GPCRdb [57] or other tools. Although the MSA for crystallized receptors is not directly related to the evolutionary variation, it may contain information relevant for the GPCR stability and propensity for crystallization. At the same time, the MSA for whole class A GPCR could capture rare variations in the most conserved residue positions of class A, including N1.50, D(E)R3.50Y, FxxxCWxP6.50 and NP7.50xxY motifs. A global score is calculated as the average of the individual MSA scores. Because Gly often has destabilizing effect on the a-helices in the 7TM domain, we Current Opinion in Structural Biology 2019, 55:25–33

28 Theory and simulation: demystifying GPCRs

Figure 1

Knowledge

Machine Learning

Sequence

Structure Current Opinion in Structural Biology

Schematic presentation of CompoMug’s architecture. It comprises four complementary modules: machine learning-based, knowledge-based, sequence-based, and structure-based.

considered Gly as a special case, boosting its ‘deviation score’ by factor of two.

Structure-based The structure-based module in CompoMug is focused on identifying pairs of residues that can be replaced by either charged amino acids to form a new stabilizing salt bridge, or by cysteines to form a disulfide bond. Optimally introducing such ionic or covalent ‘staples’ between 7TM helices may reduce flexibility and increase stability of the receptor. Predicting optimal interactions requires an accurate 3D structural model, for example, built using homology with a known crystallographic structure. In CompoMug, initial selection of potential positions for ionic lock and disulfide bonds is performed by a fast geometry-based search in the structural model. In selected positions, all possible combinations of charged Current Opinion in Structural Biology 2019, 55:25–33

pairs (E-K, E-R, D-K, D-R) or cysteines are then tested through local conformational optimization of the mutants, and the pairs with the best predicted free energy improvements are selected. Depending on a specific functional conformation captured in the structural model, the resulting ‘staples’ may stabilize inactive or active state, or any specific intermediate. Unlike other, more subtle types of interactions, introduction of strong ionic or disulfide staples can bring very significant gains for thermostability when the bond is optimal. However, even slight deviations from optimal may be detrimental for the construct, so there are usually few (if any) high-scoring candidates for any target protein.

Machine learning The machine-learning (ML) module in CompoMug employs supervised learning on available data for GPCR www.sciencedirect.com

Computational design for thermostabilization of GPCRs Popov, Kozlovskii and Katritch 29

stabilizing mutations to generate prediction models. Specifically, CompoMug has support vector machine (SVM) as a learning algorithm and employs a comprehensive set of physicochemical and structural properties of amino acid residues as the input feature set. The first version uses the available 77 known alanine scanning mutations to construct a training dataset [16,17,20,58]. To optimize CompoMug ML performance, we derived four models with a range of values for SVM regularization and kernel coefficients, yielding different expected specificity versus selectivity levels. For example, the first model produces very few (if any), highly confident point mutation candidates, while the last model produces more candidates, but many of those can be false-positives. Combining these models with different weights, one can achieve the desired number of predicted candidates for each target. It should be noted that the machine-learning module has not reached its full potential yet, and one of its major advantages is the ability to iteratively improve with accumulation of additional thermostability data, for example, generated by large scale screening projects, direct evolution and in the process of CompoMug testing itself, thus leading to more powerful ML models.

substantially affect ligand binding, as ensured by the post-processing module of CompoMug that filters out point mutation candidates in proximity of the orthosteric pocket that might affect binding properties of the receptor.

Serotonin 5-HT2c receptor Among the 40 point mutations predicted with CompoMug for 5-HT2c [47], 10 showed significant thermostability improvement, increasing apparent melting temperature by at least 1.5 C as compared to the base construct (see Figure 2a). Remarkably, one of the stabilizing mutations, C3607.45N, improved the stability by 9 C, enabling determination of both agonist-bound and antagonist-bound high-resolution structures for this receptor [59]. All modules except knowledge-based contributed to stabilizing mutations, including 5 for sequence-based, 2 for structure-based (both disulfide bond and salt bridge) and 3 for machine learning modules. A combination of three mutations gave a construct with DTm 13 C, which was also best responding to stabilization with an antagonist, for the total of DTm 21 C stability gain [47].

Frizzled FZD4 receptor First applications of CompoMug-predicted mutations The CompoMug approach has been applied prospectively to predict stabilizing mutations in more than a dozen GPCRs so far, showing reasonable rate of success in experimental validation for most of them (Table 1). As a rule, top 40–60 point mutation candidates were tested experimentally, and the best validated hits were then combined for the final construct. Moreover, mutations predicted with CompoMug proved essential for successful crystallization and structure determination of these receptors, with four of them published recently (Figure 2). Importantly, the introduced mutations did not

Until very recently, Class F was represented by only one structure of the smoothened receptor (SMO), which is also the most distant relative to the other members of the class, frizzled receptors. An additional challenge for unraveling structures of the frizzled receptors, including the potential clinical target FZD4, is lack of known highaffinity ligands binding to their 7TM domain. Out of the 37-point mutations predicted with CompoMug, five showed significant thermostabilization of FZD4 (see Figure 2b). Notably, all five mutants were selected by the sequence-based module, suggesting that the quality of the homology model based on the SMO template was insufficient for the other modules. The construct with

Table 1 GPCRs thermostabilized using CompoMug predictions, and their structures solved Target

# tested mutants

Hit rate a

Best single DTm,  C

Best DTm,  C combined b

Crystallized/Solved

Comment

5-HT2C Target #2 Target #3 Target #4 Target #5 Target #6 Target #7 FZD4 Target #9 Target #10 EP3 CB2 CysLT2 Class C

40 40 60 40 60 40 60 60 60 56 30 36 40 40–60

25% 27% 17% 20.0% 12% 10.0% 7% 25% 11% 53% 20% 25% 12% 0%

9 6 10 4 4 3 4 3 3 11 0 5 2 0

20 7 NAc 4 4 4 NA 16 NA NA 0 9 3 0

Yes/Yes Yes/Yes Yes/On hold Yes/Yes In progress In progress In progress Yes/Yes In progress In progress Yes/Yes Yes/Yes Yes/Yes No/No

1 mutant in structure In refinement Solved by another group In refinement

a b c

4 mutants in apo structure

Improved Diffraction 5 mutations in structure 3 mutants in structures No effect, high aggregation

Ratio of the detected thermostabilizing point mutations (DTm > 1.5 C) to the total predicted mutations. Apparent melting temperature for the best combination tested. Not available.

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30 Theory and simulation: demystifying GPCRs

Figure 2

(a)

5-HT2c

(b)

FZD4

(c)

EP3

(d)

CB2

Current Opinion in Structural Biology

Structures of GPCRs solved with stabilizing mutations predicted by CompoMug. (a) serotonin 5-HT2c receptor (PDB:), (b) frizzled FZD4 receptor, (c) prostaglandin EP3 receptor and (d) cannabinoid CB2 receptor. Stabilizing amino acid residues predicted with CompoMug are shown as sticks, with those mutations that were included into the final crystallized construct colored green.

quadruple mutation (M309L-C450I-C507F-S508Y) helped to solve a 2.4 A˚ resolution structure of the apo form of human FZD4. Interestingly, C507F-S508Y are two adjacent mutations, which apparently helped to stabilize the intracellular H8 helix running parallel to membrane like in other GPCR classes.

Prostaglandin EP3 receptor The base constructs for the EP3 receptor was already highly optimized for termini truncations and fusion insertion, showing good expression, thermostability profiles and ability to form crystals. The crystal quality and diffraction, however, remained poor. Interestingly, none out of 16 tested CompoMug point mutations further increased the EP3 construct thermostability, yielding 9 neutral and 7 destabilizing mutations (Figure 2C), though some of them slightly improved the protein expression. Despite lack of further improvements in DTm, four of the most promising mutants were tested directly in crystallization trials, and the construct with G2866.39A mutation yielded much better crystal quality and diffraction, resulting in 2.5 A˚ resolution structure of EP3 receptor [60]. This result is likely explained by the mutation effect on reducing heterogeneity of construct rather than thermostability per se, which would be hard to detect by experimental screening approaches focused on measuring DTm.

Cannabinoid receptor CB2 Combined with the previously solved structure of the cannabinoid receptor CB1 [61], unravelling structure of the CB2 receptor can be highly beneficial for design of Current Opinion in Structural Biology 2019, 55:25–33

selective drugs against neurodegenerative, inflammatory, and fibrotic diseases. The results for this receptor represent an interesting example of limited transferability of stabilizing mutants between closely related GPCR subtypes. Thus, only 2 out of 4 mutations transferred from CB1 crystallization construct (T1273.46A and R2426.32E) showed some stabilizing effect, and it was not enough to crystallize CB2. Out of 36 additional point mutations in CB2 predicted with CompoMug, nine showed thermostabilizing effect (Figure 2d). Adding three of the new point mutations (G782.48L, T1534.45L and G3048.48E) to the crystallization construct eventually allowed determination of the crystal structure of CB2 inactive state complex with a rationally designed antagonist at 2.8 A˚ resolution [62].

Conclusions and perspective Recently developed GPCR family-specific approaches to computational predictions of stabilizing mutations demonstrate practical hit rates (10–30%) and utility in stabilizing and solving the crystal structures of a growing number of diverse GPCRs. The results of the first applications suggest that the knowledge-based, sequencebased, structure-based and machine learning approaches provide valuable synergistic contributions to stabilizing mutations. Many of them, including double mutations and those creating new interhelical interactions, would unlikely to be identified by experimental alanine scanning or direct evolution approaches. In many cases, the computationally predicted mutations can be combined for maximum effect, leading to highly stabilized receptors with as much as 20 C gain in thermostability, and www.sciencedirect.com

Computational design for thermostabilization of GPCRs Popov, Kozlovskii and Katritch 31

improved conformational heterogeneity in specific active or inactive state. We should note, however, that in general, the combined effect from individual mutations is less than a sum of gains from individual point mutants. While the computational tools are already useful in structural biology applications, there is a room for improvements that stem from better data availability. The sequence-based approaches will use more GPCR sequences coming from whole genome sequencing, while structure-based approaches poised to improve accuracy with better availability of structural templates for homology modeling. Most importantly, machine learning approach will greatly benefit from new experimental data of point mutations, including those produced by alanine scanning, direct evolution and validation tests of earlier computational predictions, yielding a greatly expanded and comprehensive dataset for ML model training. New robust and versatile computational tools for mutation predictions will help to expand the repertoire of GPCRs amenable for crystallization, including orphan receptors that are less tractable by experimental approaches. It can also help to design construct with deeper receptor stabilization for use in structure-based drug design or design of robust GPCR-based sensors.

Conflict of interest statement P.P. and V.K. filed a U.S. provisional patent application (serial no. 62/644,008) for CompoMug algorithms.

Acknowledgements We would like to thank several groups at USC, iHuman, SiMM and MIPT for providing experimental testing of computational predictions, especially Raymond Stevens, Vadim Cheresov, Zhi-Jie Liu, Fei Xu, Peng Yao, Shifan Yang, Alexey Mishin, Anastasia Gusach, Anastasia Stepko, Martin Audet, Xiaoting Li, and Tian Hua, as well as GPCR Consortium for funding of GPCR thermostability studies. P.P. acknowledges Russian Science Foundation (RSF) research grant 18-74-00117.

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32 Theory and simulation: demystifying GPCRs

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Computational design for thermostabilization of GPCRs Popov, Kozlovskii and Katritch 33

58. Shibata Y, White JF, Serrano-Vega MJ, Magnani F, Aloia AL, Grisshammer R, Tate CG: Thermostabilization of the neurotensin receptor NTS1. J Mol Biol 2009, 390:262-277.

The study corroborated utility of CompoMug predictions for high-resolution GPCR structure determination even when the thermostability gain cannot be established.

59. Peng Y, McCorvy JD, Harpsoe K, Lansu K, Yuan S, Popov P, Qu L,  Pu M, Che T, Nikolajsen LF et al.: 5-HT2C receptor structures reveal the structural basis of GPCR Polypharmacology. Cell 2018, 172:719-730 e714. One of the first examples of computationally designed mutants employed in structure determination of a GPCR.

61. Hua T, Vemuri K, Pu M, Qu L, Han GW, Wu Y, Zhao S, Shui W, Li S, Korde A: Crystal structure of the human cannabinoid receptor CB1. Cell 2016, 167:750-762.

60. Audet M, White KL, Breton B, Zarzycka B, Han GW, Lu Y, Gati C,  Batyuk A, Popov P, Velasquez J et al.: Crystal structure of misoprostol bound to the labor inducer prostaglandin E2 receptor. Nat Chem Biol 2019, 14(12):6574-6585 http://dx.doi. org/10.1038/s41589-018-0160-y.

www.sciencedirect.com

62. Li X, Hua T, Vemuri K, Ho JH, Wu Y, Wu L, Popov P, Benchama O,  Zvonok N, Locke K et al.: Crystal structure of the human cannabinoid receptor CB2. Cell 2019, 176:459-467. An example of limited transferability between close GPCR subtypes: while two of the stabilizing mutants in CB2 were identical to those used for CB1 structure, additional 3 mutants were needed to solve CB2 structure.

Current Opinion in Structural Biology 2019, 55:25–33