Comparative modeling is a powerful technique to generate models of proteins from families already represented by members with experimentally characterized three-dimensional structures. The method is particularly important for modeling membrane-bound receptors in the G Protein-Coupled Receptor (GPCR) family, such as many of the lipid receptors (such as the cannabinoid, prostanoid, lysophosphatidic acid, sphin-gosine 1-phosphate, and eicosanoid receptor family members), as these represent particularly challenging targets for experimental structural characterization methods. Although challenging modeling targets, these receptors have been linked to therapeutic indications that vary from nociception to cancer, and thus are of interest as therapeutic targets. Accurate models of lipid receptors are therefore valuable tools in the drug discovery and optimization phases of therapeutic development. This chapter describes the construction and evaluation of comparative structural models of lipid receptors beginning with the selection of template structures.
Comparative modeling is a technique to construct a tertiary struc-ture model of a target protein from its primary structure on the basis of anticipated structural similarity to a template protein ter-tiary structure that shares substantial primary structural and func-tional identity with the target ( 1 ) . Comparative modeling (also called homology modeling) depends on the theoretical relationship expected between the three-dimensional structures of proteins that have evolved from a common ancestor protein while retaining sub-stantial similarity in both amino acid sequence and overall function. It is expected that any changes to the amino acid sequence that alter three-dimensional structure in a dramatic fashion would concomi-tantly alter protein function. Comparative modeling became widely applicable to members of the G Protein-Coupled Receptor (GPCR)
1. Introduction
208 A.L. Parrill
family, which includes numerous receptors speci fi c for lipids, when the crystallographic structure of bovine rhodopsin was published in 2000 ( 2 ) . Since that time, the comparative modeling strategy has been used to construct predictive models of numerous lipid recep-tors that have provided insights into lipid recognition ( 3– 13 ) and guided the discovery of antagonists that effectively block lipid-stim-ulated receptor functions ( 14– 17 ) .
The GPCR superfamily, particularly the class A or rhodopsin-like subfamily, includes numerous members that signal in response to lipids. The cannabinoid receptors (CB1 and CB2) signal in response to natural endocannabinoids such as anandamide and have long been of interest as therapeutic targets for analgesics ( 18 ) , and have recently gained interest as targets in the treatment of neurodegenerative and neuroin fl ammatory diseases such as Alzheimer’s disease ( 19 ) . A second widely studied subfamily of lipid receptors are those responsive to two phospholipids, lysophos-phatidic acid (LPA) and sphingosine 1-phosphate (S1P) ( 20, 21 ) . Receptors responsive to LPA or S1P have generated substantial interest as therapeutic targets for cancer ( 22, 23 ) , neuropathic pain ( 24– 27 ) , and multiple sclerosis ( 28– 30 ) . In particular, fi ngolimod (FTY-720), which is phosphorylated in vivo by sphingosine kinase to its bioactive form, has recently been approved for the treatment of relapsing multiple sclerosis through its action at the S1P 1 recep-tor ( 31 ) . Additional bioactive lipids with signaling pathways involv-ing GPCR, such as resolvin E1 and the eoxins are being reported on a regular basis ( 21 ) , suggesting that modeling studies of lipid receptors will be of value for some time to drive the discovery of both therapeutics and pharmacological probes that can aid in elucidating the functional roles of lipid receptors.
Comparative modeling methods typically construct the tertiary structure of a target protein using the exact positions of identical amino acids in the template structure, and backbone atom posi-tions of differing amino acids in the template structure. Amino acids that align against gaps in the target (template insertions) or in the template (template deletions) are modeled based on protein fragments of appropriate length that overlap on the backbone atom positions surrounding the insertion or deletion ( 32 ) . Side-chain conformations for amino acids differing between the template and target are then added from a library of side-chain rotamers to opti-mize packing. It is often necessary to produce multiple models differing in the inserted loop structures and side-chain rotamers in order to select good models for subsequent studies. In this chapter, the method to construct and analyze a tertiary structural model for lipid receptors is presented, with particular focus on the rationale to be applied for template and model selection. Although some of the notes are speci fi c to lipid receptors, the majority of the method described is applicable not only to lipid-responsive GPCR but to other classes of proteins as well.
20912 Comparative Modeling of Lipid Receptors
1. Obtain the primary structure of your target protein from GenBank ( http://www.ncbi.nlm.nih.gov/genbank ) in FASTA format (see Note 1).
2. Obtain the tertiary structure of your template protein from the Protein Data Bank ( 33 ) ( http://www.rcsb.org ) in PDB format (see Note 2).
Numerous software packages and online servers can be used to accomplish the alignment and comparative modeling steps described in the method; however, the description is based on options implemented in the MOE software package from the Chemical Computing Group (version 2009.10, Montreal, Canada). Suitable alternatives are described in Note 3.
Comparative models of GPCR are often (but not always) relaxed using geometry optimization and molecular dynamics using explicit solvated bilayer environments. Careful environmental modeling is essential if dynamic events such as lipid entry into the binding pocket from the surrounding bilayer as examined for the cannabinoid recep-tor ( 11 ) are of interest. The impact of the solvated membrane on the overall structure of lipid receptors implicitly carries through the comparative modeling process from the template structures, which were crystallized from a variety of lipid phases. Therefore, the value of modeling the membrane explicitly when using the comparative models as docking targets is not as high as for studies of dynamic events. The construction and simulation of such fully solvated and membrane-embedded systems is not discussed here.
Hardware requirements for alignment and comparative modeling depend on the software selected. The MOE software runs on a variety of hardware from desktop PCs and laptops to high-perfor-mance linux computing clusters. The sequence alignment and comparative modeling functions do not require specialized com-puters or operating systems, and run quickly on typical desktop computing con fi gurations. Likewise, the free tools available through numerous servers can also be utilized from any desktop computer. The overall fl ow of activities involved in comparative modeling of lipid receptors is shown in Fig. 1 .
1. Open three-dimensional structures of template protein struc-tures appropriate for modeling either inactive receptor confor-mations (currently b 2 adrenergic receptor ( b 2, PDB ( 33 ) )
entry 2RH1 ( 34 ) , see Note 4), rhodopsin (PDB ( 33 ) entry 1U19 ( 35 ) , chain A), b 1 adrenergic receptor ( b 1, PDB ( 33 ) entry 2VT4 ( 36 ) , chain B), and adenosine A2A receptor (A2A, PDB ( 33 ) entry 3EML ( 37 ) , see Note 4)) or active receptor conformations (currently b 2 adrenergic receptor ( b 2, PDB ( 33 ) entries 3P0G and 3PDS ( 38, 39 ) ), opsin (PDB ( 33 ) entry 3DQB ( 40 ) , chain A), and a theoretical rhodopsin model (PDB ( 33 ) entry 1BOJ ( 41 ) ) (see Note 2)).
2. Open sequences of the target protein and closely related family members (see Note 1).
3. Perform a group-to-group alignment de fi ning the template sequences in one group and the target sequence (and its close relatives) in the other (see Note 1 for details on performing group-to-group alignments in both the MOE software and using the free tools in the Biology Workbench server).
4. Manually close alignment gaps in helical segments 1–7, ensur-ing that the most conserved positions in each helical segment ( 42 ) are aligned (see Note 5). Alignments in the MOE soft-ware can be manually altered by right-clicking over a residue in the sequence window and pulling it to the desired position in the alignment.
5. One hallmark of the lipid receptor sequences that differentiates them from the currently available template structure sequences is the lack of the conserved proline residue in the fi fth trans-membrane domain (TM5 residue P5.50), as indicated in Note 5. The lack of proline in TM5 can lead to errors in alignment that have profound impact on the quality of comparative models of lipid receptors. As an example, comparative models of the LPA and S1P receptors were used for several years that were able to
Fig 1. Flowchart of lipid receptor comparative modeling process.
21112 Comparative Modeling of Lipid Receptors
identify residues playing key roles in agonist recognition and selectivity ( 3– 8, 14, 43 ) that later had to be modi fi ed due to misalignment of the fi fth transmembrane domain against the template during the model-building process ( 9 ) . The alignment was adjusted based on the observation that the initial model was consistent with experimental mutagenesis results for all muta-tion sites except those in the fi fth TM. Consistency between a modi fi ed model and the mutations to residues in TM5 became very good when the sequence alignment was manually adjusted by one residue, although the overall alignment score based on standard amino acid substitution matrices decreased slightly. Several different alignments of TM5 could be used to generate sets of candidate models for evaluation when modeling new classes of lipid receptors for which limited experimental data on the roles of residues in TM5 are available.
1. Build homology models (see Note 6) of the target sequence based on the most closely related template sequence or the template sequence that best represents the activation state of interest for the target receptor. For example, if the structural model is being generated to fi nd new agonists it is imperative to use the crystal structure of an active state of the GPCR as template rather than its inactive state. It is fairly typical for cases with high sequence identity between target and template struc-ture (>50 %) to build relatively few models (on the order of 10) due to the high level of con fi dence that the target struc-ture closely resembles the template structure, and for cases with lower sequence identity to build a greater number of models. These models will differ in side-chain rotamers at unconserved positions, as well as backbone structure and side-chain rotamers of inserted loops.
2. Critically evaluate the resulting models. Tools that can be used include Ramachandran plots (ideally few j / y combinations will fall in disallowed regions of the Ramachandran plot) and contact reports (hydrogen bond networks involving residues at polar conserved positions ( 44 ) should be present in a good model). Information from the literature such as mutagenesis or spectroscopic studies, when available, can also be used to select better models from among the complete set produced by homology modeling. For example, in the S1P receptors, muta-genesis studies have been performed that demonstrate impor-tance of residues in the third, fi fth, and seventh transmembrane (TM) segments in S1P-induced receptor activation ( 5, 6, 9, 45 ) . If the essential residues in these domains do not cluster to form a candidate binding pocket in some of the generated models, those models might be eliminated from consideration. Spectroscopic studies are also emerging that indicate a shift between hydrophobic and polar environments as a function of
3.2. Model Generation
212 A.L. Parrill
activation state for particular residues ( 46, 47 ) . Placement of an amino acid side chain expected to reside in a polar environment in the activation state of interest outward toward the surround-ing lipid would be suf fi cient reason to eliminate a candidate model based on comparison to spectroscopic results. The num-ber of candidate models can usually be limited to 5–10 using a combination of experimental results from the literature and computational measures of model quality.
3. Carefully evaluate candidate disul fi de bonds. Many of the lipid receptors lack the cysteine residue at the top of TM3 that forms a disul fi de bond stabilizing the second extracellular loop geom-etry in many of the currently available template structures. In general, if the cysteine residues involved in forming disul fi de bonds in a carefully selected template structure are conserved in the target sequence, these disul fi de bonds should be formed in the models of the target protein. If the cysteine residues involved in disul fi de bonds in the template structure are not conserved in the template, disul fi de bonds should only be formed on the basis of experimental information. In prior stud-ies of both LPA and S1P receptors ( 3, 5, 6, 8– 10, 14– 16, 45 ) , our group has chosen not to include any disul fi de bonds due to poor conservation of cysteine residues observed to form disul fi de bonds in the crystallized GPCR examples combined with a lack of experimental evidence of which alternate disul fi de bonds might occur in these lipid receptors.
4. Determine the importance of alternative loop modeling strate-gies. In the event that binding of agonists/antagonists is expected to involve interactions with residues in the extracel-lular loops, the comparative model(s) can serve as a starting point for loop optimization methods. Numerous options can be used to generate alternative loop models for comparison. One example is to use experimentally characterized segment models from lipid receptors characterized by NMR spectros-copy. Segment structures are available in the PDB for the third intracellular loop (IL3) of the CB1 receptor ( 48, 49 ) and the fi rst extracellular loop (EL1) of the S1P 4 receptor ( 50 ) . These segments could be used as comparative modeling templates for the corresponding segments of another target lipid receptor using the methods described in steps 1– 3 . Alternatively, loop modeling techniques such as those available in MODELLER through the more user-friendly SWIFT MODELLER inter-face described at http://bitmesra.ac.in/swift-modeller/swift.htm can be utilized. Such methods can be used to develop alternative models of particular loops, such as the second extra-cellular loop, which is often quite different in both sequence and length between different members of the class A GPCR, by entering the amino acid sequence range and number of
models to be generated. Step 3 would then be repeated to analyze the resulting loop models to select models to be used in subsequent modeling activities.
5. Use the best model (or subset of models) as a starting point for other investigations. Comparative models can be used to inves-tigate complexes formed between the lipid receptor and ago-nists/antagonists (a good tutorial on docking has been published by Muegge and Rarey ( 51 ) ), as targets for in silico screening to identify novel agonists/antagonists, and as start-ing points to investigate the membrane-embedded lipid recep-tor structure and function. Many of these subsequent studies can be used to provide important validation and feedback into the modeling process as shown in Fig. 1 and should be used to guide model optimization studies.
1. Target lipid receptors that are part of a closely related subfamily of GPCR (such as the cannabinoid receptor subfamily with two human homologs, CB1 and CB2) may align better against the template primary sequences using a group-to-group align-ment strategy. In the MOE software package, this can be done by choosing the Align option from the Homology menu in the sequence window, selecting the chains of the subfamily sequences, activating the Partition option at the top of the dia-log box, and then clicking the OK button. A similar group-to-group alignment strategy can be performed using the free tools in the Biology Workbench server ( http://workbench.sdsc.edu ) by generating separate alignments for the families, and then choosing the CLUSTALWPROF option (Align Two Existing Alignments) option from the Alignment Tools menu. Therefore, download primary sequences for all closely related members of the target receptor subfamily.
2. As of April 2011, there are over 20 atomic-resolution ( 2, 34– 40, 52– 62 ) crystallographic structures of six different class A GPCR (rhodopsin, b 1 adrenergic, b 2-adrenergic, adenosine A2a receptors, dopamine D3, and CXCR4 chemokine) from which to choose a template for modeling a lipid receptor. The Stephen White laboratory at UC Irvine maintains a Web page ( http://blanco.biomol.uci.edu/Membrane_Proteins_xtal.html ) that tabulates membrane proteins of known structure by categories including G Protein-Coupled Receptors and is a good place to check for additional available structural tem-plates. The GPCR network also maintains a Web site that tracks their progress toward crystallization of numerous GPCR targets, with PDB references provided for their successfully
characterized targets ( http://cmpd.scripps.edu/tracking_s tatus.htm ), often available prior to publication. The group-to-group alignment mentioned in Note 1 may bene fi t from using at least one crystal structure of each unique GPCR available.
3. Numerous servers and downloadable software packages have been developed that provide the functionality described in the process of building comparative models of lipid receptors. The alternatives identi fi ed here are those that can be used free of charge by academic researchers. A variety of other excellent software options are available with the purchase of licenses, but these options are not identi fi ed in these notes. Sequence align-ments can be performed using the Biology Workbench at the San Diego Supercomputer Center ( http://workbench.sdsc.edu/ ). Common alignment tools available through this server include ClustalW, which is also available through other servers. Comparative models can be developed using Swiss-Model ( 63, 64 ) ( http://swissmodel.expasy.org/ ), MODELLER ( 65 ) ( http://www.salilab.org/modeller/ ), and the BioInfoBank Metaserver ( http://meta.bioinfo.pl/submit_wizard.pl ) cou-pled with 3D Jury ( 66 ) consensus analysis. While each is capa-ble of generating quality models, a few details on each are provided here for comparative purposes. The SwissModel server provides options for both automatic modeling from a sequence as well as an alignment mode in which the user pro-vides a sequence alignment of the target to the template. The automatic modeling option may be of particular interest for investigators new to comparative modeling as a useful bench-mark for models generated using more user-guided methods. MODELLER provides additional specialized features includ-ing de novo modeling of loops connecting the helical segments of GPCR. The added loop modeling feature would be of par-ticular interest for lipid GPCR due to the high variability observed so far in loop structures among the six class A GPCR crystallized and the low sequence identity between the loop regions of the lipid GPCR and the currently available template structures. More computational expertise is required, however, as the software must be downloaded and installed in contrast to SwissModel which operates from a Web page interface. The BioInfoBank MetaServer can be used to combine the ease of use provided by a Web server with the advanced functions of many different comparative modeling and pro fi le-based align-ment tools. The advantage of the MetaServer is its use of mul-tiple servers to construct comparative models, followed by extensive comparative analysis of the resulting models. One of the servers used by the MetaServer, ESyPred3D, actually uses the MODELLER package to construct its models.
4. The 2RH1 ( b 2), 3EML (A2A), 3OCU (CXCR4, also entries 3OE8, 3OE0, 3)E6, and 3OE0, and 3PBL (dopamine D3)
structures include residues ASN1002-ALA1160, ASN1002-TYR1161, ASN1002-SER1201, or ASN1002-TYR1161, respectively, from T4 lysozyme in place of the third intracellular loop residues ( 37, 67 ) . These residues should be deleted from all structures as they do not re fl ect the correct structure or sequence necessary for the alignment and model building steps.
5. Class A GPCR exhibit very high conservation of residues in each helical segment, a feature that has been used to describe an index-based numbering system useful for comparing different GPCR family members. ( 42 ) These residues are conserved for both structural and functional reasons, and their alignment between the template and target should be carefully veri fi ed prior to model construction. Positions of these helical index residues in the 1U19 structure are N55 (N1.50), D83 (D2.50), R135 (R3.50), W161 (W4.50), P214 (P5.50), P267 (P6.50), and P303 (P7.50). It is common for lipid receptors not to show conservation of P5.50 and alignment within helix 5 will have to be guided based on other aspects of the sequence alignment.
6. It is important in this step to ensure that the potential energy function used in scoring and minimizing intermediate homol-ogy/comparative models does not impose an aqueous solvation environment (either explicitly or implicitly). This can be ensured by setting exterior dielectric values to 3 or by neglecting solva-tion entirely. This will ensure that the greatest errors in the con-tributions to the relative energies will be in the extracellular and intracellular loops, where the weakest homology and greatest errors are inherently localized. It is also important in the absence of bound ligands as induced fi t environments not to minimize the energy of the intermediate models to very low root mean square gradient values (no lower than 0.1 kcal/mol Å is recom-mended) as any open pockets within the helical bundle may collapse to promote greater interactions across the pocket.
Acknowledgment
This work was supported by NIH grant HL 084007.
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