Structural models for dimerization of G-protein coupled receptors: the opioid receptor homodimers

Among the most exciting functional features of G-protein coupled receptors (GPCRs) that are coming into focus lately are those relating to the role and structural characteristics of their oligomerization (mostly homo- and heterodimers). The structural underpinnings of these novel functional insights are still not clear, as current experimental techniques have not yet succeeded in identifying the dimerization interfaces between GPCR monomers. Two computational approaches have recently been designed in our lab to provide reasonable three-dimensional (3D) molecular models of the transmembrane (TM) regions of GPCR dimers based on a combination of the structural information of receptor monomers and analyses of correlated mutations in receptor families. The modeling of GPCR heterodimers has been described recently. We present here a related approach for modeling of GPCR homodimers that identifies the interfaces in the most likely configurations of the complexes. The approach is illustrated for the three cloned opioid receptor subtypes (OPRD, OPRM, and OPRK).     

Status of GPCR modeling and docking as reflected by community-wide GPCR Dock 2010 assessment

The community-wide GPCR Dock assessment is conducted to evaluate the status of molecular modeling and ligand docking for human G protein-coupled receptors. The present round of the assessment was based on the recent structures of dopamine D3 and CXCR4 chemokine receptors bound to small molecule antagonists and CXCR4 with?a synthetic cyclopeptide. Thirty-five groups submitted their receptor-ligand complex structure predictions prior to the release of the crystallographic coordinates. With closely related homology modeling templates, as for dopamine D3 receptor, and with incorporation of biochemical and QSAR data, modern computational techniques predicted complex details with accuracy approaching experimental. In contrast,?CXCR4 complexes that had less-characterized interactions and only distant homology to the known GPCR structures still remained very challenging. The assessment results provide guidance for modeling and crystallographic communities in method development and target selection for further expansion of the structural coverage of the GPCR universe.     

Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?

The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy of in silico models of receptor-ligand complexes that were submitted to a community-wide assessment of GPCR structure prediction (GPCR Dock). Two simulation protocols were used to refine 30 models of the D₃ dopamine receptor (D₃R) in complex with an antagonist. Close to 60 μs of simulation time was generated and the resulting MD refined models were compared to a D₃R crystal structure. In the MD simulations, the receptor models generally drifted further away from the crystal structure conformation. However, MD refinement was able to improve the accuracy of the ligand binding mode. The best refinement protocol improved agreement with the experimentally observed ligand binding mode for a majority of the models. Receptor structures with improved virtual screening performance, which was assessed by molecular docking of ligands and decoys, could also be identified among the MD refined models. Application of weak restraints to the transmembrane helixes in the MD simulations further improved predictions of the ligand binding mode and second extracellular loop. These results provide guidelines for application of MD refinement in prediction of GPCR-ligand complexes and directions for further method development.     

Conformational Transitions upon Ligand Binding: Holo-Structure Prediction from Apo Conformations

Biological function of proteins is frequently associated with the formation of complexes with small-molecule ligands. Experimental structure determination of such complexes at atomic resolution, however, can be time-consuming and costly. Computational methods for structure prediction of protein/ligand complexes, particularly docking, are as yet restricted by their limited consideration of receptor flexibility, rendering them not applicable for predicting protein/ligand complexes if large conformational changes of the receptor upon ligand binding are involved. Accurate receptor models in the ligand-bound state (holo structures), however, are a prerequisite for successful structure-based drug design. Hence, if only an unbound (apo) structure is available distinct from the ligand-bound conformation, structure-based drug design is severely limited. We present a method to predict the structure of protein/ligand complexes based solely on the apo structure, the ligand and the radius of gyration of the holo structure. The method is applied to ten cases in which proteins undergo structural rearrangements of up to 7.1 Å backbone RMSD upon ligand binding. In all cases, receptor models within 1.6 Å backbone RMSD to the target were predicted and close-to-native ligand binding poses were obtained for 8 of 10 cases in the top-ranked complex models. A protocol is presented that is expected to enable structure modeling of protein/ligand complexes and structure-based drug design for cases where crystal structures of ligand-bound conformations are not available.     

Structural biology of G protein‐coupled receptor signaling complexes

G protein‐coupled receptors (GPCRs) constitute the largest family of cell surface receptors that mediate numerous cell signaling pathways, and are targets of more than one‐third of clinical drugs. Thanks to the advancement of novel structural biology technologies, high‐resolution structures of GPCRs in complex with their signaling transducers, including G‐protein and arrestin, have been determined. These 3D complex structures have significantly improved our understanding of the molecular mechanism of GPCR signaling and provided a structural basis for signaling‐biased drug discovery targeting GPCRs. Here we summarize structural studies of GPCR signaling complexes with G protein and arrestin using rhodopsin as a model system, and highlight the key features of GPCR conformational states in biased signaling including the sequence motifs of receptor TM6 that determine selective coupling of G proteins, and the phosphorylation codes of GPCRs for arrestin recruitment. We envision the future of GPCR structural biology not only to solve more high‐resolution complex structures but also to show stepwise GPCR signaling complex assembly and disassembly and dynamic process of GPCR signal transduction.     

Detection of Pairwise Residue Proximity by Covariation Analysis for 3D-Structure Prediction of G-Protein-Coupled Receptors

G-protein-coupled receptor (GPCR) is one of the most important targets for medicines. Homology modeling based on the crystal structure of bovine rhodopsin is currently the most frequently used method for GPCR targeted drug design. Information about residue-residue contacts and the structural specificity in the subfamily is essential for constructing more precise 3D structures, to distinguish the structural differences between the template and targets. In this study, we adopted the covariation analysis to extract information about residue-residue interactions from the amino acid sequence. In the opsin family, a large number of adjacent covarying residue pairs were detected. The detected residue pairs have a strong tendency to gather in some regions important for the structure and function. These results suggest that the covariation analysis is practically utilized to detect adjacent residue pairs and also to apply for predicting functional sites. Analyses of other GPCR subfamilies, olfactory receptor and chemokine receptor families, demonstrated that some adjacent covarying residue pairs were common. Thus, the covariation analysis has possibilities in the substantial improvement of the 3D-structure modeling of GPCRs and in the detection of functional sites such as the ligand-binding sites.     

Increasingly accurate dynamic molecular models of G-protein coupled receptor oligomers: Panacea or Pandora's box for novel drug discovery?

For years, conventional drug design at G-protein coupled receptors (GPCRs) has mainly focused on the inhibition of a single receptor at a usually well-defined ligand-binding site. The recent discovery of more and more physiologically relevant GPCR dimers/oligomers suggests that selectively targeting these complexes or designing small molecules that inhibit receptor–receptor interactions might provide new opportunities for novel drug discovery. To uncover the fundamental mechanisms and dynamics governing GPCR dimerization/oligomerization, it is crucial to understand the dynamic process of receptor–receptor association, and to identify regions that are suitable for selective drug binding. This minireview highlights current progress in the development of increasingly accurate dynamic molecular models of GPCR oligomers based on structural, biochemical, and biophysical information that has recently appeared in the literature. In view of this new information, there has never been a more exciting time for computational research into GPCRs than at present. Information-driven modern molecular models of GPCR complexes are expected to efficiently guide the rational design of GPCR oligomer-specific drugs, possibly allowing researchers to reach for the high-hanging fruits in GPCR drug discovery, i.e. more potent and selective drugs for efficient therapeutic interventions.     

Probing a model of a GPCR/ligand complex in an explicit membrane environment: the human cholecystokinin-1 receptor

A three-dimensional model structure of a complex formed by a G-protein-coupled receptor (GPCR) and an agonist ligand is probed and refined using molecular-dynamics simulations and free energy calculations in a realistic environment. The model of the human receptor of cholecystokinin associated to agonist ligand CCK9 was obtained from a synergistic procedure combining site-directed mutagenesis experiments and in silico modeling. The 31-ns molecular-dynamics simulation in an explicit membrane environment indicates that both the structure of the receptor and its interactions with the ligand are robust. Whereas the secondary structure of the alpha-helix bundle is well preserved, the region of the intracellular loops exhibits a significant flexibility likely to be ascribed to the absence of G-protein subunits in the model. New insight into the structural features of the binding pocket is gained, in particular, the interplay of the ligand with both the receptor and internal water molecules. Water-mediated interactions are shown to participate in the binding, hence, suggesting additional site-directed mutagenesis experiments. Accurate free energy calculations on mutated ligands provide differences in the receptor-ligand binding affinity, thus offering a direct, quantitative comparison to experiment. We propose that this detailed consistency-checking procedure be used as a routine refinement step of in vacuo GPCR models, before further investigation and application to structure-based drug design.     

The conformational transition during G protein–coupled receptor (GPCR) and G protein interaction

The precise structural mechanism of G protein–coupled receptor (GPCR)–G protein coupling has been of significant research interest because it provides fundamental knowledge on cellular signaling and valuable information for GPCR-targeted drug development. Over the last decade, several GPCR–G protein complex structures have been identified. However, these structures are mere snapshots of guanosine diphosphate (GDP)-released stable GPCR–G protein complexes, which have limited the understanding of the allosteric conformational transition during receptor binding to GDP release and the GPCR–G protein coupling selectivity. Recently, deeper insights into the mechanism underlying stepwise conformational changes during GPCR–G protein coupling were obtained using hydrogen/deuterium exchange mass spectrometry, hydroxyl radical footprinting-mass spectrometry, X-ray crystallography, cryoelectron microscopy, and molecular dynamics simulation techniques. This review summarizes these recent developments.     

Homology modeling of dopamine d(2) and d(3) receptors: molecular dynamics refinement and docking evaluation

Dopamine (DA) receptors, a class of G-protein coupled receptors (GPCRs), have been targeted for drug development for the treatment of neurological, psychiatric and ocular disorders. The lack of structural information about GPCRs and their ligand complexes has prompted the development of homology models of these proteins aimed at structure-based drug design. Crystal structure of human dopamine D(3) (hD(3)) receptor has been recently solved. Based on the hD(3) receptor crystal structure we generated dopamine D(2) and D(3) receptor models and refined them with molecular dynamics (MD) protocol. Refined structures, obtained from the MD simulations in membrane environment, were subsequently used in molecular docking studies in order to investigate potential sites of interaction. The structure of hD(3) and hD(2L) receptors was differentiated by means of MD simulations and D(3) selective ligands were discriminated, in terms of binding energy, by docking calculation. Robust correlation of computed and experimental K(i) was obtained for hD(3) and hD(2L) receptor ligands. In conclusion, the present computational approach seems suitable to build and refine structure models of homologous dopamine receptors that may be of value for structure-based drug discovery of selective dopaminergic ligands.     

Homo- and hetero-oligomerization of G protein-coupled receptors

G protein-coupled receptors (GPCRs) form homo-oligomeric and hetero-oligomeric complexes. This understanding has prompted a re-evaluation of many aspects of GPCR biology, however the concept of receptor complexes has not been fully integrated into the current thinking about GPCR structure and function. Nevertheless, receptor oligomerization is a pivotal aspect of the structure and function of GPCRs that has been shown to have implications for receptor trafficking, signaling, and pharmacology and more intricate models for understanding the physiological roles of these receptors are emerging. Here, we summarize some of the advances made in understanding the structural basis and the functional roles of homo- and hetero- oligomerization in this important group of receptors. Although this discussion focuses primarily on the dopamine receptors, particularly the D2 dopamine receptor, and the opioid and serotonin receptors, we discuss the principles governing the oligomerization of all rhodopsin-like GPCRs and potentially of the entire superfamily of these receptors.     

Sequential Application of Ligand and Structure Based Modeling Approaches to Index Chemicals for Their hH4R Antagonism

The human histamine H₄ receptor (hH₄R), a member of the G-protein coupled receptors (GPCR) family, is an increasingly attractive drug target. It plays a key role in many cell pathways and many hH₄R ligands are studied for the treatment of several inflammatory, allergic and autoimmune disorders, as well as for analgesic activity. Due to the challenging difficulties in the experimental elucidation of hH₄R structure, virtual screening campaigns are normally run on homology based models. However, a wealth of information about the chemical properties of GPCR ligands has also accumulated over the last few years and an appropriate combination of these ligand-based knowledge with structure-based molecular modeling studies emerges as a promising strategy for computer-assisted drug design. Here, two chemoinformatics techniques, the Intelligent Learning Engine (ILE) and Iterative Stochastic Elimination (ISE) approach, were used to index chemicals for their hH₄R bioactivity. An application of the prediction model on external test set composed of more than 160 hH₄R antagonists picked from the chEMBL database gave enrichment factor of 16.4. A virtual high throughput screening on ZINC database was carried out, picking ∼4000 chemicals highly indexed as H₄R antagonists'' candidates. Next, a series of 3D models of hH₄R were generated by molecular modeling and molecular dynamics simulations performed in fully atomistic lipid membranes. The efficacy of the hH₄R 3D models in discrimination between actives and non-actives were checked and the 3D model with the best performance was chosen for further docking studies performed on the focused library. The output of these docking studies was a consensus library of 11 highly active scored drug candidates. Our findings suggest that a sequential combination of ligand-based chemoinformatics approaches with structure-based ones has the potential to improve the success rate in discovering new biologically active GPCR drugs and increase the enrichment factors in a synergistic manner.     

Open conformation of adipokinetic hormone receptor from the malaria mosquito facilitates hormone binding

Insect flight requires rapid mobilization of energy reserves during flight, which is mediated and regulated by hormonal control via adipokinetic hormones. The structure of the G-protein receptors to which these hormones bind, are crucial in understanding many of the physiological processes in which they play a central role. To date no 3D structure of an insect G-protein coupled receptor (GPCR) is available. Here, the first models of the 3D structures of a GPCR from the malaria mosquito are presented. Homology modeling of the receptor identified from the genome of Anopheles gambiae was used to construct two models of the receptor. The 7 transmembrane helical bundles of these two models are based on the crystal structures of beta2-adrenergic receptor and rhodopsin. The flexible loop regions were modeled using high temperature simulated annealing and constrained molecular dynamic simulations. The two receptor models differ in a number of critical features, the most important of which is that the rhodopsin-based model has a ‘closed’ structure while the beta2-based structure is ‘open’. The ‘open’ conformation provides easy access of the hormone to the binding pocket. Docking calculations with the insect adipokinetic hormones, AKH-1 (pGlu-Leu-Thr-Phe-Thr-Pro-Ala-Trp-NH₂) from the malaria mosquito and Del-CC (pGlu-Lys-Asn-Phe-Ser-Pro-Asn-Trp-Gly-Asn-NH₂) from the blister beetle showed that while the binding motif of the two is similar, AKH-1 has more than 30 times higher affinity than Del-CC, which strongly suggests that the binding is specific, and that the correct binding site was identified. Using these models it is possible to design antagonists, which block the binding site and are thus species-specific insecticides.     

Novel approach to computer modeling of seven-helical transmembrane proteins: current progress in the test case of bacteriorhodopsin.

G-protein coupled receptors (GPCRs) are thought to be proteins with 7-membered transmembrane helical bundles (7TM proteins). Recently, the X-ray structures have been solved for two such proteins, namely for bacteriorhodopsin (BR) and rhodopsin (Rh), the latter being a GPCR. Despite similarities, the structures are different enough to suggest that 3D models for different GPCRs cannot be obtained directly employing 3D structures of BR or Rh as a unique template. The approach to computer modeling of 7TM proteins developed in this work was capable of reproducing the experimental X-ray structure of BR with great accuracy. A combination of helical packing and low-energy conformers for loops most close to the X-ray structure possesses the r.m.s.d. value of 3.13 A. Such a level of accuracy for the 3D-structure prediction for a 216-residue protein has not been achieved, so far, by any available ab initio procedure of protein folding. The approach may produce also other energetically consistent combinations of helical bundles and loop conformers, creating a variety of possible templates for 3D structures of 7TM proteins, including GPCRs. These templates may provide experimentalists with various plausible options for 3D structure of a given GPCR; in our view, only experiments will determine the final choice of the most reasonable 3D template.     

Assessment of the transmembrane domain structures in GPCR Dock 2013 models

The community-wide blind prediction of G-protein coupled receptor (GPCR) structures and ligand docking has been conducted three times and the quality of the models was primarily assessed by the accuracy of ligand binding modes. The seven transmembrane (TM) helices of the receptors were taken as a whole; thus the model quality within the 7TM domains has not been evaluated. Here we evaluate the 7TM domain structures in the models submitted for the last round of prediction – GPCR Dock 2013. Applying the 7?×?7 RMSD matrix analysis described in our prior work, we show that the models vary widely in prediction accuracy of the 7TM structures, exhibiting diverse structural differences from the targets. For the prediction of the 5-hydroxytryptamine receptors, the top 7TM models are rather close to the targets, which however are not ranked top by ligand-docking. On the other hand, notable deviations of the TMs are found in in the previously identified top docking models that closely resemble other receptors. We further reveal reasons of success and failure in ligand docking for the models. This current assessment not only complements the previous assessment, but also provides important insights into the current status of GPCR modeling and ligand docking.     

Critical analysis of the successes and failures of homology models of G protein‐coupled receptors

We present a critical assessment of the performance of our homology model refinement method for G protein‐coupled receptors (GPCRs), called LITICon that led to top ranking structures in a recent structure prediction assessment GPCRDOCK2010. GPCRs form the largest class of drug targets for which only a few crystal structures are currently available. Therefore, accurate homology models are essential for drug design in these receptors. We submitted five models each for human chemokine CXCR4 (bound to small molecule IT1t and peptide CVX15) and dopamine D3DR (bound to small molecule eticlopride) before the crystal structures were published. Our models in both CXCR4/IT1t and D3/eticlopride assessments were ranked first and second, respectively, by ligand RMSD to the crystal structures. For both receptors, we developed two types of protein models: homology models based on known GPCR crystal structures, and ab initio models based on the prediction method MembStruk. The homology‐based models compared better to the crystal structures than the ab initio models. However, a robust refinement procedure for obtaining high accuracy structures is needed. We demonstrate that optimization of the helical tilt, rotation, and translation is vital for GPCR homology model refinement. As a proof of concept, our in‐house refinement program LITiCon captured the distinct orientation of TM2 in CXCR4, which differs from that of adrenoreceptors. These findings would be critical for refining GPCR homology models in future. Proteins 2013. © 2012 Wiley Periodicals, Inc.     

Structure modeling of all identified G protein-coupled receptors in the human genome

G protein-coupled receptors (GPCRs), encoded by about 5% of human genes, comprise the largest family of integral membrane proteins and act as cell surface receptors responsible for the transduction of endogenous signal into a cellular response. Although tertiary structural information is crucial for function annotation and drug design, there are few experimentally determined GPCR structures. To address this issue, we employ the recently developed threading assembly refinement (TASSER) method to generate structure predictions for all 907 putative GPCRs in the human genome. Unlike traditional homology modeling approaches, TASSER modeling does not require solved homologous template structures; moreover, it often refines the structures closer to native. These features are essential for the comprehensive modeling of all human GPCRs when close homologous templates are absent. Based on a benchmarked confidence score, approximately 820 predicted models should have the correct folds. The majority of GPCR models share the characteristic seven-transmembrane helix topology, but 45 ORFs are predicted to have different structures. This is due to GPCR fragments that are predominantly from extracellular or intracellular domains as well as database annotation errors. Our preliminary validation includes the automated modeling of bovine rhodopsin, the only solved GPCR in the Protein Data Bank. With homologous templates excluded, the final model built by TASSER has a global C(alpha) root-mean-squared deviation from native of 4.6 angstroms, with a root-mean-squared deviation in the transmembrane helix region of 2.1 angstroms. Models of several representative GPCRs are compared with mutagenesis and affinity labeling data, and consistent agreement is demonstrated. Structure clustering of the predicted models shows that GPCRs with similar structures tend to belong to a similar functional class even when their sequences are diverse. These results demonstrate the usefulness and robustness of the in silico models for GPCR functional analysis. All predicted GPCR models are freely available for noncommercial users on our Web site (http://www.bioinformatics.buffalo.edu/GPCR).     

Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14

Substantial progresses in protein structure prediction have been made by utilizing deep-learning and residue-residue distance prediction since CASP13. Inspired by the advances, we improve our CASP14 MULTICOM protein structure prediction system by incorporating three new components: (a) a new deep learning-based protein inter-residue distance predictor to improve template-free (ab initio) tertiary structure prediction, (b) an enhanced template-based tertiary structure prediction method, and (c) distance-based model quality assessment methods empowered by deep learning. In the 2020 CASP14 experiment, MULTICOM predictor was ranked seventh out of 146 predictors in tertiary structure prediction and ranked third out of 136 predictors in inter-domain structure prediction. The results demonstrate that the template-free modeling based on deep learning and residue-residue distance prediction can predict the correct topology for almost all template-based modeling targets and a majority of hard targets (template-free targets or targets whose templates cannot be recognized), which is a significant improvement over the CASP13 MULTICOM predictor. Moreover, the template-free modeling performs better than the template-based modeling on not only hard targets but also the targets that have homologous templates. The performance of the template-free modeling largely depends on the accuracy of distance prediction closely related to the quality of multiple sequence alignments. The structural model quality assessment works well on targets for which enough good models can be predicted, but it may perform poorly when only a few good models are predicted for a hard target and the distribution of model quality scores is highly skewed. MULTICOM is available at https://github.com/jianlin-cheng/MULTICOM_Human_CASP14/tree/CASP14_DeepRank3 and https://github.com/multicom-toolbox/multicom/tree/multicom_v2.0 .     

Comparative Sequence and Structural Analyses of G-Protein-Coupled Receptor Crystal Structures and Implications for Molecular Models

Background

Up until recently the only available experimental (high resolution) structure of a G-protein-coupled receptor (GPCR) was that of bovine rhodopsin. In the past few years the determination of GPCR structures has accelerated with three new receptors, as well as squid rhodopsin, being successfully crystallized. All share a common molecular architecture of seven transmembrane helices and can therefore serve as templates for building molecular models of homologous GPCRs. However, despite the common general architecture of these structures key differences do exist between them. The choice of which experimental GPCR structure(s) to use for building a comparative model of a particular GPCR is unclear and without detailed structural and sequence analyses, could be arbitrary. The aim of this study is therefore to perform a systematic and detailed analysis of sequence-structure relationships of known GPCR structures.

Methodology

We analyzed in detail conserved and unique sequence motifs and structural features in experimentally-determined GPCR structures. Deeper insight into specific and important structural features of GPCRs as well as valuable information for template selection has been gained. Using key features a workflow has been formulated for identifying the most appropriate template(s) for building homology models of GPCRs of unknown structure. This workflow was applied to a set of 14 human family A GPCRs suggesting for each the most appropriate template(s) for building a comparative molecular model.

Conclusions

The available crystal structures represent only a subset of all possible structural variation in family A GPCRs. Some GPCRs have structural features that are distributed over different crystal structures or which are not present in the templates suggesting that homology models should be built using multiple templates. This study provides a systematic analysis of GPCR crystal structures and a consistent method for identifying suitable templates for GPCR homology modelling that will help to produce more reliable three-dimensional models.     

Model of three-dimensional structure of vitamin D receptor and its binding mechanism with 1alpha,25-dihydroxyvitamin D(3).

Comparative modeling of the vitamin D receptor three-dimensional structure and computational docking of 1alpha,25-dihydroxyvitamin D(3) into the putative binding pocket of the two deletion mutant receptors: (207-423) and (120-422, Delta [164-207]) are reported and evaluated in the context of extensive mutagenic analysis and crystal structure of holo hVDR deletion protein published recently. The obtained molecular model agrees well with the experimentally determined structure. Six different conformers of 1alpha,25-dihydroxyvitamin D(3) were used to study flexible docking to the receptor. On the basis of values of conformational energy of various complexes and their consistency with functional activity, it appears that 1alpha,25-dihydroxyvitamin D(3) binds the receptor in its 6-s-trans form. The two lowest energy complexes obtained from docking the hormone into the deletion protein (207-423) differ in conformation of ring A and orientation of the ligand molecule in the VDR pocket. 1alpha,25-Dihydroxyvitamin D(3) possessing the A-ring conformation with axially oriented 1alpha-hydroxy group binds receptor with its 25-hydroxy substituent oriented toward the center of the receptor cavity, whereas ligand possessing equatorial conformation of 1alpha-hydroxy enters the pocket with A ring directed inward. The latter conformation and orientation of the ligand is consistent with the crystal structure of hVDR deletion mutant (118-425, Delta [165-215]). The lattice model of rVDR (120-422, Delta [164-207]) shows excellent agreement with the crystal structure of the hVDR mutant. The complex obtained from docking the hormone into the receptor has lower energy than complexes for which homology modeling was used. Thus, a simple model of vitamin D receptor with the first two helices deleted can be potentially useful for designing a general structure of ligand, whereas the advanced lattice model is suitable for examining binding sites in the pocket.