Jie Yang, Sen Li, Tong-Yang Zhu, Xiao-Ning Wang, Zhen Zhang
State Key Laboratory of Pharmaceutical Biotechnology, College of Life Sciences, Nanjing University, Nanjing, China;.
DOI: 10.4236/ns.2012.412A139   PDF    HTML   XML   4,489 Downloads   7,130 Views   Citations

Abstract

Target identification is a critical step following the discovery of small molecules that elicit a biological phenotype. G-protein coupled recaptors (GPCRs) are among the most important drug targets for the pharmaceutical industry. The present work seeks to provide an in silico model of known GPCR protein fishing technologies in order to rapidly fish out potential drug targets on the basis of amino acid sequences and seven transmembrane regions (TMs) of GPCRs. Some scoring matrices were trained on 22 groups of GPCRs in the GPCRDB database. These models were employed to predict the GPCR proteins in two groups of test sets. On average, the mean correct rate of each TM of 38 GPCRs from two test sets (S^T₂₃ and S^T₂₄) was found 62% and 57.5%, respectively, using training set 18 (S^L_D18); the mean hit rate of each TM of 38 GPCRs from S^T₂₃ and S^T₂₄ was found 68.1% and 64.7%, respectively. Based on the scoring matrices of PreMod, the mean correct rate of each TM of GPCRs from S^T₂₃ and S^T₂₄ was found 62% and 62.04%, respectively; the mean hit rate of each TM of GPCRs from S^T₂₃ and S^T₂₄ was found 67.7% and 68.0%, respecttively. The means of GPCRs in S^T₂₃ based on S^L_D18 is close to those based on PreMod; whereas the means of GPCRs in S^T₂₄ based on S^L_D18 is less than those based on PreMod. Moreover, the accuracy (“2”) and validity (“2 + 1”) rates of prediction all seven TMs of 38 GPCRs by the scoring matrices of PreMod are more than those by S^L_D18, S^L_A14 and S^L_A3; whereas the hit rates (94.74% and 97.37%) by PreMod are less than those of S^L_A3 but bigger than those of S^L_D18 and S^L_A14, respectively. This is the reason that we choose PreMod to predict some potential drug targets. 22 GPCR proteins in the sense chain of chromosome 19 constructing validation set were predicted and validated by PreMod whose hit rate is up to 90.91%. Further evaluation is under investigation.

Keywords

Pharmaceutical Targets for Drug Development; G-Protein Coupled Receptors; Scoring Matrices; Hit Rates

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1. INTRODUCTION

G-protein coupled receptors (GPCRs) are among the most important drug targets for the pharmaceutical industry [1]. More than 30% of all marketed therapeutics interacts with them. GPCRs are integral membrane proteins that possess seven membrane-spanning domain or transmembrane helices with the N terminal of these proteins located in extracellular and the C-terminal extended in the cytoplasm. They comprise a large protein family of transmembrane receptors that sense molecules outside the cell and activate inside signal transduction pathways and, ultimately, cellular responses. The heterotrimeric G proteins (guanine nucleotide-binding proteins) are signal transducers, attached to the cell surface plasma membrane, that connect receptors to effectors and thus to intracellular signaling pathways [2,3]. The extracellular signals are received by GPCRs that activate the G proteins, which communicate signals from many hormones, neurotransmitters, chemokines, and autocrine and paracrine factors by several distinct intracellular signaling pathways [2]. These pathways interact with one another to form a network that regulates metabolic enzymes, ion channels, transporters, and other components of the cellular machinery controlling a broad range of cellular processes, including transcription, motility, contractility, and secretion. These cellular processes in turn regulate systemic functions such as embryonic development, gonadal development, learning and memory, and organismal homeostasis [2]. G protein-dependent and G protein-independent pathways each have the capacity to initiate numerous intracellular signaling cascades to mediate these effects [4]. G proteins are GTPases (guanosine triphosphatases) that cycle between a GDP-bound form and a GTP-bound form [5]. The GTP-bound G protein is an active form that interacts with downstream effectors and transmits signals, during which the bound GTP is often hydrolyzed to GDP and the G protein recycles into the inactive GDP-bound form [5]. The heterotrimeric G protein complex comprises a Gα subunit, of which there are 4 main families (Gαs, Gαi/o, Gαq/11, and Gα12/13), coupled to a combination of Gβ and Gγ subunits, of which there exist 6 and 12 members, respecttively [2,4]. Gα subunit binds to guanine nucleotides while Gβγ subunits cannot be dissociated under nondenaturing conditions. The activity of G proteins is regulated mainly through three classes of regulatory proteins: GTPase-activating proteins (GAPs), guanine nucleotideexchange factors (GEFs), and guanine nucleotide-dissociation inhibitors (GDIs) [6]. Upon activation, the GTPbound Gα subunit dissociates from Gβγ subunits, and serves as the major signaling messenger by interacting with its signal acceptors (downstream effectors) [2].

Mammalian GPCRs constitute a superfamily of diverse proteins with hundreds of members [7,8]. GPCRs can be grouped into 6 classes based on sequence homology and functional similarity [9,10]: Class A (Rhodopsin-like receptors) [11], Class B (Secretin receptor family) [12], Class C (Metabotropic glutamate/pheromone receptors) [13], Class D (Fungal mating pheromone recaptors) [14], Class E (Cyclic AMP receptors) [15], and Class F (Frizzled/Smoothened, F/S) [16,17]. GPCRs act as receptors for a multitude of different signals [8]. One major group, referred to as chemosensory GPCRs (csGPCRs), is receptors for sensory signals of external origin that are sensed as odors [18,19], pheromones, or tastes [20]. Most other GPCRs respond to endogenous signals, such as peptides, lipids, neurotransmitters, or nucleotides [21,22]. These GPCRs are involved in numerous physiological processes, including the regulation of neuronal excitability, metabolism, reproduction, development, hormonal homeostasis, and behavior [8]. A characteristic feature of GPCRs differentially expressed in many cell types in the body, together with their structural diversity, has proved important in medicinal chemistry. GPCRs are involved in many diseases, and are also the target of around half of all modern medicinal drugs [23]. Of all currently marketed drugs, >30% are modulators of specific GPCRs [24]. However, only 10% of GPCRs are targeted by these drugs, emphasizing the potential of the remaining 90% of the GPCR superfamily for the treatment of human disease [8].

Additionally, Celera’s initial analysis of the human genome found 616 GPCRs [25] and Takeda et al. [26] found 178 intronless nonchemosensory GPCRs, whereas the International Human Genome Sequencing Consortium reported a total of 569 “rhodopsin-like” (i.e., Class A) GPCRs [27]. Vassilatis DK and co-worker conducted a comprehensive analysis and reported that the repertoire of GPCRs for endogenous ligands consists of 367 receptors in humans and 392 in mice. Included here are 26 human and 83 mouse GPCRs not previously identified [8]. Phylogenetic analyses cluster 60% of GPCRs according to ligand preference, allowing prediction of ligand types for dozens of orphan receptors. Expression profiling of 100 GPCRs demonstrates that most are expressed in multiple tissues and that individual tissues express multiple GPCRs. Over 90% of GPCRs are expressed in the brain. Strikingly, however, the profiles of most GPCRs are unique, yielding thousands of tissueand cell-specific receptor combinations for the modulation of physiological processes.

Moreover, diverse members of GPCR superfamily participate in a variety of physiological functions and are major targets of pharmaceutical drugs. GPCRs are one of the most important target classes in pharmacology and are the target of many blockbuster drugs [28]. The presumably α-helical transmembrane regions (TMs) of GPCRs are probably arranged with similarity to bacteriorhodopsin (brh) [29]. Except for low-resolution electron diffraction [30,31] and high resolution X ray-based crystallography [32] of brh, the first crystal structure of a mammalian GPCR, bovine rhodopsin [33], was solved. In 2007, the first structure of a human GPCR, β₂-adrenergic receptor, was solved [34,35]. In particular, GPCRs are of enormous importance for the pharmaceutical industry because 52% of all existing medicines act on a GPCR [36]. Very well-known therapeutic drugs such as β-blockers and anti-histamines act on GPCRs. This explains why so many three-dimensional models of GPCRs have been built. Early structural models, such as HIV-1 co-receptor CCR5 (chemokine receptors) [37,38], and human thromboxane receptor [39], are based on the atomic coordinates of the brh structure; some models, e.g. human ADP receptor (Purinergic Receptor P2Y12) [40], are constructed by homology modeling using bovine rhodopsin as a template. All of these modeling studies combined with bioinformatics and chemoinformatics become amenable to the rational design of novel drugs targeting GPCRs in the human genome [28].

These models would contribute to a better understanding of the structure and the function of GPCRs, as well as the ligand-receptor interaction. The present study is devoted to use bioinformatics and computational modeling to build up GPCRs’ theoretical modeling and folding fashions, for prediction of unknown GPCRs in the human genome and studying the interaction between GPCRs ant their ligands at the molecular level.

2. MATERIALS AND METHODS

Structural data of G-protein coupled receptors (GPCR) were taken from a new release of the GPCRDB v.7.6 (http://www.gpcr.org/7tm/htmls/entries.html) based on the latest UniProtKB (Universal Protein Knowledgebase) release of 15-May-2006 (http://www.ebi.ac.uk/swissprot/; http://au.expasy.org/)

, which contain approximately 764 proteins. Their GPCR family profiles are updated. Their amino acid sequences were from Genbank (http://www.ncbi.nlm.nih.gov/Genbank/index.html).

and SWISSPROT. The secondary structure of protein residues corresponds to the DSSP method and their seven TMs were determined based on the GPCR superfamily.

2.1. Data Partitioning

The transmembrane domain regions of 764 known GPCRs were each used as a query I TBLASTEN searches of the National Center for Biotechnology Information human genome database. Sequences were retrieved from the National Center for Biotechnology Information with the accession numbers (Appendix 1). GPCR Class A, B, and C Hidden Markov Model models were also used as queries to search the International Protein Index proteome database [8]. Grouping of the samples was based on the phylogenetic analysis results of Vassilatis and co-worker. Data sets were partitioned into three sets: Training, test, and validation sets. Although protein prediction methodology is almost always reported in terms of training and test sets only, we withheld an external validation set in order to provide an additional rigorous check on model quality. We feel this is necessary since a high statistical correlation on the training and test sets does not necessarily indicate a highly predictive model [41]. To properly partition our data sets so that they each reflect the makeup of the original data set as much as possible, we take into account the distribution of both feature diversity and biological activity as we form our training, test, and external validation sets. In this way, we maintain the original proportions of categorical bins and structural diversity in each of the three sets.

Training dataset is composed of 22 groups in three types of GPCRs (Figure 1) as follows: GPCRs from human different chromosomes (), from human same chromosomes () (such as chromosome 3 and 11), and from different species, based on the phylogenetic trees [8]. The first contains five classes: Class A (), B (), C (), F/S (), and other (). Class A consists of four groups: Group 1 (), Group 2 (), Group 3 () and Group 4 (), which is abstracted into Group 14 (). Class B, C, and F/S each contain one group (, , and). The others fall into nine groups, which forms one group 17. The first is also extracted into one group 18. The second consists of

two groups: chromosome 3 (group 19,) and chromosome 11 (group 20,). The third only includes one group 21 (), consisting of Bovine, Danre, Drome, Chick, Anoga, Dicla, Eisfo, Equas, Eulfu, Pantr, Halsh, besides human, consisting of 3, 2, 3, 3, 1, 1, 1, 1, 1, 1, 1, and 2 GPCRs, respectively. All above 22 groups make up of the training dataset (learning dataset,).

The following test datasets contains two groups of GPCRs, group 23 from human (38 GPCRs,) and group 24 from different species, consisting of 3 bovine, 3 canfa, 3 drome, 3 chick, 3 mouse, 1 Arath, 1 macmu, 1 Mesbi, and 1 Micoh GPCRs, respectively, besides 19 human GPCRs (Appendix 1). Here,

,

,

, ,

, ,

and

.

The validation set involves 22 GPCRs from the sense chain of chromosome 19.

2.2. Sequence Analysis of GPCRs using Bioinformatics

2.2.1. The Scoring Matrices of Training Sets

Take Group 1 of Class A for an example. In order to represent the GPCRs’ TM patterns, a representative nonredundant set of high resolution GPCRs’ TMs are chosen as previously reported to build a training set (Tables 1 and 2). The most consistent sequences are picked up to constitute a scoring matrix by alignment that would be used to predict the TM regions. The amino acid sequences of the seven TMs of GPCRs were extracted and aligned using ClustalW; the TM regions cluster in one fragment (motif) which are about 12, 11, 13, 14, 10, 10, and 12 amino acid residues for TM1-TM7 of the Group 1 (Table 2), respectively; and then their coding regions of such amino acid fragments were chosen to constitute the scoring matrix, which contains 4 types of nucleotides (Figure 2).

Take TM1 of GPCRs in Group 1 of Class A for an example. There are 42 GPCR proteins consisting of the training set after alignment (Table 1). Figure 2 means the scoring matrix, which was generated by assigning a value of the stimulatory potential to each of the 4 defined nucleotides in each position of Table 1. Based on the matrix, we designed a simple algorithm to evaluate the relationship significance of any sequence to the GPCRs’ TM patterns. To each nucleotide (A, T, G, and C) from those 42 proteins of TM1 of group 1 (Table 2), the symbol stands for how many times it takes place in each position, which was calculated as follows:. The score of this nucleotide denotes the proportional (weighting) it takes place in each position, which was calculated as follows:. Take the adenosine

(A) for example. Based on the Table 1, the times of adenosine is at the position of respecttively, and the sum of four nucleotides in the training set is 1512. So, the scores of adenosine is at the position of respectively, whereas it is 0 at other position because it does not appear (Figure 2). The rest (Thymine, Cytidine, and Guanosine) may be deduced by analogy. The value of the scoring matrix is 1.

2.2.2. Test Sets

According to the set theory of mathematics [42], the GPCRs chosen above consist of different training sets, , , , , etc, which composed a union

, and.

Therefore, the test set (Table 3) comes from the complement of for GPCRs aggregate (Appendix 1).

According to our previous methods [40,43], we defined the coding sequence (CDS) of GPCRs’ each TM as TM-CDS unit composed of nucleotides. At first, the TM-CDS units are obtained using the sliding window method one by one from 5’-terminal of GPCRs’ CDS to 3’-teminal: A sequence of nucleotides gives rise to TM-CDS units. For example, the coding sequences of TM1 of GPCRs in group 1 are 12 × 3 nucleotides, namely.

2.2.3. Validation Set

Similarly, we calculate the total scores of the coding sequences of 22 GPCRs located at the sense chain of chromosome 19 using the sliding window method.

2.2.4. Assessment of Model Quality

In this study, training model quality is simply the percent correct classification (binning) of GPCRs’ TM segments for the test set [41]. The overall predictive power of a given model is the percent correct classification for the test set (%test) and for the external validation set (%validation), where the external validation set represents native holdout data. More extensive model assessment was accomplished by a “dynamic partitioning” procedure, which provides a no error rate of the test and external validation sets.

2.2.5. Statistics

Data are expressed as mean±standard deviation (S.D.) through this paper. Statistical analyses were performed with F-test by one-way analysis of variance (abbreviated one-way ANOVA) and by t-test between the means of two groups of the samples. Data was considered significant for at 95 confidence limit [44]. Tests for normality were performed with Shapiro-Wilk test because of the number of samples less than 2000 [45]. The normality of the data was tested by the Shapiro-Wilk statistic. All statistical testing was conducted at significance level 0.10 and all confidence intervals had confidence level 0.90 unless otherwise noted. All tests and confidence intervals were two-sided. Confidence intervals for normal data were constructed from analysis of covariance models [45]. Here, α = 0.10 requests 90% confidence limits. The default value is 0.05. One wayANOVA, Test of Homogeneity of Variances and Multiple comparisons (LSD and Tamhane’s T2), and tests for normality were performed using SPSS version 11.5 software.

2.2.6. The Prediction Model Algorithm

In general, our prediction model (PreMod) method employs the scoring matrices combined with descriptor

selection procedures (seven TMs) that seek to find the optimal subset of the scoring matrices from the original scoring matrix manifold. Partitioning data sets into training, test, and external validation sets rigorously assesses model quality. We extend this methodology by implementation of the dynamic repeating assessment. A flowchart of the prediction model algorithm is provided in Figure 3, which involves the following steps. 1) Divide each data set into two parts: One used to build models, the other to validate models (external validation set); in our implementation, the external validation set is selected to have a high level of diversity; 2) Further partition the 80% identified for model building to form two more sets: Training (80%) and test (20%) sets; 3) Select seven TMs of GPCRs as descriptors based on phylogenetic evolution of the training set with or without crossvalidation procedure (described above); 4) Calculate the score of the training set to construct an optimized subset of the scoring matrix based on the CDS of GPCRs’ 7 TMs; 5) Predict the test set target values using the scoring matrix and calculate the percent correct classification of the test set (%test); 6) Merge the training and test sets, and build a new prediction model using statistic analyses; 7) Predict external validation set values using the prediction model (PreMod), and calculate the percent correct classification of the external validation set (%validation); 8) Repeat steps 1-8 a preset number of times (22 times); 9) Assess each model by the accuracy described above, and generate test and external validation veracity.

3. RESULTS

In what follows, we present three primary results, based on application of the methods described above.

3.1. Phylogenetic Analysis and Structural Evolution

Figure 1 displays the grouping frame of the training datasets (learning dataset,), where 22 groups belong to three types. Of the different chromosome type, there are five classes: Class A (Groups 1-4 and Group 14), Class B (Group 15), Class C (Group 16), Class F (Group 22), and Class O (Groups 5-13 and Group 17). Group 1, Group 2, Group 3, Group 4, and Group 14 of Class A are composed of 44, 38, 32, 20, and 44 GPCRs, respectively. Class B, C, and F/S contain 13, 10, and 9 GPCRs, respectively. Groups and 13 consist of 39, 24, 33, 11, 48, 40, 44, 20, and 20 GPCRs, respectively; while Group 17 includes 33 GPCRs. Group 18, Group 19,Group 20, and Group 21 contain 39, 27, 22, and 20 GPCRs, respectively. The following test datasets are composed of two groups: Group 23 from human and Group 24 from different species (Appendix 1, Table 3). Here, , ,

, and;, , , , , , , and;, , , and.

Table 1 lists the amino acid sequences of TM1 in Group 1 GPCRs, the common 12-residue regions of TM1 by alignment, and the corresponding coding sequences consisting of 36 nucleotides. Table 2 displays the amino acid sequence length and the sample number consisting of the scoring matrix of each transmembrane region of GPCRs in the training datasets after sequence alignments. Different the training sets, different the amino acid sequence length and the sample number consisting of the scoring matrix to same TMs; the same the training sets, different the amino acid sequence length and the sample number consisting of the scoring matrix to different TMs. Figure 2 illustrates the scoring matrices of seven TMs (TM1-TM7) of GPCRs in Group 1 of Class A in the training datasets. This is the core of prediction system of GPCRs.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	[1] Hopkins, A.L. and Groom, C.R. (2002) The druggable genome. Nature Reviews Drug Discovery, 1, 727-730. doi:/10.1038/nrd892
[2]	Neves, S.R., Ram, P.T. and Iyengar, R. (2002) G protein pathways. Science, 296, 1636-1639. doi:/10.1126/science.1071550
[3]	Hynes, R.O. (2002) Integrins: Bidirectional, allosteric signaling machines. Cell, 110, 673-687. doi:/10.1016/S0092-8674(02)00971-6
[4]	Tilley, D.G. (2011) G protein-dependent and G proteinindependent signaling pathways and their impact on cardiac function. Circulation Research, 109, 217-230. doi:/10.1161/CIRCRESAHA.110.231225
[5]	Shen, B., Delaney, M.K. and Du, X. (2012) Inside-out, outside-in, and inside-outside-in: G protein signaling in integrin-mediated cell adhesion, spreading, and retraction. Current Opinion in Cell Biology, 24, 600-606. doi:/10.1016/j.ceb.2012.08.011
[6]	Jaffe, A.B. and Hall, A. (2005) Rho GTPases: Biochemistry and biology. Annual Review of Cell and Developmental Biology, 21, 247-269. doi:/10.1146/annurev.cellbio.21.020604.150721
[7]	Bockaert, J. and Pin, J.P. (1999) Molecular tinkering of G protein-coupled receptors:an evolutionary success. The EMBO Journal, 18, 1723-1729. doi:/10.1093/emboj/18.7.1723
[8]	Vassilatis. D.K., Hohmann, J.G., Zeng, H., Li, F., Ranchalis, J.E., Mortrud, M.T., Brown, A., Rodriguez, S.S., Weller, J.R., Wright, A.C., Bergmann, J.E. and Gaitanaris, G.A. (2003) The G protein-coupled receptor repertoires of human and mouse. Proceedings of the National Academy of Sciences of the United States of America, 100, 4903-4908. doi:/10.1073/pnas.0230374100
[9]	Foord, S.M., Bonner, T.I., Neubig, R.R., Rosser, E.M., Pin, J.P., Davenport, A.P., Spedding, M. and Harmar, A.J. (2005). International Union of Pharmacology. XLVI. G protein-coupled receptor list. Pharmacological Reviews, 57, 279-288. doi:/10.1124/pr.57.2.5
[10]	Horn, F., Weare, J., Beukers, M.W., H?rsch, S., Bairoch, A., Chen, W., Edvardsen, O., Campagne, F. and Vriend, G. (1998) GPCRDB: An information system for G protein-coupled receptors. Nucleic Acids Research, 26, 275- 279.doi:/10.1093/nar/26.1.275
[11]	Attwood, T.K. and Findlay, J.B. (1994). Fingerprinting G-protein-coupled receptors. Protein Engineering, 7, 195-203. doi:/10.1093/protein/7.2.195
[12]	Harmar, A.J. (2001). Family-B G-protein-coupled receptors. Genome Biology, 2, 3013.1-3013.10. doi:/10.1186/gb-2001-2-12-reviews3013
[13]	Br?uner-Osborne, H., Wellendorph, P. and Jensen, A.A. (2007) Structure, pharmacology and therapeutic prospects of family C G-protein coupled receptors. Current Drug Targets, 8, 169-184. doi:/10.2174/138945007779315614
[14]	Herskowitz, I. and Marsh, L. (1988). STE2 protein of Saccharomyces kluyveri is a member of the rhodopsin/ beta-adrenergic receptor family and is responsible for recognition of the peptide ligand alpha factor. Proceedings of the National Academy of Sciences of the United States of America, 85, 3855-3859. doi:/10.1073/pnas.85.11.3855
[15]	Devreotes, P.N., Kimmel, A.R., Johnson, R.L., Klein, P.S., Sun, T.J. and Saxe III, C.L. (1988). A chemoattractant receptor controls development in Dictyostelium discoideum. Science, 241, 1467-1472. doi:/10.1126/science.3047871
[16]	Malbon, C.C. (2004). Frizzleds: New members of the superfamily of G-protein-coupled receptors. Frontiers in Bioscience, 9, 1048-1058. doi:/10.2741/1308
[17]	Taipale, J., Chen, J.K., Cooper, M.K., Wang, B., Mann, R.K., Milenkovic, L., Scott, M.P. and Beachy, P.A. (2000) Effects of oncogenic mutations in Smoothened and Patched can be reversed by cyclopamine. Nature, 406, 1005-1009. doi:/10.1038/35023008
[18]	Buck, L. and Axel, R. (1991) A novel multigene family may encode odorant receptors: A molecular basis for odor recognition. Cell, 65, 175-187. doi:/10.1016/0092-8674(91)90418-X
[19]	Mombaerts, P. (1999) Seven-transmembrane proteins as odorant and chemosensory receptors. Science, 286, 707- 711. doi:/10.1126/science.286.5440.707
[20]	Firestein, S. (2000) The good taste of genomics. Nature 404, 552-553. doi:/10.1038/35007167
[21]	Howard, A.D., McAllister, G., Feighner, S.D., Liu, Q., Nargund, R.P., Van der Ploeg, L.H. and Patchett, A.A. (2001) Orphan G-protein-coupled receptors and natural ligand discovery. Trends in Pharmacological Sciences, 22, 132-140. doi:/10.1016/S0165-6147(00)01636-9
[22]	Lee, D.K., George, S.R., Evans, J.F., Lynch, K.R. and O’ Dowd, B.F. (2001) Orphan G protein-coupled receptors in the CNS. Current Opinion in Pharmacology, 1, 31-39. doi:/10.1016/S1471-4892(01)00003-0
[23]	Filmore, D. (2004) It’s a GPCR world. Modern Drug Discovery, 11, 24-28.
[24]	Wise, A., Gearing, K. and Rees, S. (2002) Target validation of G-protein coupled receptors. Drug Discovery Today, 7, 235-246. doi:/10.1016/S1359-6446(01)02131-6
[25]	Venter, J.C., Adams, M.D., Myers, E.W., Li, P.W., Mural, R.J., Sutton, G.G., Smith, H.O., Yandell, M., Evans, C.A., Holt, R.A., Gocayne, J.D., Amanatides, P., Ballew, R.M., Huson, D.H., Wortman, J.R., Zhang, Q., Kodira, C.D., Zheng, X.H., Chen, L., Skupski, M., Subramanian, G., Thomas, P.D., Zhang, J., Gabor Miklos, G.L., Nelson, C., Broder, S., Clark, A.G., Nadeau, J., McKusick, V.A., Zinder, N., Levine, A.J., Roberts, R.J., Simon, M., Slayman, C., Hunkapiller, M., Bolanos, R., Delcher, A., Dew, I., Fasulo, D., Flanigan, M., Florea, L., Halpern, A., Hannenhalli, S., Kravitz, S., Levy, S., Mobarry, C., Reinert, K., Remington, K., Abu-Threideh, J., Beasley, E., Biddick, K., Bonazzi, V., Brandon, R., Cargill, M., Chandramouliswaran, I., Charlab, R., Chaturvedi, K., Deng, Z., Di Francesco, V., Dunn, P., Eilbeck, K., Evangelista, C., Gabrielian, A.E., Gan, W., Ge, W., Gong, F., Gu, Z., Guan, P., Heiman, T.J., Higgins, M.E., Ji, R.R., Ke, Z., Ketchum, K.A., Lai, Z., Lei, Y., Li, Z., Li, J., Liang, Y., Lin, X., Lu, F., Merkulov, G.V., Milshina, N., Moore, H.M., Naik, A.K., Narayan, V.A., Neelam, B., Nusskern, D., Rusch, D.B., Salzberg, S., Shao, W., Shue, B., Sun, J., Wang, Z., Wang, A., Wang, X., Wang, J., Wei, M., Wides, R., Xiao, C., Yan, C., Yao, A., Ye, J., Zhan, M., Zhang, W., Zhang, H., Zhao, Q., Zheng, L., Zhong, F., Zhong, W., Zhu, S., Zhao, S., Gilbert, D., Baumhueter, S., Spier, G., Carter, C., Cravchik, A., Woodage, T., Ali, F., An, H., Awe, A., Baldwin, D., Baden, H., Barnstead, M., Barrow, I., Beeson, K., Busam, D., Carver, A., Center, A., Cheng, M.L., Curry, L., Danaher, S., Davenport, L., Desilets, R., Dietz, S., Dodson, K., Doup, L., Ferriera, S., Garg, N., Gluecksmann, A., Hart, B., Haynes, J., Haynes, C., Heiner, C., Hladun, S., Hostin, D., Houck, J., Howland, T., Ibegwam, C., Johnson, J., Kalush, F., Kline, L., Koduru, S., Love, A., Mann, F., May, D., McCawley, S., McIntosh, T., McMullen, I., Moy, M., Moy, L., Murphy, B., Nelson, K., Pfannkoch, C., Pratts, E., Puri, V., Qureshi, H., Reardon, M., Rodriguez, R., Rogers, Y.H., Romblad, D., Ruhfel, B., Scott, R., Sitter, C., Smallwood, M., Stewart, E., Strong, R., Suh, E., Thomas, R., Tint, N.N., Tse, S., Vech, C., Wang, G., Wetter, J., Williams, S., Williams, M., Windsor, S., Winn-Deen, E., Wolfe, K., Zaveri, J., Zaveri, K., Abril, J.F., Guigo, R., Campbell, M.J., Sjolander, K.V., Karlak, B., Kejariwal, A., Mi, H., Lazareva, B., Hatton, T., Narechania, A., Diemer, K., Muruganujan, A., Guo, N., Sato, S., Bafna, V., Istrail, S., Lippert, R., Schwartz, R., Walenz, B., Yooseph, S., Allen, D., Basu, A., Baxendale, J., Blick, L., Caminha, M., Carnes-Stine, J., Caulk, P., Chiang, Y.H., Coyne, M., Dahlke, C., Mays, A., Dombroski, M., Donnelly, M., Ely, D., Esparham, S., Fosler, C., Gire, H., Glanowski, S., Glasser, K., Glodek, A., Gorokhov, M., Graham, K., Gropman, B., Harris, M., Heil, J., Henderson, S., Hoover, J., Jennings, D., Jordan, C., Jordan, J., Kasha, J., Kagan, L., Kraft, C., Levitsky, A., Lewis, M., Liu, X., Lopez, J., Ma, D., Majoros, W., McDaniel, J., Murphy, S., Newman, M., Nguyen, T., Nguyen, N., Nodell, M., Pan, S., Peck, J., Peterson, M., Rowe, W., Sanders, R., Scott, J., Simpson, M., Smith, T., Sprague, A., Stockwell, T., Turner, R., Venter, E., Wang, M., Wen, M., Wu, D., Wu, M., Xia, A., Zandieh, A. and Zhu, X. (2001) The sequence of the human genome. Science, 1304-1351. doi:/10.1126/science.1058040
[26]	Meneses, A. (1999) 5HT system and cognition. Neuroscience & Biobehavioral Reviews, 23, 1111-1125. doi:/10.1016/S0149-7634(99)00067-6
[27]	Lander, E.S., Linton, L.M., Birren, B., Nusbaum, C., Zody, M.C., Baldwin, J., Devon, K., Dewar, K., Doyle, M., FitzHugh, W., Funke, R., Gage, D., Harris, K., Heaford, A., Howland, J., Kann, L., Lehoczky, J., LeVine, R., McEwan, P. and McKernan, K. (2001) Initial sequencing and analysis of the human genome. Nature, 409, 860-921. doi:/10.1038/35057062
[28]	O’Dowd, B.F., Ji, X., Alijaniaram, M., Nguyen, T. and George S.R. (2006) A novel drug screening assay for G protein-coupled receptors. In: Rognan, D., Mannhold, R., Kubinyi, H. and Folkers, G., Eds., Ligand Design for G Protein-Coupled Receptors (Methods and Principles in Medicinal Chemistry), Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, 51-60.
[29]	Baldwin, J.M. (1993) The probable arrangement of the helices in G protein-coupledreceptors. The EMBO Journal, 12, 1693-1703.
[30]	Grigorieff, N., Ceska, T.A., Downing, K.H., Baldwin, J.M., Henderson, R. (1996). Electron-crystallographic refinement of the structure of bacteriorhodopsin. Journal of Molecular Biology, 259, 393-421. doi:/10.1006/jmbi.1996.0328
[31]	Kimura, Y., Vassylyev, D.G., Miyazawa, A., Kidera, A., Matsushima, M., Mitsuoka, K., Murata, K., Hirai, T. and Fujiyoshi, Y. (1997). Surface of bacteriorhodopsin revealed by high-resolution electron crystallography. Nature, 389, 206-211. doi:/10.1038/38323
[32]	Pebay-Peyroula, E., Rummel, G., Rosenbusch, J.P. and Landau, E.M. (1997) X-ray structure of bacteriorhodopsin at 2.5 angstroms from microcrystals grown in lipidic cubic phases. Science, 277, 1676-1681. doi:/10.1126/science.277.5332.1676
[33]	Palczewski, K., Kumasaka, T., Hori, T., Behnke, C.A., Motoshima, H., Fox, B.A., Trong, I.L., Teller, D.C., Okada, T., Stenkamp, R.E., Yamamoto, M. and Miyano, M. (2000) Crystal structure of rhodopsin: A G proteincoupled receptor. Science, 289, 739-745. doi:/10.1126/science.289.5480.739
[34]	Rasmussen, S.G., Choi, H.J., Rosenbaum, D.M., Kobilka, T.S., Thian, F.S., Edwards, P.C., Burghammer, M., Ratnala, V.R., Sanishvili, R., Fischetti, R.F., Schertler, G.F., Weis, W.I. and Kobilka, B.K. (2007) Crystal structure of the human β2-adrenergic G-protein-coupled receptor. Nature, 450, 383-387. doi:/10.1038/nature06325
[35]	Cherezov, V., Rosenbaum, D.M., Hanson, M.A., Rasmussen, S.G., Thian, F.S., Kobilka, T.S., Choi, H.J., Kuhn, P., Weis, W.I., Kobilka, B.K. and Stevens, R.C. (2007) High-resolution crystal structure of an engineered human β2-adrenergic G protein-coupled receptor. Science, 318, 1258-1265. doi:/10.1126/science.1150577
[36]	Drews, J. (1996) Genomic sciences and the medicine of tomorrow. Nature Biotechnology, 14, 1516-1518. doi:/10.1038/nbt1196-1516
[37]	Yang, J. and Liu, C.Q. (2000) Molecular modeling on human CCR5 receptors and complex with CD4 antigens and HIV-1envelope Glycoprotein gp120. Acta Pharmacologica Sinica, 20, 29-34.
[38]	Yang, J., Zhang, Y.W., Huang, J.F., Zhang, Y.P. and Liu, C.Q. (2000) Structure analysis of CCR5 from human and primates. Journal of Molecular Structure: Theochem, 505, 199-210. doi:/10.1016/S0166-1280(99)00393-0
[39]	Yang, J. and Hua, W.Y. (1996). Basic pharmacophore for some antithrombotic agents with combined thromboxane receptor antagonists (TXRA)/thromboxine synthase inhibitor (TXSI) activities. Drug Development Research, 39, 197-200. doi:/10.1002/(SICI)1098-2299(199610)39:2<197::AID-DDR14>3.0.CO;2-9
[40]	Zhan, C.Y., Yang, J., Dong, X.C. and Wang, Y.L. (2007) Molecular modeling of purinergic receptor P2Y12 and interaction with its antagonists. Journal of Molecular Graphics and Modelling, 26, 20-31. doi:/10.1016/j.jmgm.2006.09.006
[41]	Xiao, Y.D., Harris, R., Bayram, E., Santago II, P. and Schmitt, J.D. (2006) Supervised self-organizing maps in drug discovery. 2. Improvements in descriptor selection and model validation. Journal of Chemical Information and Modeling, 46, 137-144. doi:/10.1021/ci0500841
[42]	Yang, J., Dong, X.C. and Leng, Y. (2006) Application of FTTP to alpha-helix or beta-strand motifs. Journal of Theoretical Biology, 242, 199-219. doi:/10.1016/j.jtbi.2006.02.014
[43]	Yang, J., Dong, X.C. and Leng, Y. (2006) Conformation biases of amino acids based on tripeptide microenvironment from PDB database. Journal of Theoretical Biology, 240, 374-384. doi:/10.1016/j.jtbi.2005.09.025
[44]	Yu, J.M., Li, D.D., Xu, Z.Q., Cheng, W.X., Zhang, Q., Li, H.Y., Cui, S.X., Miao-Jin, Yang, S.H., Fang, Z.Y. and Duan, Z.J. (2008) Human bocavirus infection in children hospitalized with acute gastroenteritis in China. Journal of Clinical Virology, 42, 280-285. doi:/10.1016/j.jcv.2008.03.032
[45]	Steiger, N.M., Lada, E.K., Wilson, J.R., Alexopoulos, C., Goldsman, D. and Zouaoui, F. (2002) ASAP2: An Improved Batch Means Procedure for Simulation Output Analysis. In: Yücesan, E., Chen, C.-H., Snowdon, J.L. and Charnes, J.M., Eds., Proceedings of the 2002 Winter Simulation Conference, Piscataway, New Jersey, 336344.
[46]	Attwood, T.K., Croning, M.D. and Gaulton, A. (2002) Deriving structural and functional insights from a ligandbased hierarchical classification of G protein-coupled receptors. Protein Engineering, 15, 7-12. doi:/10.1093/protein/15.1.7
[47]	Huang, J.H., Cao, D.S., Yan, J., Xu, Q.S., Hu, Q.N. and Liang, Y.Z. (2012) Using core hydrophobicity to identify phosphorylation sites of human G protein-coupled receptors. Biochimie, 94, 1697-1704. doi:/10.1016/j.biochi.2012.03.022
[48]	Manning, G., Whyte, D.B., Martinez, R., Hunter, T. and Sudarsanam, S. (2002) The protein kinase complement of the human genome. Science, 298, 1912-1934. doi:/10.1126/science.1075762
[49]	Pitcher, J.A., Freedman, N.J. and Lefkowitz, R.J. (1998) G protein-coupled receptor kinases. Annual Review of Biochemistry, 67, 653-692. doi:/10.1146/annurev.biochem.67.1.653
[50]	Tobin, A.B., Butcher, A.J. and Kong, K.C. (2008) Location, location, location site-specific GPCR phosphorylation offers a mechanism for cell-type-specific signaling. Trends in Pharmacological Sciences, 29, 413-420. doi:/10.1016/j.tips.2008.05.006
[51]	Hauser, F., Cazzamali, G., Williamson, M., Blenau, W. and Grimmelikhuijzen, J.P. (2006) A review of neurohormone GPCRs present in the fruitfly Drosophila melanogaster and the honey bee Apis mellifera. Progress in Neurobiology, 80, 1-19. doi:/10.1016/j.pneurobio.2006.07.005
[52]	Meyer, J.M., Ejendal, K.F., Avramova, L.V., GarlandKuntz, E.E., Giraldo-Calderón, G.I., Brust, T.F., Watts, V.J. and Hill, C.A. (2012) A “genome-to-lead” approach for insecticide discovery: Pharmacological characterization and screening of Aedes aegypti D(1)-like dopamine receptors. PLOS Neglected Tropical Diseases 6, e1478. doi:/10.1371/journal.pntd.0001478
[53]	Gamo, F.J., Sanz, L.M., Vidal, J., de Cozar, C., Alvarez, E., Lavandera, J.L., Vanderwall, D.E., Green, D,V., Kumar, V., Hasan, S., Brown, J.R., Peishoff, C.E., Cardon, L.R. and Garcia-Bustos, J.F. (2010) Thousands of chemical starting points for antimalarial lead identification. Nature, 465, 305-312. doi:/10.1038/nature09107
[54]	Hill, C.A., Fox, A.N., Pitts, R.J., Kent, L.B., Tan, P.L., Chrystal, M.A., Cravchik, A., Collins, F.H., Robertson, H.M. and Zwiebel, L.J. (2002) G protein-coupled receptors in Anopheles gambiae. Science, 298, 176-178. doi:/10.1126/science.1076196
[55]	Gruber, C.W., Muttenthaler, M. and Freissmuth, M. (2010) Ligand-based peptide design and combinatorial peptide libraries to target G protein-coupled receptors. Current Pharmaceutical Design, 16, 3071-3088. doi:/10.2174/138161210793292474
[56]	Janovick, J.A., Park, B.S. and Conn, P.M. (2011) Therapeutic rescue of misfolded mutants: Validation of primary high throughput screens for identification of pharmacoperone drugs. PLoS One, 6, e22784. doi:/10.1371/journal.pone.0022784
[57]	Janovick, J.A., Patny, A., Mosley, R., Goulet, M.T., Altman, M.D., Rush 3rd, T.S., Cornea. A. and Conn, P.M. (2009) Molecular mechanism of action of pharmacoperone rescue of misrouted GPCR mutants: The GnRH receptor. Molecular Endocrinology, 23, 157-168. doi:/10.1210/me.2008-0384
[58]	Janovick, J.A., Maya-Nunez, G. and Conn, P.M. (2002) Rescue of hypogonadotropic hypogonadism-causing and manufactured GnRH receptor mutants by a specific proteinfolding template: misrouted proteins as a novel disease etiology and therapeutic target. The Journal of Clinical Endocrinology & Metabolism, 87, 3255-3262. doi:/10.1210/jc.87.7.3255
[59]	Galietta, L.J., Springsteel, M.F., Eda, M., Niedzinski, E.J., By, K., Haddadin, M.J., Kurth, M.J., Nantz, M.H. and Verkman, A.S. (2001) Novel CFTR chloride channel activators identified by screening of combinatorial libraries based on flavone and benzoquinolizinium lead compounds. The Journal of Biological Chemistry, 276, 19723-19728. doi:/10.1074/jbc.M101892200
[60]	Ulloa-Aguirre, A., Janovick, J.A., Leanos-Miranda, A. and Conn, P.M. (2003) Misrouted cell surface receptors as a novel disease aetiology and potential therapeutic target: The case of hypogonadotropic hypogonadism due to gonadotropin-releasing hormone resistance. Expert Opinion on Therapeutic Targets, 7, 175-185. doi:/10.1517/14728222.7.2.175
[61]	Bernier, V., Lagace, M., Bichet, D.G. and Bouvier, M. (2004) Pharmacological chaperones: Potential treatment for conformational diseases. Trends in Endocrinology & Metabolism, 15, 222-228. doi:/10.1016/j.tem.2004.05.003
[62]	Noorwez, S.M., Malhotra, R., McDowell, J.H., Smith, K.A., Krebs, M.P. and Kaushal, S. (2004) Retinoids assist the cellular folding of the autosomal dominant retinitis pigmentosa opsin mutant P23H. The Journal of Biological Chemistry, 279, 16278-16284. doi:/10.1074/jbc.M312101200
[63]	Tveten, K., Holla, ?.L., Ranheim, T., Berge, K.E., Leren, T.P. and Kulseth, M.A. (2007) 4-Phenylbutyrate restores the functionality of a misfolded mutant low-density lipoprotein receptor. FEBS Journal, 274, 1881-1893. doi:/10.1111/j.1742-4658.2007.05735.x
[64]	Benedek, G.B., Pande, J., Thurston, G.M. and Clark, J.I. (1999) Theoretical and experimental basis for the inhibition of cataract. Progress in Retinal and Eye Research, 18, 391-402. doi:/10.1016/S1350-9462(98)00023-8
[65]	Heiser, V., Scherzinger, E., Boeddrich, A., Nordhoff, E., Lurz, R., Schugardt, N., Lehrach, H. and Wanker, E.E. (2000) Inhibition of huntingtin fibrillogenesis by specific antibodies and small molecules: Implications for Huntington’s disease therapy. Proceedings of the National Academy of Sciences of the United States of America, 97, 6739-6744. doi:/10.1073/pnas.110138997
[66]	Muchowski, P.J. and Wacker, J.L. (2005) Modulation of neurodegeneration by molecular chaperones. Nature Reviews Neuroscience, 6, 11-22. doi:/10.1038/nrn1587
[67]	Forloni, G., Terreni, L., Bertani, I., Fogliarino, S., Invernizzi, R., Assini, A., Ribizzi, G., Negro, A., Calabrese, E., Volonté, M.A., Mariani, C., Franceschi, M., Tabaton, M. and Bertoli, A. (2002) Protein misfolding in Alzheimer's and Parkinson’s disease: Genetics and molecular mechanisms. Neurobiology of Aging, 23, 957-976. doi:/10.1016/S0197-4580(02)00076-3
[68]	Peng, Y., Li, C., Chen, L., Sebti, S. and Chen, J. (2003) Rescue of mutant p53 transcription function by ellipticine. Oncogene, 22, 4478-4487. doi:/10.1038/sj.onc.1206777
[69]	Janovick, J.A., Goulet, M., Bush, E., Greer, J., Wettlauffer, D.G. and Conn, P.M. (2003) Structure-activity relations of successful pharmacologic chaperones for rescue of naturally occurring and manufactured mutants of the gonadotropin-releasing hormone receptor. Journal of Pharmacology and Experimental Therapeutics, 305, 608- 614. doi:/10.1124/jpet.102.048454
[70]	Costanzi, S. (2010) Modeling G Protein-Coupled Receptors: A concrete possibility. Chimica oggi, 28, 26-31.
[71]	Sugahara, D., Kaji, H., Sugihara, K., Asano, M. and Narimatsu, H. (2012) Large-scale identification of target proteins of a glycosyltransferase isozyme by Lectin-IGOT- LC/MS, an LC/MS-based glycoproteomic approach. Scientific Reports, 2, 680. doi:/10.1038/srep00680
[72]	Ying, S.Y., Chang, D.C. and Lin, S.L. (2013) The MicroRNA. Methods in Molecular Biology, 936, 1-19. doi:/10.1007/978-1-62703-083-0_1
[73]	Coskun, M., Bjerrum, J.T., Seidelin, J.B. and Nielsen, O.H. (2012) MicroRNAs in inflammatory bowel disease— pathogenesis, diagnostics and therapeutics. World Journal of Gastroenterology, 18, 4629-4634. doi:/10.3748/wjg.v18.i34.4629