http://dx.doi.org/10.13160/ricns.2016.9.2.136
Structure Prediction of KiSS1-derived Peptide Receptor Using Comparative Modelling
Santhosh Kumar Nagarajan and Thirumurthy Madhavan†
Abstract
KiSS1-derived peptide receptor, a GPCR protein, binds with the hormone kiss peptin. They are important in the neuroendocrine regulation of reproduction and in the secretion of gonadotrophin-releasing hormone. Thus, analysing the structural features of the receptor becomes important. However, the three dimensional structure of the protein is unavailable. Hence in this study, we have performed the homology modelling of KiSS1-derived peptide receptor with 5 different templates. 30 models were constructed using two platforms – Easymodeller and ITasser. The optimal models were chosen based on the model validation. Two models were selected after validation. The developed models could provide useful for analysing the structural features of KiSS1-derived peptide receptor and their pathophysiological role in various disorders related to them.
Keywords: KiSS1-derived Peptide Receptor, GPCR, GPR54, Kisspeptin, Homology Modelling
1. Introduction
The KiSS1-derived peptide receptor (also known as GPR54 or the Kisspeptin receptor) belongs to the G- protein coupled receptor family[1,2]. GPR54 binds with the peptide hormone kisspeptin (metastin) which are encoded by the Kiss-1 gene[3]. Kisspeptin was originally discovered as a metastasis inhibitor[4]. Later, their sig- nalling mechanism found to be playing a major role in the neuroendocrine regulation of reproduction[5]. It was learned that GPR54 triggers the release of gonadotro- phin-releasing hormone (GnRH)[6]. Also, kisspeptin neurons act as conduits that relay humoral and environ- mental signals to GnRH neurons. This indicates that the kisspeptin/gpr-54 signalling is important in the neuroen- docrine regulation of reproduction. Thus, GPR54 becomes a potential therapeutic target.
Increased hypothalamic GPR54 signalling, could result in the activation of puberty[7]. It was shown that, by inactivating mutations in the gene that encodes for GPR54, results in a pubertal delay in human[8]. Funes
S et al. developed a mutant mouse line with a targeted disruption of the GPR54 receptor. Their analysis of the GPR54 mutant mice revealed that there are develop- mental abnormalities in both male and female genitalia.
Also, there are histo-pathological changes in tissues which contained sexually dimorphic features[9]. The study indicates the importance of GPR54/KiSS-1 sig- nalling in normal sexual development.
Kisspeptin and GPR54 are expressed in various other tissues and their functions have not been clearly reported. In a study by Horikoshi et al. the immune- reactivity of kisspeptin is elevated in human preg- nancy[10]. Kisspeptins have been reported as potent vasoconstrictors in humans, indicating their importance in the cardiovascular system[11]. They are also found to modulate synaptic excitability in hippocampal dentate granule cells[12]. There is also report denoting the role of kisspeptin in islet function and thus in insulin secre- tion[13].
Since kisspeptin/GPR54 signalling greatly influence GnRH and gonadotropin secretion, it is plausible to consider them as a potential target for treatment of the conditions related to them. Exploring the structural fea- tures of kisspeptin and GPR54 thus becomes important.
But there are no available structures of kisspeptin and GPR54. Homology modelling provides an alternate
Department of Bioinformatics, School of Bioengineering, SRM Univer- sity, SRM Nagar, Kattankulathur, Chennai 603203, India.
†Corresponding author : thiru.murthyunom@gmail.com, thirumurthy.m@ktr.srmuniv.ac.in
(Received: May 30, 2016, Revised: June 15, 2016, Accepted: June 25, 2016)
way of predicting the three-dimensional structure of a protein when only the sequence data of the protein is available. The number of protein structures resolved experimentally lags behind the sequence data availa- ble[14]. The main reason for this is the enormous amount of time required to prepare protein for crystallization as experimental process such as protein expression, puri- fication followed by crystallization, requires years to perform. In this case, homology modelling based on comparative modelling can provide as a tool for the experimental procedures in finding the structure of the protein in a rather short time. In this study, we have developed three-dimensional models of GPR54 based on comparative modelling and validated them. The developed models could provide as a tool for further studies on the structural features and binding features of kisspeptin/GPR54 interaction.
2. Material and Methods
2.1. Template Selection
The amino acid sequence of the human KiSS-1 recep- tor (accession No: Q969F8) was retrieved from the Uni- prot database. Protein BLAST[15] search was performed against PDB[16] to find suitable templates for modelling the receptor. 5 different templates were selected based on sequence identity, query coverage and E-value. The selected templates were – 4DJH, 5DHG, 4EA3, 4EJ4 and 4DKL. If the level of sequence identity is above 30%, then up to 90% of the polypeptide conformation tends to be modelled well[17-19]. All three templates were having sequence identity ≥ 30%. As the identity of the templates were low (Table 1), to improve the model accuracy, the multiple template based homology mod- elling was also performed. Query coverage for the tem- plates was greater than 70%. Also, all of the templates retained the seven transmembrane helix regions, which is the characteristic feature of the GPCR proteins.
2.2. Homology Modelling
Using the modelling platforms, EasyModeller 4.0[20]
and ITasser[21], the three dimensional structures of GPR54 were developed. EasyModeller 4.0 uses MOD- ELLER 9.12[22] and Python 2.7.1 in the backend. I- TASSER server is an on-line server used for protein structure and function predictions. In the recently con- cluded CASP (Critical Assessment of Techniques for Protein Structure Prediction), The I-TASSER server was ranked as number one in the server section[23]. At first, the predicted models were assessed and validated using the RMSD values. Then, Using RAMPAGE web server, Ramachandran plots for the models were plot- ted[24]. Ramachandran plot provides a way to visualize backbone dihedral angles ψ against φ of amino acid res- idues in protein structure, which identifies the sterically allowed regions for these angles. Later, validation by Verify3D and ERRAT plots were carried out. Verify3D determines the compatibility of the predicted model with its own amino acid sequence by assigning a struc- tural class based on its location and environment (alpha, beta, loop, polar, nonpolar etc.) and comparing the results to good structures[25]. ERRAT plots are plotted as a function of the position of a sliding 9-residue win- dow. The error function is based on the statistics of non- bonded atom-atom interactions present in the struc- ture[26].
3. Results and Discussion
3.1. Model Generation
Using EasyModeller, 5 models are modelled for each of the five templates – 4DJH, 5DHG, 4EA3, 4EJ4 and 4DKL. Therefore totally 25 models were developed using EasyModeller. Using the three different templates, single and multiple template based approaches were carried out. For Multiple template based approach, using the CLUSTALW[27] program, multiple sequence
Table 1. The query coverage and identity values of the templates
PDB ID Max Score Total score Query Coverage % E Value Identity %
4DJH 106 169 70% 7e-25 32%
5DHG 150 150 74% 3e-40 37%
4EA3 150 150 74% 4e-40 37%
4EJ4 96.7 155 71% 1e-21 32%
4DKL 103 159 80% 5e-24 30%
alignment was done to find conserved residues. Various models were developed using multiple template based approach. Five best models developed from ITasser server were also selected. The alignment of the tem- plates with the receptor GPR54 was represented in Fig.
1.
3.2. Model Validation
The predicted models were validated using various validation techniques. Root mean square deviation (RMSD) of all the predicted models with their respec- tive template was calculated. Ramachandran plot was generated for each model and the number of residues in favourable, allowed and disallowed region was identi- fied. The statistics of both RMS deviation and Ram-
achandran plots are represented in the Table 2. Only models scoring acceptable results are displayed and are numbered. Verify3D was also performed for all the models. Finally ERRAT plots were developed for the models. The results from Verify3D and ERRAT plots are represented in Table 3. Based on the statistics, from the models developed using Easymodeller, models 2, 6, 13, 16 and 24 (one model from each template) were found to be the best models. Generally, all the five mod- els generated using ITasser scored good results in all the validations. Especially, model 28 scored well in all the validation and is found to be the most reliable among the developed models. Also, all the developed models have similar structure. The best models – Model 13 and Model 28 are represented in Fig. 2. RC plot and ERRAT Fig. 1. Alignment between the target (GPR54) and templates (4DJH, 5DHG, 4EA3, 4EJ4 and 4DKL).
plots of the selected models were represented in Fig. 3 and Fig. 4, respectively.
4. Conclusion
Three dimensional models for KiSS1-derived peptide receptor (GPR54) were generated using single and mul- tiple template based approaches. Model numbers 2, 6, 13, 16 and 24 were selected as best, based on their RMS
deviation, Ramachandran plot, ERRAT plot and Veri- fy3D values. The selected models showed similar struc- tures. Based on the results after model validation, it is found that all the generated models are similar and the structures are reliable. These predicted models would be useful in the studying the interaction of kisspeptin with the receptor GPR54 in future. Also, these models may serve as a reliable tool for analysing the important struc- tural features and function of GPR54.
Table 2. RMS Deviation values
Model No
Templates Used
Homology Modelling Validation RMSD
Ramachandran Plot Number of residues in
favored region (%)
Number of residues in allowed region (%)
Number of residues in outlier region (%) 1
4DJH
0.501 92.9 4.0 3.0
2 0.306 93.2 4.8 2.0
3 0.310 94.9 2.5 2.5
4 0.365 93.9 3.5 2.5
5 0.378 94.9 3.5 1.5
6
5DHG
0.262 95.5 4.0 0.5
7 0.278 93.4 4.3 2.3
8 0.250 94.9 3.0 2.0
9 0.265 95.2 4.0 0.8
10 0.211 93.2 5.1 1.8
11
4EA3
0.167 94.4 4.5 1.0
12 0.263 95.5 3.0 1.5
13 0.152 94.9 3.5 1.5
14 0.206 93.4 3.8 2.8
15 0.205 93.7 4.3 2.0
16
4EJ4
0.549 93.4 5.1 1.5
17 0.597 92.2 4.5 3.3
18 0.465 90.4 6.8 2.8
19 0.630 91.2 5.8 3.0
20 0.518 92.2 4.0 3.8
21
4DKL
0.947 90.9 6.1 3.0
22 0.480 88.6 8.6 2.8
23 0.946 89.6 6.3 4.0
24 0.431 90.4 6.3 3.3
25 0.837 93.2 3.5 3.3
26
ITasser
0.696 78.3 12.1 9.6
27 0.408 79.0 11.9 9.1
28 0.209 95.5 3.8 0.8
29 0.878 80.6 10.1 9.3
30 0.262 82.6 10.1 7.3
Table 3. ERRAT and Verify results
Model No Templates Used ERRAT
Overall quality factor
Verify3D
(% of the residues had an averaged 3D-1D score >= 0.2) 1
4DJH
67.313 52.26
2 73.829 54.52
3 70.055 44.47
4 69.780 42.460
5 70.330 53.27
6
5DHG
70.497 53.77
7 70.468 55.53
8 69.643 48.49
9 65.165 47.74
10 73.065 48.99
11
4EA3
65.476 54.27
12 75.309 47.99
13 77.538 47.74
14 70.938 46.48
15 68.622 42.71
16
4EJ4
75.733 38.94
17 69.689 48.99
18 70.588 35.18
19 68.041 40.20
20 73.753 41.71
21
4DKL
51.323 67.34
22 61.741 57.54
23 49.867 65.33
24 61.702 57.29
25 58.090 57.04
26
ITasser
84.872 64.57
27 79.949 60.55
28 90.256 77.39
29 85.641 71.86
30 88.830 63.57
Fig. 2. Best models (Model13 and Model 28) selected after validation
Fig. 3. RC plot for selected models 13(a) and 28(b)
Fig. 4: ERRAT plot developed for the selected models 13(a) and 28(b)
*on the error axis, two lines are drawn to indicate the confidence with which it is possible to reject regions that exceed that error value
References
[1] A. I. Muir, L. Chamberlain, N. A. Elshourbagy, D.
Michalovich, D. J. Moore, A. Calamari, P. G. Sze- keres, H. M. Sarau, J. K. Chambers, P. Murdock, K.
Steplewski, U. Shabon, J. E. Miller, S. E. Middle- ton, J. G. Darker, C. G. Larminie, S. Wilson, D. J.
Bergsma, P. Emson, R. Faull, K. L. Philpott, and D.
C. Harrison, "AXOR12, a novel human G protein- coupled receptor, activated by the peptide KiSS-1", J. Biol. Chem., Vol. 276, pp. 28969-28975. 2001.
[2] T. Ohtaki, Y. Shintani, S. Honda, H. Matsumoto, A.
Hori, K. Kanehashi, Y. Terao, S. Kumano, Y.
Takatsu, Y. Masuda, Y. Ishibashi, T. Watanabe, M.
Asada, T. Yamada, M. Suenaga, C. Kitada, S.
Usuki, T. Kurokawa, H. Onda, O. Nishimura, and M. Fujino, "Metastasis suppressor gene KiSS-1 encodes peptide ligand of a G-protein-coupled receptor", Nature, Vol. 411, pp. 613–617, 2001.
[3] M. Kotani, M. Detheux, A. Vandenbogaerde, D.
Communi, J. M. Vanderwinden, E. L. Poul, S.
Brézillon, R. Tyldesley, N. Suarez-Huerta, F. Van- deput, C. Blanpain, S. N. Schiffmann, G. Vassart, and M. Parmentier, “The metastasis suppressor gene KiSS-1 encodes kisspeptins, the natural ligands of the orphan G protein-coupled receptor GPR54”, J.
Biol. Chem., Vol. 276, pp. 34631-34636, 2001.
[4] J. H. Lee, M. E. Miele, D. J. Hicks, K. K. Phillips, J. M. Trent, B. E. Weissman, and D. R. Welch,
“KiSS-1, a novel human malignant melanoma metastasis-suppressor gene”, JNCI-J. Natl. Cancer I., Vol. 88, pp. 1731-1737, 1996.
[5] S. M. Popa, D. K. Clifton, and R. A. Steiner, “The role of kisspeptins and GPR54 in the neuroendo- crine regulation of reproduction”, Annu. Rev.
Physiol., Vol. 70, pp. 213-238, 2008.
[6] T. M. Plant, S. Ramaswamy, and M. J. Dipietro,
“Repetitive activation of hypothalamic G protein- coupled receptor 54 with intravenous pulses of kis- speptin in the juvenile monkey (Macaca mulatta) elicits a sustained train of gonadotropin-releasing hormone discharges”, Endocrinology, Vol. 147, pp.
1007-1013, 2006.
[7] M. Shahab, C. Mastronardi, S. B. Seminara, W. F.
Crowley, S. R. Ojeda, and T. M. Plant, “Increased hypothalamic GPR54 signaling: a potential mecha- nism for initiation of puberty in primates”, P. Natl Acad. Sci. U.S.A., Vol. 102, pp. 2129-2134, 2005.
[8] S. B. Seminara, S. Messager, E. E. Chatzidaki, R.
R. Thresher, J. S. Jr. Acierno, J. K. Shagoury, Y. Bo- Abbas, W. Kuohung, K. M. Schwinof, A. G. Hen-
drick, D. Zahn, J. Dixon, U. B. Kaiser, S. A. Slau- genhaupt, J. F. Gusella, S. O'Rahilly, M. B. Carlton, W. F. Jr. Crowley, S. A. Aparicio, and W. H.
Colledge, “The GPR54 gene as a regulator of puberty”, New Engl. J. Med., Vol. 349, pp. 1614- 1627, 2003.
[9] S. Funes, J. A. Hedrick, G. Vassileva, L. Markowitz, S. Abbondanzo, A. Golovko, S. Yang, F. J. Mon- sma, and E. L. Gustafson, “The KiSS-1 receptor GPR54 is essential for the development of the murine reproductive system”, Biochem. Bioph. Res.
Co., Vol. 312, pp. 1357-1363, 2003.
[10] Y. Horikoshi, H. Matsumoto, Y. Takatsu, T. Ohtaki, C. Kitada, S. Usuki, and M. Fujino, “Dramatic ele- vation of plasma metastin concentrations in human pregnancy: metastin as a novel placenta-derived hormone in humans”. J. Clin. Endocrinol. Metab., Vol. 88, pp. 914-919, 2003.
[11] E. J. Mead, J. J. Maguire, R. E. Kuc, and A. P. Dav- enport, “Kisspeptins are novel potent vasoconstric- tors in humans, with a discrete localization of their receptor, G protein-coupled receptor 54, to athero- sclerosis-prone vessels”, Endocrinology, Vol. 148, pp. 140-147, 2007.
[12] A. C. Arai, Y. F. Xia, E. Suzuki, M. Kessler, O. Civ- elli, and H. P. Nothacker, “Cancer metastasis-sup- pressing peptide metastin upregulates excitatory synaptic transmission in hippocampal dentate gran- ule cells”, J. Neurophysiol., Vol. 94, pp. 3648-3652, 2005.
[13] A. C. Hauge-Evans, C. C. Richardson, H. M. Milne, M. R. Christie, S. J. Persaud, and P. M. Jones, “A role for kisspeptin in islet function”, Diabetologia, Vol. 49, pp. 2131-2135, 2006.
[14] G. C. Baker, J. J.Smith, and D. A. Cowan, “Review and re-analysis of domain-specific 16S primers”, J.
Microbiol. Meth., Vol. 55, pp. 541-555, 2003.
[15] S. F. Altschul, W. Gish, W. Miller, E. W. Myers, and D. J. Lipman, “Basic local alignment search tool”, J. Mol. Biol., Vol. 215, pp. 403-410, 1990.
[16] H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat, H. Weissig, I. N. Shindyalov, and P. E.
Bourne, “The protein data bank”, Nucleic Acids Res., Vol. 28, pp. 235-242, 2000.
[17] M. Shalini and T. Madhavan, “Homology Modeling of CCR 4: Novel therapeutic target and preferential maker for Th2 cells”, J. Chosun Natural Sci., Vol.
7, pp. 234-240, 2014.
[18] B. Sathya and T. Madhavan, “Homology modeling of cysteinyl leukotriene1 receptor”, J. Chosun Nat- ural Sci., Vol. 8, pp. 13-18, 2015.
[19] S. K. Nagarajan and T. Madhavan, “3D Structure prediction of thromboxane A2 receptor by homology modeling”, J. Chosun Natural Sci., Vol.
8, pp. 75-79, 2015.
[20] B. K. Kuntal, P. Aparoy, and P. Reddanna, “Easy- Modeller: A graphical interface to MODELLER”, BMC Research Notes, Vol. 3, pp. 226, 2010.
[21] A. Roy, A. Kucukural, and Y. Zhang, “I-TASSER:
a unified platform for automated protein structure and function prediction”, Nat. Protoc., Vol. 5, pp.
725-738, 2010.
[22] N. Eswar, M. A. Marti-Renom, B. Webb, M. S.
Madhusudhan, D. Eramian, M. Shen, U. Pieper, and A. Sali, “UNIT 5.6 Comparative Protein Structure Modeling with MODELLER”, Current Protocols in Bioinformatics, suppl. 15, New York: John Wiley &
Sons, Inc., pp. 1–30, 2006.
[23] J. Yang, R. Yan, A. Roy, D. Xu, J. Poisson, and Y.
Zhang, “The I-TASSER Suite: Protein structure and
function prediction”, Nat. Methods, Vol. 12, pp. 7- 8, 2015.
[24] S. C. Lovell, I. W. Davis, W. B. Arendall III, P. I.
W. de Bakker, J. M. Word, M. G. Prisant, J. S. Rich- ardson, and D. C. Richardson, “Structure validation by Cα geometry: φ, ψ and Cβ deviation”, Proteins, Vol. 50, pp. 437-450, 2002.
[25] J. U. Bowie, R. Lüthy, and D. Eisenberg, “A method to identify protein sequences that fold into a known three-dimensional structure”, Science, Vol.
253, pp. 164-170, 1991.
[26] C. Colovos and Y. O. Yeates, “Verification of pro- tein structures: patterns of nonbonded atomic inter- actions”, Protein Sci., Vol. 2, pp. 1511-1519, 1993.
[27] J. D. Thompson, D. G. Higgins, and T. J. Gibson,
“CLUSTAL W: improving the sensitivity of pro- gressive sequence weighting, position-specific gap penalties and weight matrix choice”, Nucleic Acids Res., Vol. 22, pp. 4673-4680, 1994.
