I Indian Journal of Exprimental Biology Vol. 52, April 2014, pp. 375-382

Pharmacophore based approach to design inhibitors in Crustaceans: An insight into the molt inhibition response to the receptor guanylyl cyclase

Sajal Shrivastava & S Adline Princy* Quorum Sensing Laboratory, SASTRA’s Hub for Research and Innovation, SASTRA University, Thanjavur 613 401, India

Received 13 May 2013; revised 8 January 2014

The first set of competitive inhibitors of molt inhibiting hormone (MIH) has been developed using the effective approaches such as Hip-Hop, and manual alterations. Moreover, the conserved residues at 71 and 72 positions in the molt inhibiting hormone is known to be significant for selective inhibition of ecdysteroidogenesis; thus, the information from mutation and solution structure were used to generate common pharmacophore features. The geometry of the final six-feature pharmacophore was also found to be consistent with the homology-modeled MIH structures from various other decapod crustaceans. The Hypo-1, comprising six features hypothesis was carefully selected as a best pharmacophore model for virtual screening created on the basis of rank score and cluster processes. The hypothesis was validated and the database was virtually screened using this 3D query and the compounds were then manually altered to enhance the fit value. The hits obtained were further filtered for drug-likeness, which is expressed as physicochemical properties that contribute to favorable ADME/Tox profiles to eliminate the exhibit and poor . In conclusion, the higher fit values of CI-1 (4.6), CI-4 (4.9) and CI-7 (4.2) in conjunction with better pharmacokinetic profile made these molecules practically helpful tool to increase production by accelerating molt in crustaceans. The use of feeding sub-therapeutic dosages of these growth enhancers can be very effectively implemented and certainly turn out to be a vital part of emerging nutritional strategies for economically important crustacean livestock.

Keywords: Ecdysteroid, Growth enhancers, HipHop, Molt inhibiting hormone, Pharmacophore

In crustaceans, molting, growth, and development is in the medullar terminalis of X-organ and then governed by ecdysteroids synthesized by paired transported to the sinus gland8-12. Various endocrine glands, Y-organs that are located in the quantification studies have revealed that the quantity anterior cephalothorax1-4. Crustacean hyperglycemic or the number of receptors on the Y-organ remains hormone (CHH) family neuropeptides are potential constant and during intermolt period, the synthesis modulators of various regulatory processes in the of ecdysteroids favorably inactivated by MIH13-15. crustacean physiological system: CHH stimulate Contemporary reports from radioreceptor binding during stress conditions such as hypoxia, hyper assays also intend a relation between the rate of or hypothermia, molt inhibiting hormone (MIH) ecdysteroid production and the receptiveness of the inhibits the synthesis and/or secretion of ecdysteroids Y-organ to MIH, but the MIH receptor has not been from Y-organ and vitellogenin inhibitory peptide fully characterized for any species16-20. Inhibition (VIH) inhibits the reproduction. The CHH family of facultative synthesis of ecdysteroid by MIH is neuropeptides reveal pleotropic properties, i.e. the facilitated by activation of the specific transcription neuropeptides having more than two diverse factor that impedes phantom expression21. It has been biological activities have been described5-7. The most reported that MIH predominantly binds to guanylyl extensively acknowledged paradigm proposes that cyclase (GC-II) and/or G protein coupled receptor synthesis of ecdysteroids, polyhydroxylated C-27 on the Y-organ cells and suppresses the ecdysteroid steroid derived from cholesterol is negatively biosynthesis by a cAMP-dependent activation of regulated by molt inhibiting hormone synthesized nitric oxide synthase (NOS) and NO-dependent guanylyl cyclase (GC-I), both of which are articulated —————— 22-27 * in YOs . The existence of MIH, one of the most Correspondent author critical neuropeptide in the family of CHH was Telephone: +91 4362 264101 Fax: +91 4362 264120 suggested by the fact that eye-stalk ablation leads to E-mail: [email protected] prompt upsurge in ecdysteroid titer in hemolymph 376 INDIAN J EXP BIOL, APRIL 2014

and hence, persuades precocious molting28. However, and functional specificity and the homology model as CHH, gonad inhibiting hormone (GIH) and of CHH evidently indicates the absence of the mandibular organ inhibiting hormone (MOIH) are N-terminal α-helix and C-terminal tail. also synthesized in X-organ and secreted from sinus Pharmacophore based approach—To date, the 3D gland, eye-stalk ablation cause imbalances in other structure of receptor guanylyl cyclase has not been physiological processes29,30. obtained by the experiment and the homology Structure of molt inhibiting hormone (MIH)—MIH module packages were unsuccessful to build the has been sequestered and characterized from decapod initial model of rGC due to very little sequence and crustaceans of several taxa and it was apparent that structural resemblances in the database. Additional the size of mature MIH ranges from 8-11 kDa and structural information, notably of the receptor are 72-78 amino acid residues in length. Reports guanylyl cyclase in either free or MIH-bound state, strongly indicate the presence of six highly conserved will be very helpful for the optimization of inhibitors, cysteine residues in all CHH family neuropeptides such as to increase frequency of molting to achieve that are responsible for the formation of the better growth in crustaceans. three disulfide bridges to stabilize their structure31. In absence of a three-dimensional structure of Where pertinent data exist, most MIH comprise receptor guanylyl cyclase and data from crystal a non-amidated C-terminus whereas CHH have an structure and binding site of molt inhibiting hormone, amidated C-terminus substantial for conferring pharmacophore-based design of competitive hyperglycemic activity32. The solution structure of inhibitors is one of the ubiquitous approaches for MIH from the kuruma prawn, Marsupenaeus and lead optimization. The discovery japonicus reveals the existence of five α-helices of the three dimensional structure of molt inhibiting located within the N-terminal motif in the hormone by X-ray crystallography initiated the way MIH structure and was considered a part of the for design and synthesis of competitive inhibitors. determinants of the functional specificity33-35. MIH as a therapeutically relevant drug target with Mutation analysis has validated that the N-terminal undetermined active site geometries, HipHop based N13 and C-terminal S71 and I72 residues are pharmacophore modeling provides an effective especially significant for conferring molt inhibiting mechanism for virtual screening. In recent years, activity (Fig. 1) and these two motifs were close to the pharmacophore-based approach has become very each other in the 3-dimensional spatial arrangement36. commanding for the discovery of novel lead Since the binding of MIH with receptors in the compounds and for lead optimization. In the Y-organ suppresses the growth and development in present study, molecular recognition techniques have crustaceans, a transient interference with binding of been employed including HipHop to screen potential MIH to its receptor can promote the growth37-39. drug candidates in silico and manually modified to Most of the CHH family peptides exhibit biological produce novel drug like compounds. Pharmacophore

Fig.1—Multiple sequence alignment of mature Molt inhibiting hormones from representative crustacean species [The residues are numbered from the first residue of mature peptide and the amino acids critical for the activities of MIH are indicated by asterisks (*). The sources for MIH sequences considered for multiple sequence alignment are as follows: CAR- Carcinus maenas (GenBank accession number: CAA53591); CAL- Callinectes sapidus (GenBank accession number: AAA69029); CAN- Cancer pagurus (GenBank accession number: CAC39425); CHAR- Charybdis feriatus (GenBank accession number: AAC64785); ORC- Orconectes limosus (UniProtKB/Swiss-Prot accession number: P83636); PRO- Procambarus clarkii (UniProtKB/Swiss-Prot accession: P55848); PEN- Penaeus japonicus (UniProtKB/Swiss-Prot accession number: P55847); MET- Metapenaeus ensis (GenBank accession number: AAC27452); LIT- Litopenaeus vannamei (GenBank accession number: ABD73292); TRA- Trachypenaeus curvirostris (GenBank accession number: AF312978)] SHRIVASTAVA & PRINCY: MOLT INHIBITING RESPONSE TO GUANYLYL CYCLASE IN CRUSTACEANS 377

modeling has been adopted to rapidly identify new Structural analysis and selection of training sets— potential drugs to overcome dawdling growth by The co-ordinates and the spatial arrangement of interfering with the interaction of MIH with its residues of MIH were obtained from refined X-ray receptor. Pharmacophore models for the MIH- structure of Marsupenaeus japonicus (MJ-MIH) antagonists have been generated using HipHop which was retrieved from the Protein Data Bank algorithm entailing identification and overlay (accession code: 1J0T). The most critical task in the common features. These models are anticipated drug discovery process is developing an appropriate to provide a rational hypothetical picture of the model to predict the activity of given molecules. The primary chemical features responsible for binding dipeptide from position 71 and 72 substantial for of MIH with its cognate receptor, and thus to deliberating molt inhibiting activity were chosen as a supply useful knowledge for developing new active training set and were used in common feature candidates targeting the MIH receptor. pharmacophore generation using HipHop module from Accelrys 2.5 (Fig. 2). Only Material and Methods those residues accountable for binding with receptor Feature delineations and pharmacophore were included because they could alone deliver representation—To logically design and identify critical information on pharmacophore requirements. inhibitors as growth enhancers via a pharmacophore- The molt inhibiting hormone from Carcinus maenas, based design method a precise pharmacophore is Callinectes sapidus and Charybdis feriatus were essential. The absence of published pharmacophore homology-modeled using the solution structure of for competitive inhibitors of the molt inhibiting MIH (PDB: 1J0T) from Marsupenaeus japonicus hormone is a fundamental problem to the design of (unpublished data). The pharmacophore features of novel inhibitors. Using the data from various dipeptides at 71 and 72 positions critical for molt published data regarding MIH concentration inhibiting activity from these MIH structures were required for conferring biological activity and the generated and analyzed. It was observed that the CATALYST common pharmacophore feature isoleucine at 72 position in MIH is highly conserved generation programme, pharmacophore was while 71 position is variable. Hence, the four possible developed for the dipeptide of MIH. When validated combinations which include glutamate, tyrosine, this pharmacophore features was then used for virtual arginine and serine at 71 position along with screening and in silico validation. isoleucine at 72 position were used to generate pharmacophore features. All the four dipeptide combinations were extracted from MIH structure along with their spatial arrangements and the bond orders were verified by applying CHARMm force field. Pharmacophore feature generation and validation—HipHop module of CATALYST which was popularly known for its common feature pharmacophore generation was used as search queries to virtually screen 3D-structural libraries40. CATALYST identified 3D spatial arrangements of chemical features that are common to active in a training set and retrieve structures that fit the hypotheses or as models to predict Fig.2—Pharmacophore mapping of dipeptides from various MIHs the activities of novel compounds41. These four [The common pharmacophore features of dipeptides from various MIHs responsible for binding to receptor in the Y-organs and dipeptide combinations of modeled MIH were selected conferring molt inhibiting activity in decapod crustaceans were as a training set to generate a common generated. The residues at 71 and 72 position were clustered and feature pharmacophore (Hip-Hop) model for pharmacophores were generated from (a) Charybdis feriatus (b) virtually screening competitive inhibitors. Carcinus maenas (c) Marsupenaeus japonicas and (d) Callinectes Two pharmacophore-modeling modules, namely sapidus. Green color indicates hydrogen bond acceptor (HBA); 42 43 brown indicates ring aromatic (HBD); cyan indicates hydrophobic HypoGen and HipHop were incorporated in (Hy) features] CATALYST where HypoGen allows programmed 378 INDIAN J EXP BIOL, APRIL 2014

pharmacophore construction by using an assembly Database virtual screening and manual of molecules with bioactivities spanning over four alterations—The objective of the virtual screening is orders of magnitude. Alternatively, HipHop creates to reduce of the vast virtual chemical space of small common feature pharmacophores, an ensemble of organic compounds, to screen against a specific target steric and electronic features irrespective to the protein, to a wieldy amount of compound that impede activities of the training compounds and engender three a highest chance to lead to a drug candidate44. dimensional pharmacophores that can be used as The validated hypothesis was used as a query in probes to virtually screen 3D-structural libraries. the screening of 3D conformational molecular Therefore, HipHop was employed to generate common structure MiniMayBridge database. The best and fast feature pharmacophores for competitive inhibitors searching along with flexible or rigid mapping using dipeptide combinations as training set along with method in CATALYST was used for the screening their molar concentrations required for exhibiting the of database which calculates and stores multiple inhibitory activity to ensure the optimal intermolecular conformations for each structure during the database interactions with guanylyl cyclase. Active training set generation process. A large hit list was retrieved members were then assessed on the basis of the due to implementation of flexible screening method. varieties of chemical features they encompass, in Therefore, downstream filters were used to reduce association with the ability to adopt a conformation that the hit list to a manageable size containing the permits those features to be superimposed on a specific most promising hits for in vitro testing. The first filter configuration. The chemical features were analyzed applied was to cluster hits according to their fit and the high-ranking pharmacophores associated with score and molecular weight which allows selecting their conformation models were then conceded to representative compounds from each structure classes. CATALYST hypotheses generation. Hypotheses were clustered for developing more selective models by The top predicted compound then underwent a calculating the distance between residues and this series of manual modifications to improve chemical distance was used as a function of the number of features and the modified compound was reanalyzed common pharmacophore features and the root-mean- with the same parameters that were used previously squared displacement between the matching features. for virtual screening. The hits obtained were further Hydrogen bond acceptor (HBA), hydrogen bond donor filtered for drug-likeness, which is expressed (HBD) and hydrophobic (HY) chemical functions were as physicochemical properties that contribute to selected on the basis of the chemical features of the favorable ADME/Tox profiles to eliminate toxicity 45,46 clustered molecule in the training set that are and poor pharmacokinetics . The ADMET considered to be responsible for a desired biological descriptors were then calculated in Discovery activity (Fig. 3). studio 2.5 which evaluates aqueous solubility, (PPB), percent human intestinal absorption (HIA), ADMET AlogP98, cytochrome P450 (CYP450) 2D6 inhibition, and hepatotoxicity. The predicted compounds from virtual screening and arbitrated by toxicity and pharmacokinetics was selected for further analysis.

Results Pharmacophore feature generation and validation—Pharmacophore models were developed in HipHop module in CATALYST software and these models were considered for further analysis. Molecule in the training set was predicted as active and ten

statically best pharmacophore models were generated. Fig.3—Clustering and validation pharmacophore features [The In the training set, active compound maps the features validated pharmacophore features entails two hydrophobic, three of hydrophobic point (Hy), hydrogen bond acceptor hydrogen bond donors and one hydrogen bond acceptor used for (HBA) and donor (HBD). The residues in the training virtual screening. It is evident from the pharmacophore feature that HBD and Hydrophobic groups are predominantly responsible set map the three hydrogen bond donor feature, which for the binding with GC.] divulges that this feature could be essentially SHRIVASTAVA & PRINCY: MOLT INHIBITING RESPONSE TO GUANYLYL CYCLASE IN CRUSTACEANS 379

accountable for the high molecular bioactivity, and comprises of two algorithms in which the “Fast thus should be taken into account in discovering and Flexible Search Database” calculates already manually modifying novel competitive inhibitors for existing conformers of the database, and the Best MIH receptor47,48. The common features from all the Flexible Search Databases is capable of changing pharmacophore hypotheses were analyzed, clustered the conformation of a molecule during computation. and ranked. Interestingly all the pharmacophore Fifty six diverse and drug-like inhibitors were hypothesis comprises of very similar chemical screened with the pharmacophore model by means features. The best hypothesis (Hypo-1) comprised of of “Best Flexible Search Database” selection, and six features: two hydrophobic (Hy), three hydrogen a maximum of 100 conformers per compound were bond donor (HBD), and one hydrogen bond acceptor generated. Despite the use of various filters, (HBA) (Fig. 3). HipHop attempts to construct the the screened compounds were not particularly simplest hypotheses where tolerance sphere describes drug like. Consequently, the top ranked molecules, the expanse in space that should be engaged by CI-1, CI-4 and CI-7 retrieved from virtual screening a specific kind of chemical functionality. Among were carefully chosen for further analysis and manual the ten hypotheses generated automatically by modification. Manual alterations were guided by CATALYST, the first ranked molecule has been pharmacophore features, fit score, shown to have the acceptor–donor feature in proper and pharmacokinetic properties. Ten drug-like spacing. Therefore, this pharmacophore can elicit compounds were successively created by making most of the compounds having acceptor, donor and manual modifications in which only CI-5 and CI-6 hydrophobic features together from the database demonstrated better result (Fig. 4). For both the which would enable the binding with receptor. These procedures, only those structures that map at compounds were mapped to the pharmacophore map least three features of the pharmacophore template and had a fit value of 3.6 which mean that a minimum were retrieved. Subsequently, fourteen ligands of four out of six features can map well with training were selected that passes the second filter of compound. A low fit value shows that the centers of high throughput screening which eliminates the functional groups of the compounds are displaced molecules with unfavorable physicochemical and from the centers of the corresponding pharmacophore pharmacokinetic properties based on the Lipinski’s features even though the chemical functions of rule of five. Out of the 14 ligands analyzed, seven the individual hits overlay with the corresponding (Fig. 5) were predicted to be free from toxicity pharmacophore. Subsequently, eliminating poor using toxicological endpoints such as Daphnia magna overlapping compounds to the pharmacophore model EC50, mutagenicity, carcinogenicity, chromosome advances the success rate of the retained bio-active damage and skin sensitization50. compounds exhibiting comparable biological The selection of best competitive inhibitor effects. The highly active compound should possess depends upon the better fit values, various molecular chemical assemblies that perfectly overlap (map) with corresponding features. Hence, the same criteria were applied in database searching; the fits were successively fitted against hypo-I and those having a fit value below 3.6 were not allowed to pass the second filter of the virtual screening. The filters ensure the reliability of the pharmacophore hypotheses in virtual screening of the commercial databases. However, acquaint with an extra filter to reduce the hits and rank the compounds to be screened in vitro is essential as the pharmacophore searching produces too many compounds in the specified fit value standards.

Virtual screening, alteration and ranking of molecules—A library of commercially available Fig.4—Comparison of common pharmacophore feature of the clustered residues with CI-4 [The competitive inhibitor (CI-4) was chemical compounds supplied by Accelrys was analyzed for 3-D spatial arrangement with the pharmacophore screened for MIH receptor inhibitory activity by a features of MIH binding site which illustrates a higher degree of 49 virtual screening . A database search in CATALYST resemblance.] 380 INDIAN J EXP BIOL, APRIL 2014

. Structure based drug discovery in case of membrane receptors using virtual screening is not a conventional method since the geometry of active site is unknown. In such cases, based common pharmacophore feature generation and virtual screening is the key strategy and attention53-56. Several validation results from drug development using common feature pharmacophore (Catalyst/ HipHop) have provided authenticity of programmed ligand based pharmacophore generation. The HipHop module is successfully used in designing several drugs such as proliferation inhibitors for mesangial cell57, pharmacophore modeling of human CYP17 58 59 inhibitors , inhibitors for tyrosine kinase , novel cyclooxygenase-2 selective inhibitors60 etc. which Fig.5—Representative structures of competitive inhibitors for demonstrates the validity and consistency of these MIH receptor [The CI-1, 2, 3, 4 and 6 were acquired from high methods. The comparative studies have also revealed throughput screening from the database, whereas CI-5 and CI-7 were designed by manual modifications of CI-6.] that the accuracy and success of the drug designed using the HipHop module is highly comparable properties, toxicity. The CI-4 exhibits the highest fit with any other software used for the similar purposes. value but possess higher number rotatable bonds On the basis of these and several other studies, while CI-1 has higher number of acceptors and the method was preferred to design first-of-its kind rotatable bonds which is contrary to the Lipinski’s growth enhancers, for which a pharmacophore model rule of five. The variation in the fit values of CI-5 was generated as features and used as a 3-dimensional and CI-7 is due to the difference in the position of probe to explore the database. To enhance the halides. In CI-5, chlorine atom is attached to the specificity of the compound and reducing the toxicity, ortho-position whereas bromine is attached to the the compounds were successfully modified with the meta-position in the CI-7. Bromine being less certainty that the molecules can be synthesizable. electronegative than chlorine may provide appreciable more hydrophobic character to the nucleus. In CI-7, In decapod crustaceans, one of the key therapeutic presence of three nitrogen atom in the alternate strategies is to impede the biological activity of MIH, position of the chain viz. nitrogen from thiourea and hence, to upsurge the ecdysteroid level in the and the adjacent amide group benefit the molecule hemolymph. The findings obtained from the present to map with the pharmacophore features. Evidently, study are practically helpful to increase the production competitive inhibitors such as the CI-4 described here by controlling molt in crustaceans. The results from epitomize powerful research tools and can be used the study of chemical features of dipeptide of MIH to assess the possible regulatory roles of MIH reveal that Hypo1 has a remarkable capability in various physiological processes. Moreover, the to design novel competitive inhibitors and it was homology models of CHH has also suggested that observed that hydrogen bond donors and hydrophobic the design of a new series of inhibitors for MIH pockets play a key role in MIH selectivity. With receptor is highly specific and had no measurable the intention to validate the key pharmacophore affinity for other CHH family neurohormones. feature, several homology models were generated from different species of decapod crustaceans and Discussion validated. The Hypo-2 where the HBD features were GPCR (G-protein coupled receptor) and membrane removed failed to produce the result as Hypo1. embedded receptors are gaining more importance as Therefore, it can be concluded that the HBD group in researchers continue to explore novel therapeutic the binding site of MIH (dipeptide) plays a key role in targets in various species51,52. Though, obtaining the MIH selectivity. Hence, Maybridge and the database crystal structures for these receptors may be of manually altered molecules were screened using impossible due to their large and highly dynamic Hypo1 and the active hits from these databases structures, creating pronounced challenges in rational were subsequently ranked based on the fit value, SHRIVASTAVA & PRINCY: MOLT INHIBITING RESPONSE TO GUANYLYL CYCLASE IN CRUSTACEANS 381

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