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Arabian Journal of Chemistry (2020) 13, 5107–5117

King Saud University Arabian Journal of Chemistry

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ORIGINAL ARTICLE Pharmacoinformatics and molecular dynamic simulation studies to identify potential small-molecule inhibitors of WNK-SPAK/OSR1 signaling that mimic the RFQV motifs of WNK kinases

Mubarak A. Alamri

Department of Pharmaceutical Chemistry, College of Pharmacy, Prince Sattam Bin Abdulaziz University, P.O. Box 173, Al-Kharj 11942, Saudi Arabia

Received 14 January 2020; accepted 17 February 2020 Available online 21 February 2020

KEYWORDS Abstract The WNK-SPAK/OSR1 signaling is a complex of serine and threonine protein kinases Pharmacophore; that involves in the regulation of human blood pressure. The WNK kinases phosphorylate and MD simulation; activate SPAK and OSR1 kinases through the interaction of RFQV motifs of WNK kinases with SPAK; the C-terminal domains of SPAK and OSR1. Upon phosphorylation, SPAK and OSR1 phospho- + + + OSR1; rylate key ion co-transporters such as Na -[K ]-2Cl (NKCC1-2) and K -Cl (KCC1-4), which are Virtual screening; essential for electrolytes balance and blood pressure regulation. Targeting the binding site of the WNK RFQV motifs of WNK kinases on the C-terminal domain (CTD) of SPAK and OSR1 has emerged as a valuable approach to inhibit the WNK-SPAK/OSR1 signaling pathway. Herein, an effort has been intended to pinpoint non-peptidic small-molecules that could disrupt the binding of SPAK/OSR1 to WNK kinases, hence, inhibit the SPAK and OSR1 phosphorylation and activation by WNK kinases through pharmacoinformatics and molecular dynamic simulation methodologies. A sequential structure-based virtual screening of a focus protein-protein interaction chemical library composed of 11,870 compounds lead to the identification of three compounds having good lead-compound properties with respect to their predicted inhibitory constants, pharmacophore fit scores, binding affinities, ADME-T parameters, drug-likeness properties and ligand efficiency metrics. The mechanism of interaction and binding stability of these compounds to OSR1-CTD were confirmed using molecular docking and dynamic simulation studies. Hence, the identified

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Production and hosting by Elsevier https://doi.org/10.1016/j.arabjc.2020.02.010 1878-5352 Ó 2020 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). 5108 M.A. Alamri

compounds may have therapeutic potential as novel antihypertensive agents subjected to experi- mental validation. Ó 2020 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction The virtual screening is a computational approach that is utilized in the early-stage drug discovery campaign to search The WNK-SPAK/OSR1 signaling is defined as a master regu- chemical databases for novel bioactive molecules against the lator of human blood pressure (Alessi et al., 2014). In 2001, the target of interest in timely and cost-effective way (Sliwoski first link between this signaling cascade and hypertension was et al., 2014). Generally, two distinct classes of virtual screening reported when an inherited form of hypertension in humans can be used, ligand-based and structure-based virtual screen- known as ‘‘Gordon’s syndrome” was found to results from ing, depending on the available information regarding the mutations of the genes that encoded for WNK (with no lysine), ligands and three-dimensional (3D) structure of the target, serine/threonine protein kinases (Wilson et al., 2001). Subse- respectively (Aparoy et al., 2012). Additionally, the pharma- quent biochemical studies showed that WNK kinases phos- cophore modeling is one of the significant tools in modern phorylate and activate two other intermediate serine/ drug discovery. It is defined as the process of identification threonine protein kinases namely, SPAK (SPS1-related of electronic and steric chemical features that are essential proline/alanine-rich kinase) and OSR1 (oxidative stress- for optimal interaction between a ligand and its target. The responsive kinase 1) kinases (Moriguchi et al., 2005). Active 3D pharmacophore model can be used as queries for SPAK and OSR1 in complex with Mo25, a scaffolding pharmacophore-based virtual screening, de novo design and protein, were found to regulation the function of key cation- lead optimization (Khedkar et al., 2007). The main focus of chloride cotransporters (CCCs) such as the Na/K/Cl co- this presented study is to screen Asinex protein-protein interac- transporters 1 and 2 (NKCC1/2), the Na/Cl co-transporter tion database for identification of novel binders of OSR1/ (NCC) and the K/Cl co-transporters (KCCs) by phosphoryla- SPAK C-terminal domains that could disrupt their interaction (Alessi et al., 2014; Filippi et al., 2011). Generating mouse tions with WNK kinases. The molecular mechanism of inhibi- models expressing an enzymatically inactive form of WNK, tion of obtained inhibitors were explored by molecular SPAK or OSR1 kinases result in a lowered blood pressure docking and molecular dynamic simulation. The good phar- due to the inhibition of CCCs phosphorylation (Hadchouel macodynamic and pharmacokinetic profiles of selected com- et al., 2016). The latter, highlighted the WNK-SPAK/OSR1 pounds suggesting the possibility of them to be potential signaling pathway as a valuable target for development of inhibitors of WNK signaling as a new class of antihyperten- novel class of antihypertensive agents. sion agents. Human SPAK and OSR1 are highly related homologues sharing 68% of their total primary amino acid sequences with 2. Material and methods ambiguous tissue expression profiles (Vitari et al., 2006). In addition to the kinase domain and serine-rich motif, SPAK and OSR1 possess a highly conserved carboxy-terminal The general methodology used in this research is depicted in domain (CTD); which is a 92-amino acids long (residues (Fig. 1). 456–545 for SPAK and 434–527 for OSR1) and is required for the binding of SPAK and OSR1 to the specific RFxV/I 2.1. Generation and validation of pharmacophore model (Arg-Phe-Xaa-Val/Ile) motifs within both upstream WNK kinases and downstream CCCs (Richardson and Alessi, The crystal structure of C-terminal domain (CTD) of OSR1 in 2008; Vitari et al., 2006). Co-crystallization of the CTD of complex with RFQV (Arg-Phe-Asn-Val) peptide derived from OSR1 with RFQV-peptide derived from WNK4 has demon- WNK4 (PDB: 2V3S) was imported into LigandScout software strated that the CTD has two adjacent hydrophobic pockets, from RCSB protein data bank (Villa et al., 2007). The termed primary and secondary pockets (Villa et al., 2007). structure-based pharmacophore was generated using auto- The RFQV-peptide binds to OSR1-CTD through the primary matic pharmacophore generating tool in LigandScout pro- pocket, while the secondary pocket has been suggested as an gram (Wolber and Langer, 2005). The resulted allosteric pocket (AlAmri et al., 2017). NMR structural study, pharmacophore model consists of the whole features involved indicated that the binding of RFQV-peptide to OSR1-CTD in the binding of RFQV peptide residues to the primary pocket induces large conformational changes that effect almost every of OSR1-CTD. STOCK1S-50699, a known WNK and SPAK amino acids within the CTD of OSR1 suggesting the crucial binding inhibitor, was docked (using Autodock vina) and role of this domain in the regulation of whole signaling trans- mapped on the generated pharmacophore model using duction (AlAmri et al., 2019). Targeting the primary pocket LigandScout program to obtain the final pharmacophore with small molecule protein–protein interaction inhibitors model (Mori et al., 2013). The final pharmacophore model has been exploited, however, the identified molecules such as was then validated using the receiver operating characteristic STOCK1S-50699 and STOCK2S-26016 lack the drug- (ROC) curve, with LigandScout software, by screening the likeness properties which hampered their further in vivo stud- pharmacophore model against a set of active and inactive com- ies (Ishigami-Yuasa et al., 2017; Mori et al., 2013). pounds to determine the ability of this pharmacophore to Pharmacoinformatics and molecular dynamic simulation studies 5109

Fig. 1 Graphical representation of in silico approach for the identification of hit molecules. distinguish between these compounds. STOCK1S-50699 and affinities in comparison to STOCK1S-50699 were considered STOCK2S-26016, known WNK-SPAK binding inhibitors, for further analysis. were used as active compounds. The two compounds were also used to generate the decoy set of 100 inactive compounds (50 2.4. In silico ADME-T analysis compounds per each) using DUD-E webserver (http://dude.docking.org/generate)(Mysinger et al., 2012). pkCSM server was used to evaluate the absorption, distribution, metabolism and excretion- toxicity (ADME-T) parame- 2.2. Pharmacophore-based virtual screening ters for the identified hit compounds (Pires et al., 2015). For the compound to be selected as a hit, it must be non- In silico pharmacophore-based virtual screening was per- hepatotoxic and non-carcinogenic. SwissADME was used to formed with ‘‘Asinex focused protein–protein interaction assess other physiochemical properties of these hit compounds ” (PPI) library having 11,870 small-molecules against the gener- (Daina et al., 2017). ated pharmacophore model using LignadScout software. The library contains non-macrocyclic compounds with a diversity 2.5. Calculation of ligands efficiency metrics and inhibition of more than 500 scaffolds. The library was obtained from constants (https://www.asinex.com/ppi/) in sdf format and was converted into Idb using LigandScout library generation tool. The compounds that meet all pharmacophore features were The inhibition constants (Ki) of hit compounds were predicted considered as hit compounds and ranked based on their from Autodock vina binding energy scores using Eq. (1) pharmacophore-fit scores which reflect to which degree the (Edwards and Price, 2010; Hopkins et al., 2004; Hopkins molecules fit the pharmacophore features. et al., 2014; Murray et al., 2014; Reynolds et al., 2007). ½BindingEnergyðBEÞ1:366 Ki ¼ 10 ð1Þ 2.3. Docking-based virtual screening The ligand efficiency parameters were estimated using the following equations: (Edwards and Price, 2010; Hopkins The retrieved hit compounds from pervious screening was sub- et al., 2004, 2014; Murray et al., 2014; Reynolds et al., 2007). jected to docking-based virtual screening against the 3D structure of OSR1-CTD (PDB: 2V3S) using Autodcok Vina in LE ¼BE HA ð2Þ PyRx 0.8 program (Dallakyan and Olson, 2015). Before dock- 0:026HA ing, hit compounds were energy minimized and converted LEscale ¼ 0:873e 0:064 ð3Þ from sdf files into pdbqt files using Open Babel tool in PyRx 0.8 program (O’Boyle et al., 2011). The grid box was cantered LLE ¼LogKi LogP ð4Þ to cover the amino acid residues involved in the topology of ¼ ð Þ the primary pocket of OSR1-CTD. Prior screening, FQ LE LEscale 5 STOCK1S-50699 was added to the database as a control. ¼ ð Þ Compounds that bind to OSR1-CTD with high binding LELP logP LE 6 5110 M.A. Alamri

In which LE, LEscale, LLE, FQ and LELP are stand for were added to neutralize the system. The system was then equi- Ligand Efficiency, Ligand Lipophilic Efficiency, Ligand Effi- librated and energy minimized using steepest decent algorithm ciency Scaled, Fit Quality and Ligand Efficiency Lipophilic with tolerance value of 1000 kJ mol1 nm1 followed by equi- Price, respectively. libration using NVT and NPT ensemble for 100 ps. Bond lengths and electrostatic calculations were constrained using 2.6. Pharmacophore mapping of hit compounds LINear Constraint Solver (LINCS) algorithm and particle mesh Ewald method, respectively (Essmann et al., 1995; Hess Hit compounds were mapped into the generated pharma- et al., 1997). MD simulation was carried out for 20 ns MD pro- cophore model using the alignment tap in LigandScout duction with a time step of 2 fs (femto-second) at the constant software. pressure of 1 atm and constant temperature of 300 K and snapshots saves every 2 pico-second (ps). Several parameters 2.7. Molecular docking study included root-mean-square deviation (RMSD), root-mean- square fluctuation (RMSF) and radius of gyration (Rg) were analyzed using GROMACS to determine the conformational Hit compounds fulfilling the pervious filters were docked and performance stability of each complex system in the against the 3D structure of OSR1-CTD using Autodock vina dynamic environment. program (Trott and Olson, 2010). The protein structure (PDB: 2V3S) was obtained from RCSB protein data bank (Villa et al., 2007). The structure was solved at an X-ray reso- 3. Results and discussion lution of 1.7 A˚and it was composed of dimers of OSR1-CTD in complex with RFQV peptide. Discovery studio 4.5 (Accel- 3.1. Pharmacophore model generation and validation rys, San Diego, CA, USA) was used to remove the unwanted water molecules and ligands as well as to generate the pdb files A structure-based pharmacophore model was generated based for the protein in monomer form. Autodock tools program on the crystal structure of the RFQV (Arg-Phe-Asn-Val) pep- was used to generate the pdbqt files and to prepare the gridbox tide derived from WNK4 binding to the C-terminal domain for the docking configuration files (Sanner, 1999). The gridbox (CTD) of OSR1 (Fig. 2)(Villa et al., 2007). was centered to cover the primary pocket with the following Based on the interaction of RFQV motif with the primary parameters; the box size of x = 14 y = 14 z = 22 and the pocket of OSR1-CTD, the extracted pharmacophore features box center: x = 1.605 y = 11.139 z = 23.381. Discovery Stu- consist of eleven hydrogen bond acceptors (HBA), ten hydro- dio 4.5 and PyMOL Molecular Graphics System 1.3 were used gen bond donors (HBD), two hydrophobic (HYD) and one to visualized and analyzed the docking results. positive ionizable features beside twenty-eight exclusion- volume spheres which are the essential regions that determine 2.8. Molecular dynamic (MD) simulation the overall shape of the binding pocket (Figs. 3A and 1B). Since the using of the peptide-based pharmacophore model The dynamic behavior of docked inhibitor-OSR1-CTD com- for screening of small-molecules library may result in identifi- plexes was evaluated via all-atom MD simulation for 20 ns cation of no hit compounds as the pharmacophore-based using GROMACS 2018.1 package (Hess et al., 2008). The screening becomes inefficient with more than eight features, a topology files of all docked-inhibitors were obtained using rational method to reduce the number of pharmacophore fea- SwissParem tool (Zoete et al., 2011). The OPLS-AA/L force tures is needed (Jung et al., 2018). Therefore, a known WNK field was applied to the system to carry out the MD simulation. and SPAK binding inhibitor with a Kd value of 37 mM, namely A triclinic water box of TIP3P water model molecules STOCK1S-50699, was docked and mapped onto the generated (Jorgensen et al., 1983) with 1.0 nm distance from the edge pharmacophore model to identify the most important interac- of the box to protein was surrounded to each complex of tion features for optimal binding to OSR1-CTD (Figs. 3C and protein-ligand system. A suitable numbers of counter ions 1D) (Mori et al., 2013). Five pharmacophore features were

Fig. 2 Crystal structure of OSR1-CTD (PDB: 2v3s): (A) Ribbon representation of OSR1-CTD in complex with RFQV peptide-derived from WNK4 (yellow). a- helices and b- sheets were shown in cyan and purple colors, respectively. (B) Molecular surface representation of OSR1-CTD (white) shown the binding mode of RFQV peptide-derived from WNK4 (yellow) to the primary pocket. Pharmacoinformatics and molecular dynamic simulation studies 5111

Fig. 3 Generation of pharmacophore model. Pharmacophore model was generated based on the interaction of RFQV peptide derived from WNK4 kinase with the CTD of OSR1 kinase (PDB:2V3S). (A) Interaction of RFQV peptide with OSR1-CTD. (B) Structure-based Pharmacophore model that was generated using LigandScout program. (C) Interaction of STOCK1S-50699, WNK-SPAK binding inhibitor, with OSR1 C-terminal domain. (D) Mapping the RFQV (cyan) and STOCK1S-50699 (pink) onto the generated structure-based pharmacophore model. The HBA, HBD, H, and PI represent hydrogen bond acceptor, hydrogen bond donor, hydrophobic and pi-pi interaction, respectively. The pharmacophore features were deﬁned in LigandScout by colour codes; red, green, yellow, blue and grey spheres which represent hydrogen bond acceptor, hydrogen bond donor, hydrophobic, positive ionizable group and exclusion volume, respectively.

identified; three hydrogen bond acceptors and two hydropho- 3.2. Combined virtual screening of protein–protein interaction bic regions. This pharmacophore model beside all of the (PPI) library exclusion-volumes were considered as the final structure- based pharmacophore model (Fig. 4A). Notably, the distances The validated structure-based pharmacophore model was used between the pharmacophore features are large which reflect the as a 3D query for the pharmacophore-based virtual screening size of typical protein-protein interaction binding pockets of ‘‘Asinex focused protein-protein interaction library” having (Voet et al., 2013). To estimate the performance of pharma- 11,870 small-molecules. Of these compounds, 150 hit com- cophore model, the pharmacophore model was screened pounds were found to meet the pharmacophore query features against a set of active and decoy compounds to determine its with a hit rate of 1.3%. The pharmacophore-fit scores of these ability to correctly recognized a list of compounds as actives compounds was in between 54.85 and 58.31. In the next step, or inactive (decoys). The ROC analysis which is indicated by these hit compounds were subjected to docking-based virtual the area under the curve (AUC) as well as enrichment factor screening against the 3D structure of OSR1-CTD to filter them (EF) values showed that the pharmacophore yielded a ROC further based on the free energy binding score. From this exer- score of 0.86 which means that a randomly-selected-active cise, 31 hit compounds were identified to bind to the OSR1- compound has a higher score than a randomly-selected- CTD with high docking scores in comparison to the binding decoy 8.6 times out of 10 (Fig. 4B). Since the value of AUC score of STOCK1S-50699, a known SPAK/OSR1 inhibitor, was beyond 0.5, the pharmacophore performed good in distin- which was used as a control. The docking scores for these com- guishing between active and inactive samples within the pounds were between 1.8 and 9 Kcal/mol. screened dataset. The EF values of the set screened by the pharmacophore at 1%, 5%, 10%, and 100%, were 0.0, 10.2, 3.3. ADME-T properties 5.1, and 2.4, respectively. The pharmacophore retrieved the two active (100%) compounds from screen dataset, corrobo- The determination of the absorption, distribution, metabolism rating the ROC statistics. and excretion-toxicity) (ADME-T) parameters is a significant 5112 M.A. Alamri

Fig. 4 The ﬁnal pharmacophore model and its performance. (A) The ﬁnal pharmacophore model. The pharmacophore features were shown in LigandScout by colour codes; red and yellow spheres which represent hydrogen bond acceptor and hydrophobic, respectively. (B) The performance of the pharmacophore model by ROC curves using the Directory of Useful Decoys (DUD) dataset. The ROC plot was generated using LigandScout.

Fig. 5 Chemical structures of candidate compounds. A, B and C are hit 1, 2 and 3 respectively. step in the early phases of drug discovery. Computational tools have provided useful and efﬁcient measurements of these Table 1 ADME-T properties of hit compounds. ADMET parameters in time- and cost effective manners Parameter Hit 1 Hit 2 Hit 3 (Hughes et al., 2011). The key properties were measured for Absorption & Distribution the 31 compounds. Among the 31 compounds, only non- BBB+ No Yes No hepatotoxic and non-carcinogenic compounds were selected HIA 85.059 94.219 89.239 and three hit compounds were obtained (Fig. 5). The summary Aqueous solubility (LogS) 5.72 5.40 4.53 of their ADME-T properties is depicted in Table 1. Impor- Bioavailability Score 0.55 0.55 0.55 tantly, hit 2 has the probability of crossing the blood brain Metabolism barrier (BBB+) while the other two hits (hit 1 and 3) were CYP 2D6 Inhibitory No No No not. It was also observed that hit 2 has the highest probability Promiscuity of being absorbed by human intestine (94.219%), then hit 2 Toxicity (89.239%) and then hit 3 (85.059%). The aqueous solubility Hepatotoxicity No No No is critical property for drug oral activity as well as for pharma- Acute Oral Toxicity (mol/kg) 2.434 2.666 3.595 ceutical preparation. The values of aqueous solubility for the Carcinogenicity No No No identiﬁed hit compounds were within the standard range which (0.71) (0.59) (0.68) should be between 1 and 5 (Tsaioun and Kates, 2011). The BBB+; blood brain barrier, HIA; human intestine absorption. bioavailability scores for all hit compounds were 0.55 meaning that the compounds may have >10% bioavailability in rat (Martin, 2005). Therefore, these values indicated that the hit compounds may have good absorption and distribution properties. The cytochrome P450 (CYP2D6) is a key enzyme tively. The LD50 values were expressed in mol/kg according responsible for metabolism of >25% of currently available to the standard practice of QSAR in which each 1/mol/kg is drugs (Pirmohamed and Park, 2003). Interestingly, the hit corresponding to ~500 mg/kg (Raevsky et al., 2018). Accord- compounds were predicted to have no inhibitory effect on this ingly, all hit compounds fall into class II labelled as ‘moder- enzyme. The predicted LD50 values for the hit compounds ately toxic’ as they have LD50 values of ~1200, 1300 and were 2.434, 2.666 and 3.595 mol/kg for hit1, 2 and 3, respec- 1800 mg/kg for hit1, 2 and 3, respectively. Pharmacoinformatics and molecular dynamic simulation studies 5113

3.4. Physicochemical properties measurement and bioactivity Table 3 Bioactivity prediction of the hit compounds. prediction of the hit compounds Bioactivity parameter Hit 1 Hit 2 Hit 3 The physicochemical properties provide a deeper insight into AutoDock Vina docking score (kcal/mol) 6.3 6.3 5.8 m the drug-likeness of a drug molecule. Lipinski’s rule of five is Ki ( M) 24.43 24.43 56.75 a famous method to evaluate the drugability of compounds Ligand Efficiency (LE)/(kcal/mol/heavy 0.196 0.190 0.187 atom) which states: For a given molecule, the number of hydrogen- LESCALE 0.316 0.306 0.326 bond donors (HBD) and hydrogen-bond acceptors (HBA) Fit Quality (FQ) 0.620 0.620 0.573 must not be greater than 5 and 10, respectively, the molecular Ligand Lipophilic Efficiency (LLE) 3.38 3.13 1.92 mass and logP should not exceed 500 g/mol and 5, respectively Ligand-efficiency-dependent lipophilicity 24.33 23.79 19.68 (Lipinski, 2016). The summary of the physicochemical proper- (LELP) ties of the identified hit compounds is illustrated in Table 2. The results indicated that all hit compounds obey the Lipin- ski’s rule of five. The number of rotatable bonds is known to modulate the bioavailability of compounds and it should not the identified three hit compounds were lower than the recom- be more than 7 (Veber et al., 2002). All three hit compounds mended limit Table 3. This phenomenon was also observed met this standard except hit 3 that has a value of 9 for number with STOCK1S-50699 which has LE = 0.19. The FQ value of rotatable bonds. The range for refractivity is another is directly affected by LE value which results in lower value parameter of drug likeness which recommended to be between of FQ for the three hit compounds (Hopkins et al., 2004). In 40 and 130 (Ghose et al., 1999). None of the compounds vio- another hand, the values of ligand lipophilic efficiency (LLE) late this limit except hit 2 that has slightly high value of 132.72. for the hit compounds were within the standard range Moreover, the three hit compounds have ideal polar surface (Hopkins et al., 2004). Furthermore, the Ligand Efficiency area (PSA) values which is recommended to be less than 140 Lipophilic Price (LELP) measures the ligand efficiency in term for optimal drug absorption and distribution (Cerqueira of lipophilicity of compounds and it should be between 10 et al., 2015). and 10 for a given lead compound (Hopkins et al., 2004). The binding energy scores of the three hit compounds were Obviously, the three hit compounds showed higher values of used to calculate the inhibitory constant (K ) in micromolar i LELP due to the lower values of LE. The LELP value was also concentration range (mM) Table 3. The K value determines i high for STOCK1S-50699 (LELP = ~42). the activity of compounds, typically it should be in a micromolar concentration range for a lead compound as well as in a low 3.5. Pharmacophore mapping nanomolar concentration range for a drug (Hughes et al., 2011; Stevens, 2014). The calculated Ki values for the three compounds are 24.43-, 24.43- and 56.75 mM, respectively. The hit compounds remarkably mapped well onto all the fea- Therefore, these compounds can be defined as lead compounds tures of the pharmacophore model (Fig. 6). Table 3 showed the for discovery of WNK-SPAK/OSR1 signaling inhibitors. pharmacophore fit scores which represents how well a com- In term of ligand efficiency metrics, a qualified hit should pound maps to the pharmacophore; the higher fit score indi- possess a threshold value of 0.3, 3, and 0.8 for the ligand Effi- cates a better fit to the pharmacophore model and the ciency (LE), ligand lipophilic efficiency (LLE), and fit quality molecules with high values should be active as WNK and (FQ), respectively (Hopkins et al., 2004; Hopkins et al., SPAK/OSR1 binding inhibitors. 2014; Murray et al., 2014). Due to the nature of the primary pocket which has been showing to be a surface exposure bind- 3.6. Molecular interactions and binding modes ing site, the in silico binding energy of ligands are expected to be low (Villa et al., 2007). Consequently, the value of LE for To analyze the binding modes as well as the type of interactions of the hit compounds with the CTD of OSR1, a molecular docking was performed using Autodock Vina (Trott and Olson, 2010). Hit 1 exhibits conventional and carbon hydrogen Table 2 Molecular properties of the hit compounds. bonds with Arg451 and Ile450, respectively, Pi-alky bonds Molecular Hit 1 Hit 2 Hit 3 with Leu473, Ala 471 and Val 464 and Pi-anion interaction property with Glu467 (Fig. 7A). The molecular interactions of hit 2 include conventional and carbon hydrogen bonds with Formula C25H22N4O2SC27H30N2O4 C24H33N3O3S Mass 442.53 446.54 443.6 Glu453 and Phe542, respectively and Pi-alky interactions with ClogP 4.77 4.52 3.68 Leu473 and Ile450 (Fig. 7B). Hit 3 involves in conventional HBA 3 5 4 hydrogen bond with Arg451 and Pi-alky bonds with Ala471, HBD 1 0 1 Leu468 and Ile450 (Fig. 7C). Interestingly, all hit compounds Rotatable bounds 6 7 9 adapt similar binding mode in the primary pocket of OSR1- Polar Surface Area 97.16 60.89 90.12 CTD (Fig. 7D–F). Mutation and NMR binding studies, indi- ˚2 (PSA)/A ) cated that most of the residues involve in the interactions with Rule of five 000 the identified hits such as Leu473, Arg451, Ile450, Ala471 have violations Refractivity 128.58 132.72 129.04 been shown to be essential for the binding of RFQV peptide to Heavy atoms (HA) 32 33 31 the CTD of OSR1 (AlAmri et al., 2019, 2017; Villa et al., 2007; Vitari et al., 2006). 5114 M.A. Alamri

Fig. 6 Mapping the three hit compounds onto the structure-based pharmacophore model. A, B and C are for hit 1, 2 and 3, respectively. Pharmacophore features were represented by colour codes; red and yellow spheres which indicated hydrogen bond acceptor and hydrophobic, respectively.

Fig. 7 The molecular interactions and binding modes of hit compounds with the CTD of OSR1. Ribbon representation of the interaction of (A) hit1 (cyan) (B) hit 2 (pink), (C) hit 3 (blue) with OSR1-CTD. The type of interactions was illustrated in green, light green, pink and yellow which represent conventional hydrogen bond, carbon hydrogen bond, hydrophobic (Pi-Alkyl) and Pi-Anion type of interactions. Molecular surface representation of the binding of (D) hit 1 (cyan) (E) hit 2 (pink), (F) hit 3 (blue) to the primary pocket of OSR1-CTD. The co-crystal RFQV peptide-derived from WNK4 is shown in green.

3.7. MD simulation each residue (Fig. 9). The resultant RMSF values showed no significant fluctuations were observed at the ligand binding The dynamic stability and behavior of each docked-inhibitor- sites in the docked-inhibitor-OSR1-CTD complexes with fluc- ˚ OSR1-CTD complex was explored through a 20 ns molecular tuations values ranged from 0.05 to 0.15 A. The high RMSF dynamic simulation study. The root-mean-square deviation peaks were observed in the loop regions formed by residue (RMSD), root-mean-square fluctuation (RMSF) and radius Glu507, Gly508, Ser509, Asp510 and Ile511. Notably, the lat- of gyration (Rg) were calculated for the protein backbone. ter effect was the least with compound hit 3 with RMSF value The results of calculated RMSD of docked complexes showed of 0.15 compared with RMSF values of 0.30 and 0.25 for hit 1 that after sharp raised in the RMSD values at the beginning of and 2, respectively (Fig. 9). the MD simulation all system reach equilibrium after ~1.5 ns To evaluate the structural stability and compactness of pro- for hit 1 and 2 and after ~2.5 ns for hit 3 with average RMSD tein, the radius of gyration (Rg) was calculated (Fig. 10). The values of 0.13 ± 0.02, 0.14 ± 0.02 and 0.14 ± 0.02 A˚for hit 1, Rg of protein backbone for the docked-inhibitor-OSR1-CTD 2 and 3, respectively. (Fig. 8). complexes showed stable behavior with values between 1.25 ˚ The RMSD results were all lower than 0.2 A˚indicating a and 1.33 A throughout the simulation at 20,000 ps. However, stable dynamic behavior for the last 18.5 ns for hit 1 and 2 compound hit 2 showed sharp fluctuation in the Rg values and 17.5 ns for hit 3. Due to the critical role of individual after 10,000 ns and then remained stable through all the rest amino acid in the stability of ligand inside the binding pocket, of simulation period. This could suggest that the hit 2 may the RMSF value of was calculated to explore the flexibility of adapt a new conformation within the binding pocket. The Pharmacoinformatics and molecular dynamic simulation studies 5115

Fig. 8 RMSD (A˚) vs time (ns) of OSR1-CTD backbone obtained from complexes of OSR1-CTD-screened inhibitors.

Fig. 9 RMSF (A˚) vs residue number of OSR1-CTD when bound to ﬁnal screened inhibitors. results of MD simulation indicate that the docked-inhibitor- 4. Conclusion OSR1-CTD complexes remained stable with favorable conformations throughout 20 ns suggesting that the identify In conclusion, the present work was conducted to pinpoint inhibitors were stable at the active site of OSR1-CTD during novel non-peptidomimetic inhibitors of WNK-SPAK/OSR1 the interactions. signaling pathway by targeting the CTD of SPAK/OSR1 5116 M.A. Alamri

Fig. 10 Radius of gyration (A˚) vs time (ns) obtained from complexes of OSR1-CTD-screened inhibitors.

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