J Mol Model (2014) 20:2156 DOI 10.1007/s00894-014-2156-1
ORIGINAL PAPER
Homology modeling, molecular dynamics and atomic level interaction study of snake venom 5′ nucleotidase A. Syed Yasir Arafat & A. Arun & M. Ilamathi & J. Asha & P. R. Sivashankari & Cletus J. M. D’Souza & V. Sivaramakrishnan & B. L. Dhananjaya
Received: 31 July 2013 / Accepted: 24 January 2014 # Springer-Verlag Berlin Heidelberg 2014
Abstract 5′ Nucleotidase (5′ NUC) is a ubiquitously distributed enzyme known to be present in snake venoms (SV) that is responsible primarily for causing dysregulation of physiological homeostasis in humans by inducing anticoagulant effects and by inhibiting platelet aggregation. It is also known to act synergistically with other toxins to exert a more pronounced anti-coagulant effect during envenomation. Its structural and functional role is not yet ascertained clearly. The 3D structure of snake venom 5′ nucleotidase (SV-5′ NUC) is not yet known and was predicted by us for the first time using a comparative homology modeling approach using Demansia vestigiata protein sequence. The accuracy and stability of the predicted SV-5′ NUC structure were validated using several A. Syed Yasir Arafat and A. Arun contributed equally to this work. B.L. Dhananjaya and V.Sivaramakrishnan are equal corresponding authors. Electronic supplementary material The online version of this article (doi:10.1007/s00894-014-2156-1) contains supplementary material, which is available to authorized users. A. S. Y. Arafat : A. Arun : M. Ilamathi : J. Asha : V. Sivaramakrishnan (*) Department of Bioinformatics, School of Chemical and Biotechnology (SCBT), SASTRA University, Thirumalaisamudram, Thanjavur 613401, India e-mail:
[email protected] J. Asha : P. R. Sivashankari : B. L. Dhananjaya (*) Department of Research, School of Chemical and Biotechnology (SCBT), SASTRA University, Thirumalaisamudram, Thanjavur 613401, Tamilnadu, India e-mail:
[email protected] C. J. M. D’Souza Department of Studies in Biochemistry, University of Mysore, Mysore 570006, India B. L. Dhananjaya Toxinology/Toxicology and Drug Discovery Unit, Center for Emerging Technologies (CET), Jain University, Kanakapura, Ramanagara 562112, Karnataka, India
computational approaches. Key interactions of SV-5′ NUC were studied using experimental studies/molecular docking analysis of the inhibitors vanillin, vanillic acid and maltol. All these inhibitors were found to dock favorably following pharmacologically relevant absorption, distribution, metabolism and excretion (ADME) profiles. Further, atomic level docking interaction studies using inhibitors of the SV-5′ NUC active site revealed amino acid residues Y65 and T72 as important for inhibitor–(SV-5′ NUC) interactions. Our in silico analysis is in good agreement with experimental inhibition results of SV-5′ NUC with vanillin, vanillic acid and maltol. The present study should therefore play a guiding role in the experimental design of new SV-5′ NUC inhibitors for snake bite management. We also identified a few pharmacophoric features essential for SV-5′ NUC inhibitory activity that can be utilized further for the discovery of putative anti-venom agents of therapeutic value for snake bite management. Keywords 5′ Nucleotidase . Snake venom . Purines . Vanillic acid . Homology modeling . Molecular dynamics . Molecular docking . Demansia Vestigiata . Envenomation
Introduction Snakebite is a significant public health problem causing considerable morbidity and mortality worldwide, particularly in the tropics. Snakebite is now recognized as a highly neglected tropical disease (NTD) by the World Health Organization (WHO) [1]. The WHO estimates that, globally, at least 421,000 envenomings and 20,000 deaths occur each year due to snakebite [1]. Based on the fact that envenoming occurs in about one in every four snake bites, it is suggested that between 1.2 million and 5.5 million snake bites could occur annually worldwide [1]. Snake venom (SV) is a complex mixture of biologically active components, which have a diverse array of actions on both prey and humans [2, 3].
2156, Page 2 of 10
J Mol Model (2014) 20:2156
Among the other enzymatic toxins, 5′ nucleotidase (SV-5′ NUC) is an enzyme present ubiquitously in SV [2–4], suggesting its central importance in snake envenomation strategies. SV-5′ NUC is known predominantly for its involvement in hemostatic dysfunction, which occurs during envenomation by interacting with factors of intrinsic pathways to bring about an anti-coagulant effect [4] and modulation of platelet function [4–7]. 5′ Nucleotidase is also known to act synergistically in vivo with other toxins like ADPases, phospholipases, and disintegrins, as well as hemorrhagic toxins to exert a more pronounced anti-coagulant effect during envenomation [8, 9]. It is also known to be involved in snake envenomation strategies through the endogenous liberation of purines as “multitoxin” [2, 3, 9, 10]. Recently, it was demonstrated that nucleotides synergistically augment the myotoxic effects of myotoxin-rich SV [11]. Although it is widely accepted that it is involved in modulation of hemostatic functions, other pharmacological roles of SV-5′ NUC are not well characterized and, considering its importance in envenomation, it is imperative to gain insight into the structure of SV-5′ NUC in order to design novel inhibitors against it for better management of snakebite. The structures of inhibitor-enzyme complexes provide ideal platforms for the design of potent inhibitors that will be useful in the development of prototypes and lead to compounds with potential therapeutic applications. Since there is a complete lack of structural information on SV-5′ NUC, predicting its structure using a computer-aided approach is the most plausible preliminary logical option to design novel inhibitors against it. Most commonly used computational structure prediction techniques include de-novo or ab-initio modeling and comparative protein modeling, which again is divided into homology modeling and protein threading [12, 13]. In the present study, we performed homology modeling of Demansia vestigiata 5′ NUC to predict its three-dimensional (3D) structure from its protein sequence. Demansia vestigiata (Black whip) is an Australian venomous snake that belongs to the Elapidae family. The predicted 3D structure model provided, for the first time, potential structural insight/simulation to inhibit SV-5′ NUC by the in silico design of small molecules against it. Furthermore, it is believed that these inhibitors could be used as molecular models for the development of new therapeutic agents in the treatment of Ophidian accidents.
(Trimeresures malabaricus) venom was purchased from Irula, Chennai, India. All other chemicals used were of analytical reagent grade.
Materials and methods
Homology modeling and validation
Chemicals and snake venoms
Homology modeling of the target protein was carried out with MODELLER9v7 and multiple models were generated. The generated models were ranked based upon their discrete optimized protein energy (DOPE) scores and MOLPDF scores [16]. DOPE is a statistical potential used to assess homology models in protein structure prediction. The energy of the
Maltol, vanillin and vanillic acid were purchased from SigmaAldrich (St. Louis, MO). Cobra (Naja naja) venom was purchased from the Haffkins Institute (Bombay, India). Russells viper (Daboia russellii) venom and Pit viper
5′ nucleotidase assay Venom 5′ nucleotidase activity was assayed by the procedure of Avruch and Wallach [14] with slight modifications. In brief, the reaction was carried out in a final volume of 1 ml containing 10 mM MgCl2, 50 mM NaCl, 10 mM KCl, 50 mM TrisHCl buffer pH 7.4, 10 mM 5′ AMP with an appropriate amount of venom and incubated for 30 min at 37 °C. The ascorbic acid method [26] was used to determine the inorganic phosphate released; 1 ml ascorbic acid reagent, containing equal parts of 0.42 % ammonium molybdate in 1 N sulfuric acid, 10 % ascorbic acid and water was added to the assay mixture. This was left at room temperature for 30 min and absorbance at 660 nm was determined. This was quantified by comparison with the reference curve established with KH2PO4. Results are expressed in nmoles of inorganic phosphate liberated min−1 mg-1 of protein. For inhibition studies, venom samples (30 μg) were pre-incubated with vanillic acid (300 μM) or vanillin (300 μM) or maltol (300 μM) at 37 °C for 30 min. The inhibition is expressed as IC50 values. Hardware All computer-based investigation was carried out with a Red Hat 5.3 Linux platform running on a Wipro PC with an Intel dual core processor and 4 GB of RAM. Sequence analysis and physicochemical characterization The SV-5′ NUC sequence with accession number A6MFL8 was retrieved from the Uniprot database and its physicochemical characterization was computed using the Expasy program. A similarity search for SV-5′ NUC in the Protein Data Bank (PDB) was performed using the BLAST server. The crystal structure of human 5′ nucleotidase (H-5′ NUC) PDB ID-2J2C was selected as the template for the target SV-5′ NUC based on its sequence and functional homology. Alignment between the target SV-5′ NUC sequence and the template H-5′ NUC sequence was performed and visualized using ES pript [15].
J Mol Model (2014) 20:2156
protein model generated through MODELLER was assessed by the DOPE score, to ascertain the satisfaction of spatial restraints. The target model having the least DOPE and MOLPDF scores with acceptable statistics from Ramachandran plot was selected further for all other studies. Validation studies were further performed on selected SV-5′ NUC target models using NIH SAVES (involving PROCHECK, WHATIF check, Ramachandran contour map and Errat plot) server analysis [17–21]. Molecular dynamics simulation Molecular dynamics (MD) simulations were performed using the Groningen machine for chemical simulations (GROMACS) V3.3 package. The independent SV-5′ NUC model or the SV-5′ NUC-ligand complex (vanillin/vanillic acid) were used for performing MD simulations. All three systems were solvated independently in a cubic box of 1 nm dimension by applying GROMOS96 force field, and the subsequent procedures followed were similar in all the three systems. The SPC water model was used in order to create the aqueous environment. Particle mesh Ewald (PME) electrostatic and periodic boundary conditions were applied in all directions. The three systems were neutralized by adding two Cl− counter ions by replacing an equal number of water molecules. The systems were subjected to 10,000 steps steepest descent energy minimization, therefore the system can be rid of high energy interactions and steric clashes. All bond lengths were constrained using the LINCS method and the energy minimized system was treated for a 100-ps equilibration run. The pre-equilibrated system was consequently subjected to a 5-ns production MD simulation with constant temperature (300 K), pressure (1 atm) and without any position restraints. Snapshots were collected every 5 ps and all analyses of MD simulations were carried out using GROMACS analysis tools. The values obtained for each trajectory were averaged. The root mean square of the deviation (RMSD) of the protein backbone/structure-ligand complexes were monitored throughout the MD simulation to determine structural convergence and interaction stability. Binding-site prediction Binding-site identification plays a major role in structure based drug design (SBDD). In our study, the binding-site region of the chosen predicted model was identified using the SiteMap program (v2.5), which identifies one or more regions suitable for ligand binding. Further, the hydrophobic and hydrophilic map (donor, acceptor and metal-binding regions) was produced using various contour maps and scored. The score was generated using default parameters implemented in SiteMap program (v2.5) to generate more than two sites.
Page 3 of 10, 2156
Ligand preparation The chemical molecules maltol (CID: 8369), vanillin (CID: 1183) and vanillic acid (CID: 8468) were retrieved from the PubChem database. The legends were prepared for docking by using the LigPrep program (v2.5). The tautomers for each of these ligands were generated, optimized and also neutralized. Partial atomic charges were computed using the OPLS_2005 force field and the ligands were energy minimized [22]. Molecular docking analysis The “Extra Precision” (XP) mode of Glide (v5.7) was used to perform all docking calculations using the OPLS-AA 2005 force field [22, 23]. In this work, the bounding box of size 10 Å×10 Å×10 Å was defined and confined to the sitemap predicted active site region of SV-5′ NUC model for docking the ligands. A scale factor of 0.4 for van der Waals radii was applied to atoms of protein with absolute partial charges less than or equal to 0.25. A total of 5,000 poses per ligand was generated during the initial phase of the docking calculation, out of which best 1,000 poses per ligand were chosen for energy minimization. The dielectric constant of 4.0 and 1,000 steps of conjugate gradient minimizations were included in the energy minimization protocol. Upon completion of each docking calculation, 10,000 poses per ligand were generated and the best docked structure was chosen using a Glide Score function. The choice of the best pose was made using a model energy score that combines the energy grid score, Glide score, and the internal strain of the ligand. ADME analysis The absorption, distribution, metabolism and excretion (ADME) properties of all molecules were obtained using a QikProp program (v3.4), which predicts the details of physically significant bioactive principles and pharmaceutically relevant properties of a legend. The program was performed in normal mode, and more than 40 chemical and biological properties were analyzed for all the ligands considered in this study. This program is also believed to evaluate the druglikeliness of the compounds based on Lipinski’s rule of five, which is essential for rational drug design. Energy-optimized pharmacophore mapping The energy-optimized pharmacophore (e-pharmacophores) regions were determined based on the energy terms obtained from the Glide XP scoring function to accurately characterize the ligand–protein interaction [24]. The e-pharmacophore sites of the ligands were generated from the single-mode computed energy terms of the docking poses of the ligand to
2156, Page 4 of 10
J Mol Model (2014) 20:2156
lack of availability of SV-5′ NUC sequences for N. naja, D. russelli and T. malabaricus in the Uniprot sequence database. Further, D. vestigiata belongs to the same Elapidae family as N. naja, D. russelli and T. malabaricus. Demansia vestigiata SV-5′ NUC comprises 559 amino acids (Uniprot id: A6MFL8) with a molecular mass of 64,642 Da, and is said to possess hydrolase activity. Sequence analysis revealed that both human and SV-5′ NUC belong to the same superfamily of proteins containing the Rossman-like domain and halo acid dehydrogenase motifs. The physico-chemical characterization of the protein revealed the following: theoretical pI : 5.61; total number of (Asp + Glu) negatively charged residues: 80; total number of (Arg + Lys) positively charged residues: 64; extinction coefficient : 72,700 M−1 cm−1 with an estimated half-life of 30 h (mammalian reticulocytes, in vitro). The computed instability index score 40.77 of SV-5′ NUC revealed that the protein was unstable. The grand average of hydropathicity (GRAVY) and aliphatic index prediction of SV-5′ NUC amino acids revealed a score of −0.400 and 77.92, respectively.
the protein. The generated details of the atom centers (donor, acceptor and aromatic ring pi–pi interactions) and their Glide XP energies that comprise each pharmacophore site were summed. The atom centers/sites were then ranked based on these energies and the most favorable sites were then mapped to produce the common e-pharmacophore. Molecular electrostatic potential analysis Molecular electrostatic potential analysis (MELPA) analysis at the functional binding pocket of the modeled target protein was carried out using Pymol (v1.3) based on the surface level potential values. The molecular surface was generated based on the Poisson-Boltzmann distribution and visualized using Pymol (v1. 3).
Results and discussion Experimental data In search of a specific inhibitor of SV-5′ NUC we tested compounds such as maltol, vanillin and vanillic acid (Table 1). It was observed that vanillin and vanillic acid exhibited inhibitory effects on N. naja, D. russelli and T. malabaricus venom 5′ NUC activities. However, maltol did not exhibit any inhibitory activity even though it are structurally related. Based on the above reasons and levels of the IC50 values obtained for different SV-5′ NUCs (Table 1), we found that vanillic acid was a much more potent inhibitor than vanillin. The above results also corroborated results reported previously [4, 25]. Henceforth, all further in silico experiments were aimed at understanding the interaction of vanillin/vanillic acid with the SV-5′ NUC. Maltol was found experimentally to lack any interaction with any of the three SV-5′ NUC, suggesting that, despite being a structural analogue of vanillin and vanillic acid, it lacked the key chemical descriptors for interaction.
Homology modeling and validation From sequence analysis and BLAST search against the PDB database, the functional homolog of SV-5′ NUC in humans (PDB: 2J2C) with a resolution of 2.2 Å was identified as the template for homology modeling studies due to its lowest evalue of zero and high sequence identity of 95 %. Figure 1 shows the existence of highly conserved residues at the sequence level between them. It also illustrates the secondary structural information for SV-5′ NUC along with the surface accessibility of the residues. The last 77 residues were not modelled due to the lack of structural information, and are believed not to play any functional role in the enzymatic activity of SV-5′ NUC. Four models for the modeled region of SV-5′ NUC were generated and the best of them (SV-5′ NUC1) chosen according to its lowest molpdf and DOPE scores (Table S1). The modelled structure (SV-5′ NUC1) confirmed that it is a member of the 5′ nucleotidase superfamily of α/β hydrolases containing the HAD-IG-nucleotidase subfamily domain (1–482 residues). The modelled region of the SV-5′ NUC1 structure (95 % identity and 97 % similarity towards 2J2C) is depicted in Fig. 1. The observed 3D structure
Sequence analysis and physicochemical characterization Demansia vestigiata was chosen as a representative model organism for our in silico study because of the recent availability of the SV-5′ NUC sequence for this snake only, and the
Table 1 Effect of ligands on snake venom 5′ nucleotidase activity (SV-5′ NUC). Values represent mean ± SD (n=4). NI No inhibition Snake venom
Specific activity (nmol ip released min-1 mg-1 protein)
Maltol (IC50 μmol)a
Vanillin (IC50 μmol)a
Vanillic acid (IC50 μmol)a
Naja naja Vaboia russelli Trimeresures malabaricus
653±3.48 900±4.61 7,222±2.38
NI NI NI
162±1.5 135±1.2 146±1.1
84±0.8 98±0.9 65±0.7
a
IC50 value signifies the 50 % inhibition of enzymatic activity at a particular concentration of the chemical compound
J Mol Model (2014) 20:2156
Page 5 of 10, 2156
Fig. 1 Multiple sequence alignment of A6MFL8 of Demansia Vestigiata with its human PDB homolog [2J2C] of 5′ nucleotidase. Red Regions of conserved residues throughout the protein. Numbering of amino acids is denoted with respect to A6MFL8 and secondary structures are also
shown. Surface accessibility is depicted for the modeled region with white, cyan, blue and red color indicating buried, intermediate, accessible residues and incomputable residues, respectively (see web version of the article for colored figure). The figure was produced using ESPript
of SV-5′ NUC1 shows the presence of a mixture of α/β folds with single Rossman-like domain with anti-parallel β sheets (Fig. 2). It also shows the presence of haloacid dehydrogenase (HAD) member like motifs {hhhhDxDx(T/V)}, {hhhh(T/S)}, and {hhhh(G/N)(D/E)x(3–4)(D/E)} (where “h” stands for a hydrophobic residue) in the sequence. The validation of the SV-5′ NUC1 model with PROCHECK-based Ramachandran map statistics revealed 85.1 % amino acid residues in the favored region, 14.4 % in
the additionally allowed region and 0.5 % in the generously allowed region, respectively. Moreover, no residues were observed in the disallowed region (Fig. 3). Thus, our SV-5′ NUC1 model is stereochemically significant with a reasonable distribution of backbone angles in the protein structure and acceptability of the built model. The G-factor values representing the dihedral, covalent and overall bond angles was found to be −0.38°, 0.12° and −0.17°, respectively. The main-chain and side-chain parameters assessed for SV-5′
2156, Page 6 of 10
Fig. 2 Three dimensional (3D) modeled structure of snake venom 5′ nucleotidase (SV-5′ NUC1) shown in ribbon representation. C, N Corresponding terminal ends of the protein, respectively; BS top-ranked binding site region derived from sitemap results. The figure was produced using Pymol (v1.3)
NUC1 using PROCHECK revealed favorable stereochemical properties (Fig. S2, S3). The ERRAT plot depicted various non-bonded interactions between different atom types of amino acids. This provided structure-modifying guidance
J Mol Model (2014) 20:2156
to improve the sterically hindered regions in the protein. The overall quality factor of the homology model was 90.48 % in the ERRAT plot, with minor ‘structure error’ reflecting the steric hindrance between a few amino acids (Fig. 3). As expected, Verify-3D also revealed that 91.67 % of the amino acids in the current structure of SV-5′ NUC1 have a compatible 1D–3D score greater than 0.2. The SV-5′ NUC1 model has a Z-score value of −0.82 in the range of native conformations of crystal structures, which further enhanced the confidence of accepting the SV-5′ NUC1 model (Fig. 3). The crystal structure of the human 5′ NUC and SV-5′ NUC1 model was superimposed to confirm the striking conformational similarity between them. The RMSD value of 0.5 Å was observed for the superimposed structure (Fig. S4). This further emphasized the quality of the built model due to the minimum deviation with respect to backbones and side chains, respectively. Binding-pocket prediction, molecular docking analysis and MD simulation To check the stability of SV-5′ NUC1, the RMSD of backbone atoms/total energy was plotted against time as shown in Fig. 4a for an MD simulation run for 5 ns. The graphic
Fig. 3a–c SV-5′ NUC1 model evaluation. a PROCHECK based Ramachandran 2D contour map, b ERRAT plot analysis report, c Q-mean Z-score plot
J Mol Model (2014) 20:2156
Page 7 of 10, 2156
RMSD (nm)
a
Time (ps)
RMSD (nm)
b
Time (ps)
RMSD (nm)
c
Time (ps)
Fig. 4 Root mean square deviation (RMSD) vs time graph for a 5 ns simulation of a SV-5′ NUC1 model, b SV-5′ NUC1–vanillin complex, c SV-5′ NUC1–vanillic acid complex
illustration of their trajectory clearly shows the overall stability observed for the SV-5 NUC1 model. The RMSD observed between an energy-minimized model of SV-5′ NUC1 and the final structure of the MD simulation was as low as 0.02 nm. The average RMSD calculated for the SV-5′NUC1 model was found to be 0.08 nm. The above results clearly emphasize that the SV-5′ NUC1 3D structure is highly stable and can be used further for molecular docking process. In order to investigate the interaction between SV-5′ NUC1 and the pubchem ligands (Fig. 5), the binding site was defined based on calculations predicted by the SiteMap module in Schrödinger, as well as information available from the literature. The best binding site (siteMap1) residues revealed a
higher binding site score (Table S5) calculated based on effective dscore, size and volume of the cavity. The predicted site is comprised of amino acid residues Y65 and T72, which are believed to be important for the ligand–protein interaction. Based on the coinciding literature survey and our SiteMap1 results, this site was chosen as the most favorable binding site to dock the ligands (vanillin and vanillic acid) independently with SV-5′ NUC1. Glide XP mode docking was performed for both the energy minimized ligands in the validated binding pocket of the SV-5′ NUC1 protein. Vanillin forms hydrogen bond interactions with the side chain OH atom of Tyr65. Vanillic acid forms hydrogen bond interactions with side chain OH atoms of Y65 and T72. Their observed interaction binding poses and interaction maps are shown in Fig. S6. The observed binding pattern of vanillic acid makes us speculate that the –COOH group present in this ligand could provide better interaction for inhibition compared to the –CHO group of vanillin. From the molecular docking results, we observed that vanillic acid was a better inhibitor than vanillin due to its lower Glide XP and Glide energy scores (Table 2). Moreover, vanillic acid was said to contain only two weak hydrogen bond interactions with Y65 and T72 compared with only one hydrogen bond of vanillin with Y65. The observed bioinformatics results corroborated with our experimental results, revealing vanillic acid as a better inhibitor than vanillin. Further, in the case of MD simulations of the SV-5′ NUC1–vanillin complex (Fig. 4b) hydrogen bond interaction between them was stable only after 2.5 ns, whereas MD simulation of the SV-5′ NUC1vanillic acid complex (Fig. 4c) clearly showed that hydrogen bond interactions between the protein and ligand were highly stable throughout the 5 ns simulation. This also strengthens the results obtained from our molecular docking interaction results of SV-5′ NUC1 with vanillin/vanillic acid. ADME analysis We analyzed more than 40 physical signifiers and pharmacologically relevant properties of the two lead compounds, including molecular weight, H-bond donors, H-bond acceptors, log P (octanol/water), QP log S, QPP Caco, % of human oral absorption and their positions according to Lipinski’s rule of five (Table 3). This is a rule of thumb to evaluate the drug-like properties of a compound. The rule describes pharmacological or biological activity properties at molecular level that are important in the drug’s pharmacokinetics in the human body, including its ADME. Nevertheless, the rule does not predict whether a compound is pharmacologically active. Upon ADME analysis, the three ligands maltol, vanillin and vanillic acid were found to exist within the acceptable range of Lipinski’s rule of five. For the three lead compounds, the partition coefficient [QP log P (o/w)] and water solubility (QP log S), which are crucial when estimating the distribution
2156, Page 8 of 10
J Mol Model (2014) 20:2156
Fig. 5a–c Ligand structures. a Vanillin (IUPAC name = 3methoxy-4-hydroxy benzaldehyde). b Vanillic acid (3methoxy-4-hydroxy benzoic acid). c Maltol (3-hydroxy-2-methyl-4-pyrone)
and absorption of drugs within the body, ranged between 1.0 to 1.1 and −1.0 to −1.3, respectively. The cell permeability (QP PCaco)—a fundamental factor regulating the metabolism of drugs and their entry through biological membranes— ranged from 100 to 800. The percentage of human oral absorptions for all compounds was found to be in the range of 65 % to 100 %. All the above observed pharmacokinetic parameters are within the satisfactory range defined for human use, thereby suggesting their possible use as drug-like molecules. E-Pharmacophore mapping A structure-based ligand docking approach or a ligand-based pharmacophore approach are two possible routes to drug discovery and design of active molecules. For any given ligand there will be many different pharmacophore sites; in most cases, within this pharmacophore locations it will be
difficult to find out which sites contribute to activity. To overcome this problem, incorporating protein–ligand interactions into ligand-based pharmacophore approaches has been shown to produce enhanced improvements in identifying the best pharmacophore sites. By applying the above said principle, three common pharmacophore sites were observed each in vanillin and vanillic acid (Fig. 6). This information helped us to eliminate pharmacophore sites that lack significant interactions and also to prioritize them. Hence, from this methodology we were able to get both good improvements as well as diversity in our hits. The respective site scoring due to which we were able to prioritize the e-pharmacophoric sites revealed an acceptor group, an aromatic ring, and one H-bond donor group as our common pharmacophore model. Based on the individual epharmacophore sites of the ligands, a common pharmacophore hypothesis was generated (Table S7, Fig. S8, Fig. 6). The bond distances and angles are represented as shown in Fig. S8. This information should prove further
Table 2 Molecular docking results of SV-5′ NUC1 with ligand using Glide XP in Schrödinger (v9.3). D Donor, H hydrogen, A acceptor Ligand IDa Ligand nameb
Glide XP score Glide energy Interacting residues (kcal mol−1) (D⋯H-A) (kcal mol−1)
CID 8468
Vanillic acid −5.707
−32.985
CID 1183
Vanillin
−2.814
−25.166
CID 8369
Maltol
−1.260
−10.213
a
Ligand IDs are from the Pubchem database
b
Ligand names are from the Pubchem database
c
Maltol showed no interaction with SV-5′ NUC1
H-bond length (Å)
Neighbouring residues within 4 Å
OH⋯H(Y65) NH(T72)⋯O 1.860 2.395 E66, F69, L68, L71, F155, P158, V203, V206, L211. OH(Y65)⋯O 2.027 D54, Y60, F155, D204, V203, V206, Y207, L211, K212, Y252. –c – –
J Mol Model (2014) 20:2156
Page 9 of 10, 2156
Table 3 ADME values of the ligands for the below mentioned descriptors Ligand IDa
Mol wtb
QPlogSc
QPlog HERGd
QPP caco2e
% Human oral absorptionf
Rule of fiveg
CID 8468 CID 1183 CID 8369
168.14 152.14 126.01
−1.249 −1.028 −1.002
−1.425 −3.194 −1.132
108.343 787.641 96.242
69.688 85.085 72.054
0 0 0
a
Ligand IDs are from the pubchem database
b
Molecular weight of the molecule (130.0–725.0 acceptable)
Predicted aqueous solubility, log S. S in mol dm−3 is the concentration of the solute in a saturated solution that is in equilibrium with the crystalline solid (−6.5–0.5)
c
d
Predicted IC50 value for blockage of HERG K + channels (concern below −5)
Predicted apparent Caco-2 cell permeability in nm s−1 . Caco-2 cells are a model for the gut-blood barrier. QikProp predictions are for non-active transport (500 great)
e
f Predicted human oral absorption on 0 to 100 % scale. The prediction is based on a quantitative multiple linear regression model. This property usually correlates well with human-oral-absorption, as both measures the same property (>80 % is high
