Computational structure prediction, virtual screening, pharmacokinetic

INTERNATIONAL JOURNAL OF APPLIED ENGINEERING RESEARCH, DINDIGUL Volume 2, No 2, 2011 © Copyright 2010 All rights reserved Integrated Publishing Association RESEARCH ARTICLE

ISSN - 0976-4259

Computational structure prediction, virtual screening, pharmacokinetic profiling and molecular interaction studies on Na+ and Ca2+ ion channel blockers of Phoneutria toxins(PhTxs) from Phoneutria nigreventer Barani Kumar.R, Xavier Suresh.M Department of Bioinformatics, Sathyabama University, Jeppiaar Nagar, Rajiv Gandhi Road, Chennai - 600 119,Tamilnadu, India. [email protected] ABSTRACT Many of the toxins produce a wide range of interaction with many biological macromolecules such as enzymes, ion channels, cellular receptors, etc. Phoneutria nigreventer a poisonous spider produces a cocktail of proteins which affects Na+, Ca2+ and K+ channels. In this work we studied the functional role of the proteins with respect to protein structure which is still unknown. The spider toxin fractions have no experimentally determined three dimensional structures in any of the structural databases. So, comparative modeling method was employed to predict the structure of the toxin fractions and then multiple structure alignment studies were carried out. The fraction that plays a very important role is PhTx2-6 toxin, which affects the sodium channels and it is the main cause for toxic activity on the cells. More than 300 analogs of derivatives of aspirin as well as derivatives of clove oil were computationally analyzed and further their ADME/Tox profiles were tested. From the ADMET studies we have selected the best analogs that possessed appropriate pharmacokinetic properties and interaction studies were performed for neutralizing the effect of the toxin PhTx2-6 using the Ligand Fit program of Discovery Studio 2.0. Based on the scoring functions and hydrogen bond interactions we identified the analog of acetylsalicylic acid, “N-(4-hydroxyphenyl) butanamide” to be the best interacting molecule which may have the efficiency to cure the neurotoxic effect produced by Phoneutria toxins. Keywords: Neurotoxin, Homology modeling, ion channels, Aspirin, Clove oil, ADME/Tox. 1. Introduction Toxins in animal are one of the important weapons for predation and protection and are used to paralyze and kill prey. It consists of a numerous complicated chemical components which can be a part of the toxin but proteins and peptides are also common components. The organism uses an elegant genetic and bio chemical ways to generate these toxins. Spider toxin which originates from the family of venomous proteins that leads to act as neurotoxins. Most of the spider toxin blocks Ca2+ and Na+ ion channels but some of the rare groups of spider toxins block K+ channels. The Brazilian “armed” spider Phoneutria nigriventer is extremely aggressive, with nocturnal and erratic habits, hunting and eating a vast variety of animals including many species of insects, other spiders, small rodents, etc. (Gomez, et al. 2002) Phoneutria nigriventer is a successful predator and it is living with the diversity of potent toxins present in its venom. The spider assumes a very characteristic position when molested, standing on its hind legs, even against enemies that are several folds larger, as humans (Domont et al. 1993). The Brazilian wandering spiders appear in the Guinness Book of World Records 2007 as the world's most venomous spiders and are the

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spiders considered directly responsible for most human deaths due to envenomation from spider bites. These spiders are members of the ctenidae family of wandering spiders. The other species are P. bahiensis, P. boliviensis, P.eickstedtae, P.fera, P.keyserlingi, P.pertyi and P. reidyi. Phoneutria bites are reported to cause severe and irradiating pain, and several toxic symptoms, characterized by cramps, tremors, tonic convulsions, spastic paralysis, priapism, sialorrhea, arrhythmias, visual disturbance, and cold sudoresis. (Schenberg, S. and Lima, F. A. 1966; Lucas, S.1988). There are three isolated neurotoxin fractions from the venom, PhTx-l (Phoneutria Toxin-1), PhTx-2(Phoneutria Toxin-2), PhTx-3(Phoneutria Toxin-3), and a fraction inducing contractions of the guinea-pig ileum smooth musculature. Upon intra cerebroventricular administration, PhTx-1 produced central excitatory effects in mice, associated with spastic paralysis. PhTx-2 reproduced most effects reported for the crude venom, and was recently shown to inhibit inactivation of voltage-dependent sodium channels in frog skeletal muscles. (Araujo et al. 1993) PhTx3 caused skeletal muscle relaxation, and it was reported to contain six peptides with sequences of amino acids similar to neurotoxins derived from venoms of other spider species.( M.A.M. Prado et al. 1997). The vital neurotoxic effect of the venom appears to be an action on voltage gated Na+ channels, which can induce repetitive-action potential discharges in nerve and muscle fiber membranes. The venom indeed depolarizes the muscle membrane and increases the frequency of miniature end plate membrane potentials in a tetrodotoxin (TTX) sensitive fashion (Fontana, M.D et al. 1985). In addition the venom caused morphological alterations in nerve fibers that are compatible with its action on Na+ channels (Cruz-Hofling et al. 1985; Love, S et al. 1986). Genomic research on phoneutria neurotoxins have been initiated in recent years. (Duarte, H et al. 1998) cloned the cDNAs encoding neurotoxic peptides from the spider Phoneutria nigriventer. These peptide toxins are probably responsible for the main excitatory action of the venom on cells, and appear to account for the ability of the venom to release neurotransmitters. Several research reports reveal the functional importance of PhTx2 and its role on sodium ion channels and its related functions (Araujo et al. 1993). Moreover, PhTX-2 increased the entry of Na+ in cortical synaptosomes by inducing membrane depolarization, calcium influx and glutamate release in a TTX-sensitive way (RibeiroSantos, et al. 1993). The PhTx-2 has also been shown to induce the release of other neurotransmitters, including acetylcholine (Prado M A M, et al.1998). It seems that at least one of the toxins purified from this fraction (PhTx2-6) has complex effects on Na+ channel (Matavel, et al. 2002). The main active composition of the toxin fraction PhTx2-6 is sexual dysfunction i.e., eractile dysfunction(ED). One of the characteristics of the poisons from the bite of the brazilian spider, Phoneutria nigriventer is penile erection, mainly apparent in children bitten by this spider (Prado et al. 1997; Nunes et al. 2008). These two poisonous derivatives, Ph Tx2-5 and Ph Tx2-6, termed as ‘eretina’, which causes relaxation of cavernous smooth muscle and causing penile erection. In the present work we extend our study to the identification of similarities and divergences among the toxin fractions by evolutionary analysis. Analysis starts from sequence level, but it has given very poor result i.e. there is no significant identity among the toxin fractions. Then the three dimensional structure of PhTxs were searched in various structural databases.

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But there are no experimentally predicted structures of phoneutria toxins and have predicted through comparative modeling method using bioinformatics software, Modeller9v7 (http://salilab.org/modeller/). Then the predicted structures were validated with SAVEServer (Structure Analysis and Verification Server) (http://nihserver.mbi.ucla.edu/SAVES/). Then multiple structure alignment was carried out with predicted model for identification of common domains and dissimilar scaffolds. Then the most potent toxin fraction Ph Tx2-6 is allowed for in silico interaction studies with some of the potent neurotoxin inhibitors. 2. Materials and methods 2.1. Sequence alignment and template selection Since the crystal structure of the Phoneutria toxins (PhTx’s) has not been determined, sequences of phoneutria toxins were retrieved from swissprot database (http://www.uniprot.org/) and converted in to fasta format. Then the three dimensional structure of this cocktail proteins were obtained by using the homology modeling based on the various crystal structures of each toxins selected through BlastP (http://blast.ncbi.nlm.nih.gov/) and PDBSUM (http://www.ebi.ac.uk/pdbsum/). Sequence identity between target and template proteins were set to >30% for selection of template structures from protein databank (PDB) (http://www.pdb.org/). 2.2. Sequence alignment and toxin modeling Homology modeling was performed for all ten toxin proteins using automated modeling program, Modeller9v7. Before modeling, target-template protein sequence alignments were generated by ClustalW (http://www.genome.jp/tools/clustalw/). Then the aligned sequences were allowed for modeling. For constructing the toxin protein structures in modeller9v7 we need to generate three files including (i) alignment file, (ii) atom file, and (iii) Script file. Then modeller generates the protein structures based on the spatial restraints between template and target orientations (Sali and Blundell, 1993). 2.3. Structure validation Modeled proteins of PhTxs were validated in SAVE, an online server. This program checks the stereo chemical quality of a protein structure by analyzing residue-by-residue geometry and overall structure geometry. The three dimensional structure was validated with the Procheck program. This program generates Ramachandran plot, along with overall G-factor and accuracy of the protein model. 2.4. Multiple structure alignment In order to get the evolutionary relationship of structure responsible for functions, common folding pattern and domains are identified using multiple structure alignment study. Multiple flexible structure alignment was performed using POSA (Partial Order Structure Alignment) (http://fatcat.burnham.org/POSA/) using partial order graphs (POG) (Yuzhen Y and Godzik A 2005). The input was given in the form of compressed file of either PDB file

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or SCOP identifiers. The output has an aligned multi colour structure where each structure shows each protein. The output includes a phylogenetic tree and Hidden Markov Model (HMM) of the tree. 2.5. Binding site analysis Binding sites are cavities that are present in the surface of the protein that aid in binding the substrates/inhibitors. The binding sites in the target structure were predicted by using the flood-filling algorithm embedded in Discovery Studio 2.0. A grid resolution of 0.50 Å which is an indication of grid spacing and minimum number of grid points 100 points were used for this analysis. 2.6. Selection of ligands Eugenol is an antipyretic obtained from clove oil which is a dark-brown liquid, a distillate of flowers, stalks and leaves of the clove tree Eugenia aromatic (Velisek et al. 2005). It has also been shown to have an anti-thrombotic effect in humans, due to its inhibition of platelet aggregation and thromboxane synthesis. (Prakash and Neelu Gupta 2005). Acetaminophen and Acetylsalicylic acid are derivatives of aspirin which has anti-inflammatory and antipyretic properties and acts as an inhibitor of cyclooxygenase which results in the inhibition of the biosynthesis of prostaglandins. It also inhibits platelet aggregation and is used in the prevention of arterial and venous thrombosis (From Martindale, The Extra Pharmacopoeia, 30th Ed, p5). The above derivatives were considered as a parent compounds to screen analogs. Zinc database, a free database of commercially available compounds for virtual screening was used for retrieving the analogs of the parent compounds. Molecules in these databases are annotated by molecular property (John J. Irwin and Brian K. Shoichet 2005). The molecular properties help in filtering the irrelevant molecules by restricting the search by setting preferable values. 2.7. Pharmacokinetic profiling of analogs Preclinical ADME/Tox studies help in ruling out false positives and identify the most potential drug candidates with appropriate kinetic and dynamic properties (Sudhakar, et al. 2010). For this, all the analogs were subjected to ADME/Tox property calculations. The molecules which were proven to be potentially drug like were ultimately considered as potential lead molecules for docking study. 2.8. Molecular docking The LigandFit module from Discovery studio 2.0 was used to perform the docking, based on shape-based searching and Monte Carlo methods. While docking, the variable trials Monte Carlo conformation was applied where the number of steps depends on the number of rotatable bonds in the ligand. By default, the torsions number is two, the number of trials is 500 and the maximum successive failure is 120. The docking poses were evaluated based on dock scores and hydrogen bonding with the binding site residues. The scoring system included Ligscore, Piecewise linear potential (PLP), Jain, Potential Mean Force (PMF) and Dock score.

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3. Results and Discussion 3.1. Protein modeling and structure validation Homology modeling performed by using appropriate templates for all ten toxin proteins using automated modeling program, Modeller9v7 and illustrated in Table 1. The structure of those ten toxin fractions are depicted in Figure 1. The modeled proteins were validated through SAVE server and amino acid percentile contributions are given in the Table 2 and the Ramachandran plot of PhTx2-6 and represented in Figure 2. Table 1: Percentage of identity between the target-template sequences Toxin name

Accessio n Number P29423

Template PDB ID

Name of the Protein & Source Organism

1OWS(B)

Phospholipase A2 from Indian cobra.

PhTx 2-5

P29424

1Z1X

Novel disintegrin from saw-scaled viper.

44.8

PhTx 2-6

P29425

1YXH (A)

Phospholipase A2 from Naja naja sagittifera.

38.7

PhTx 2-9

P29426

1AXH (A)

Atracotoxin-HVI from Hadronyche versuta.

45.5

PhTx 3-1

O76200

1SKZ(A)

Serine protease inhibitor from Haementeria officinalis (Mexican leech).

PhTX 21

% identit y 40.0

52.4

PhTx 3-2

O76201

1TG4

Phospholipase A2 from Russells viper (1tg4).

53.7

PhTx 3-3

P81789

1NIX(A)

Hainan Toxin from Ornithoctonus hainana.

42.9

PhTx 3-4

P81790

2GM6

45.7

PhTx 3-5

P81791

1MUT

Cysteine dioxygenase type from Ralstonia eutropha MUTT enzyme from E.coli

PhTx 3-6

P81792

1XKA(L )

Synthetic inhibitor FX-2212A from Homo sapiens

59.2 44.0

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(a)

(d)

(g)

(c)

(b)

(e)

(f)

(h)

(i)

Figure 1: Modelled 3D structures of P.nigreventer toxin proteins (a) PhTx 2-1; (b) PhTx 2-5; (c) PhTx2-6; (d) PhTx 2-9; (e) PhTx3-1; (f) PhTx 3-2; (g): PhTx 3-3; (h) PhTx 3-4; (i): PhTx3-5; (j) PhTx 3-6.

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Table 2: Modeled protein structure validation using Ramachandran graph Toxin Fraction Ph Tx 2-1 Ph Tx 2-5 Ph Tx 2-6 Ph Tx 2-9 Ph Tx 3-1 Ph Tx 3-2 Ph Tx 3-3 Ph Tx 3-4 Ph Tx 3-5 Ph Tx 3-6

F.R* (%) 82.2 100 93 70.4 78.8 82.1 76 77.5 66.2 75.9

A.R^ (%) 15.6 0 7 22.2 16.7 13.4 24 16.9 29.7 13.9

G.A.R$ (%) 2.2 0 0 7.4 4.2 0 0 4.5 1.4 8.9

D.A.R# (%) 0 0 0 0 1.4 4.5 0 1.1 2.7 1.3

F.R*- % of amino acids in favored Region; A.R^- allowed region; G.A.R$- Generously allowed region; D.A.R#-Disallowed region.

Figure 2: Ramachandran plot for Modeled PhTx2-6 3.2. Multiple structure alignment Multiple flexible structure alignment was performed using POSA (Partial Order Structure Alignment) using partial order graphs and the structures are given in Figure 3. As a result of multiple structure alignment, Evolutionary tree representation for the ten toxin proteins of Phoneutria nigreventer was generated by POSA server Figure 4. The predicted models of PhTxs were allowed for multiple structure alignment to identify the common scaffolds among the two toxin fragments (PhTx-2 and PhTx-3). Superimposition revealed that conserve very low percentile of homology among the toxins proteins. Toxins PhTx2-1,

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PhTx3-1 and PhTx3-2, PhTx2-9 and PhTx3-3 shares common folding patterns whereas, PhTx2-6 and PhTx3-5 showed divergence in structural level. Hence this study proved that the venom of this species has wide range divergence and it may be evolved from more than one ancestor.

Figure 3: Multiple structure alignment of PhTxs protein

Figure 4: Tree representation of toxin structures of P. nigreventer generated by POSA server. 3.3. Binding site analysis The flood filling algorithm used for predicting the cavities identified seven binding sites within the protein. The binding site includes around 14 residues and most of the functional residues of PhTx 2-6 also present in the site. The residues are Lys2, Gln40, Asp41, Arg34, Gln42, Pro43, Asp49, Cys50, Gly52, Arg54, Gly55, Glu56, Cys57 and Gly60 and thus this

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is identified to be the most desired site for inhibition. The drugs were limited to this search space during the docking process. 3.4. Screening and selection of drugs The similar analog compounds for the derivatives of aspirin and clove oil have been retrieved from Zinc database. The pharmacological effect of aspirin and clove oil imparted through its structure was used as a base to identify similar compounds that could mimic its structural and functional property. About 300 analogs (similar compounds of eugenol, acetylsalicylic acid, and acetaminophen) were retrieved as drug molecules from a database which are also annotated by molecular property. The molecular properties help in filtering the irrelevant molecules by restricting the search by setting preferable values (John J. Irwin and Brian K. Shoichet.2005). 3.5. Filtering analogs by ADMET profiling The analogs were further subjected to filtering by ADME and toxicity studies to identify the most potential drug like compounds. The ADME/T properties like solubility, absorption, plasma protein binding, blood brain barrier and cytochrome binding profiles for all the 300 analogs were tested by using Discovery studio 2.0. The suitable analogs were selected by comparing the kinetic and toxicity values with the reference values given by the program. The top five analogs from each drug were identified to be the most potential molecules showing no toxicity and more drugs like property. The details of those fifteen analogs are given in Table 3 and their pharmacokinetic profiling is represented in Table 4. Table 3: Name of the best analogs filtered for docking studies ANALOGS OF

Eugenol

IUPAC NAME 2-methoxy-4-prop-2enylphenol. 5-prop-2-enyl-1, 3benzodioxole. Methyl(6R)-6-(4-hydroxy-3methoxyphenyl)-3,4dimethyl-2-oxo-1,6dihydropyrimidine-5carboxylate. (4S)-2-amino-4-(1,3benzodioxol-5-yl)-5-oxo4,6,7,8-tetrahydrochromene3-carbonitrile. Methyl(4S)-4-(4-hydroxy-3methoxyphenyl)-1,6dimethyl-2-oxo-3,4-

CHEMICA L FORMULA

MOLE. FORMULA [g/mol]

C10H12O2

164.20

C10H10O2

162.18

C15H18N2O 5

306.31

C17H14N2O4

310.30

C15H18N2O5

306.31

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dihydropyrimidine-5carboxylate N-(4hydroxyphenyl)butanamide N-(4-methoxyphenyl)-Nmethylacetamide N-(4-ethoxyphenyl)acetamide 4-(ethylamino)phenol (7R)-7-[3-methoxy-4-(2morpholin-4-yl-2oxoethoxy)phenyl]-5-methylN-phenyl-1,7dihydro[1,2,4]triazolo[1,5a]pyrimidine-6-carboxamide P-Butyramidophenol

Acetyl salicylic acid

C10H13NO2

179.21

C10H13NO2

179.21

C10H13NO2 C8H11NO

179.21 137.18

C26H28N6O 5

504.21

179.21

C10H13NO2 N-(4-methoxyphenyl)-Nmethylacetamide Methyl 2propanoyloxybenzoate Methyl 2-(2chloroacetyl)oxybenzoate N-(4-methoxyphenyl)-2sulfanylacetamide

Acetomino phen

C10H13NO2

179.21

C11H12O4

208.21

C10H9ClO4

228.62

C9H11NO2S

197.25

Table 4: Pharmacokinetics profiling using ADMET tool Analogs of

Eugenol

Acetyl salicylic acid

Molecule

ZINC0000 1411 ZINC0000 2035 ZINC0002 2671 ZINC0006 7195 ZINC0006 7198 ZINC1234 6153 ZINC0040 4043

*

BB B

$

#

&

@

2

0

2

0

3

0

0

2

2

0

3

0

0

0

2

0

3

0

0

2

2

0

3

0

0

2

3

0

3

0

0

2

2

0

3

0

0

1

A

Hep Toxicit y 0

CY

P 2D6 0

+

S Leve l 3

PP B 2

543


Acetaminophe n

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ZINC0036 4024 ZINC0158 6542 ZINC0159 9351 ZINC0036 3571 ZINC0028 1667 ZINC0000 0602 ZINC0203 4356 ZINC2021 0549

2

0

3

0

0

1

2

0

3

0

0

1

2

0

3

0

0

2

2

0

4

0

0

2

2

0

4

0

0

2

2

0

4

0

0

2

2

0

4

0

0

2

2

0

4

0

0

2

* BBB (blood-brain barrier): V-Very high penetrant; H-High; M- Medium; L-Low; Uundefined. $ Absorption Level: G-Good absorption; M- Moderate absorption; L- Low absorption; L-very low absorption;# Solubility Level: EL-Extremely low; VL- No, very low, but possible; L- Yes, low; G- Yes, good; O- Yes, optimal; TS- No, too soluble;& Hepatotoxicity: NT-Nontoxic; T-Toxic;@ CYP2D6: NI-Non-inhibitor; I-inhibitor;+ PPB (plasma protein binding) Level: G- Binding is < 90% (No markers flagged and AlogP98 < 4.0); M- Binding is > 90% (flagged at 90% or AlogP98 > 4.0), L- Binding is > 95% (flagged at 95% or AlogP98 > 5.0). 3.6. Virtual screening Virtual screening has become one of the significant methods to identify the potential compounds in drug designing. This method was adopted in our work to identify compounds that can inhibit/block the function of PhTx2-6 a deadly toxin fraction. We identified top analogs based on the pharmacokinetic aspects through ADMET profiling. Virtual screening was carried out with the top fifteen analogs in Argus lab. From the results we were able to identify one such analog from each drug that had a good binding towards the functional residues of PhTx 2-6. 3.7. Interaction studies The three selected analogs were docked into the binding site of the receptor using Ligand fit protocol. The docking run generated 10 poses for each of the analog. The ligscore, Jain, PLP and PMF scoring functions were used to identify the best docked pose. The dock scores computed by the different scoring functions along with active site residue interactions for these analogs are tabulated in Table 5. The docked poses for each of the drugs and their interacting residues with distances are illustrated in Figure 5. The stability of the docked poses was evaluated by determining the hydrogen bonding between the receptor and ligand. The interaction pattern analyzed based on the functional residues indicated that all the three analogs formed hydrogen bonds with Lys2. Analog 2 additionally formed 544


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bonds with Asp 34 and Glu56, only analog 3 formed hydrogen bonds with Asp 41 and represented in the Figure 5. The dock score for all the docked drugs showed that analog 2 (N-(4-hydroxyphenyl) butanamide) have the highest dock score of 66.77. From the overall docking we identified that analog 2 (N-(4-hydroxyphenyl) butanamide) to be the best interacting compound based on the dock score and bonded interactions with the functional residues of the protein fraction PhTx2-6.

(a)

(b)

(c)

(d)

Figure 5: (a) Interaction of PhTx2-6 with 2-methoxy-4-prop-2-enylphenol ; ( b) Interaction of PhTx2-6 with N-(4-Hydroxyphenyl) butanamide; (c) Interaction of PhTx2-6 with N-(4methoxyphenyl)-N-methylacetamide; (d) Interaction clusters of best three inhibitor molecule with PhTx2-6.

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Table 5: LigandFit scoring functions and its interaction of best three analog compounds Scoring Function and Interactions Ligscore1 Ligscore2 PLP1 PLP2 Jain PMF Dock Score No. of Hbonds

Interactions with distance in Å

2-methoxy-4prop-2enylphenol 2.29 3.52 65.12 59.4 2.62 -21.53 28.15

Compound Names N-(4-Hydroxyphenyl) butanamide 4.35 3.76 84.97 73.91 1.91 -34.52 66.77

N-(4methoxyphenyl)-Nmethylacetamide 1.75 3.58 50.2 48.07 1.61 -25.17 28.62

2

5

2

Lys2:HZ1-Lig:O2 (2.49) Lys2:HZ1-Lig:O9 (1.97)

Lys2:HZ1-Lig-O:11 (2.40) Lys2:HZ2-Lig-O:11 (1.96) Arg34:HH21-LigO:21 (2.23) Arg34:HH22-Lig-O:21 (1.96) Glu56:HN-Lig-O:11 (2.16)

Lys2:Hz1- Lig:O5 (2.26) Lig: O13-Asp41: O (2.66)

4. Conclusion In this study we have predicted the structure for all the ten toxin fractions of Phoneutria nigreventer which have not been predicted yet experimentally. Multiple structure alignment studies revealed three different types of structural group and have two common folding patterns which may help us to predict the function of toxins. The pharmacokinetic profiling yielded three compounds to be the best and concurrently from the result of virtual screening we conclude that the compounds namely, 2-methoxy-4-prop-2-enylphenol, N-(4Hydroxyphenyl) butanamide and N-(4-methoxyphenyl)-N-methylacetamide were given significant results in the form of scoring functions and individual residue interactions. Among these three analog compounds, the analog of acetylsalicylic acid, (N-(4Hydroxyphenyl) butanamide) showed better results than the remaining top scored analogs. Further experimental research is needed to improve the analogs into drug for this Phoneutria toxin. ACKNOWLEDGMENT The authors gratefully thank the management of Sathyabama University for providing the Cluster Computing Lab facility to carry out this study. The authors also thank the anonymous reviewers for their valuable comments and suggestions. 546


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5. References 1. Araujo, D A M, Cordeiro, M N, Diniz, C R and Beirao, P S L. (1993), Effects of a toxic fraction, PhTx2, from the spider Phoneutria nigriventer on the sodium current. Naunyn-Schmied Arch. Pharmacol. 347, pp205-208. 2. Cruz-Hofling, S. Love, G. Brook, L.W. and Duchen. (1985), Effects of Phoneutria nigriventer spider venom on mouse peripheral nerve. Q. J. Exp. Physiol. 70: pp623– 640. 3. Costa-Gonc-alvesa A, Lanzaa, L F, Cortesb, S F, Nunesa, K P.(2008), Tx2-6 toxin of the Phoneutria nigriventer spider potentiates rat erectile function. Toxicon; 51(7): pp1197-1206. 4. Cruz JS, Penaforte CL, Araújo DA, Matavel, A et.al. (2002), Electrophysiological characterization and molecular identification of the Phoneutria nigriventer peptide toxin PnTx2-6. FEBS Lett. 523: pp219–223. 5. Duarte, H, Damasceno, D, Martins, M, Kalapothakis, E et al. (1998), Cloning of cDNAs encoding neurotoxic peptides from the spider Phoneutria nigriventer. Toxicon; 36(16):pp 1843-1850. 6. Fontana, MD and Vital Brazil, O (1985), Mode of action of Phoneutria nigriventer spider venom at the isolated phrenic nerve-diaphragm of the rat. Braz. J. Med. Biol. Res. 18: pp557–565. 7. From Martindale, The Extra Pharmacopoeia, 30th Ed, P5. 8. Gomez, M V, Kalapothakis E, Guatimosim, C and Prado, M A M (1997), A toxin from the spider Phoneutria nigriventer that blocks calcium channels coupled to exocytosis. British Journal of Pharmacology; 122: pp591–597. 9. John J. Irwin and Brian K. Shoichet. (2005), ZINC – A Free Database of Commercially Available Compounds for Virtual Screening. J Chem Inf Model.45: pp 177–182. 10. Love, S and Cruz-Höfling, M. A. (1986), Acute swelling of nodes of Ranvier caused by venoms which slow inactivation of sodium channels. Acta Neuropathol. (Berl.) 70: pp1–9. 11. Lucas, S. (1988), Spiders in Brazil. Toxicon. 26: pp759-772. 12. Marangoni, S, Borges, N.C.C, Marangoni, R.A, Domont, G.B et al. (1993), Biochemical characterization of a vascular smooth muscle contracting polypeptide purified from Phoneutria nigriventer (armed spider) venom; 31(4): pp 377–384.

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13. Miranda, Debora Marques, Romano-Silva, Marcus V. Gomez, et.al. (2002), Phoneutria nigriventer venom: A cocktail of toxins that affect ion channels. cellular and molecular neurobiology.22(6): pp579-588. 14. Patil R, Das S, Stanley A, Sudhakar A, et al. (2010), Optimized Hydrophobic Interactions and Hydrogen Bonding at the Target-Ligand Interface Leads the Pathways of Drug-Designing. PLoS ONE 5(8) e12029. 15. Prado M.A.M, Gomez M.V, Kalapothakis E, Moura. J. R et.al. (1998), Investigation of the effect of PhTx2, from the venom of the spider Phoneutria nigriventer, on the release of [3H]-acetylcoline from rat cerebrocortical synaptosomes. Toxicon 36: pp1189–1192. 16. Prakash, P and Neelu Gupta. (2005), Therapeutic uses of Ocimum sanctum linn (tulsi) with a note on eugenol and its pharmacological actions: a short review. Indian J Physiol Pharmacol. 49(2): pp 125–131. 17. Ribeiro-Santos, R, Ribeiro, A M, Gomez,M V, Romano-Silva ,M A, et al. (1993), Rat cortical synaptosomes have more than one mechanism for calcium entry linked to rapid glutamate release: Studies using the Phoneutria nigriventer toxin PhTx2 and potassium depolarization, Journal of BioChemistry, 269, pp313–319. 18. Sali A and Blundell TL. (1993), Comparative protein modelling by satisfaction of spatial restraints. Journal of Molecular Biology 234(3), pp 779-815. 19. Schenberg, S. and Lima, F. A. (1966), Pharmacology of the polypeptides from the venom of the spider Phoneutria fera. Mem. Inst. Butantan. 33, pp627-638. 20. Svobodova J, Piackova V, Groch L , Velisek Z et.al. (2005), Effects of clove oil anaesthesia on common carp (Cyprinus carpio L.)Vet. Med. Czech. 50(6), pp 269– 275. 21. Yuzhen Ye and Adam Godzik. (2005), Multiple flexible structure alignment using partial order graphs. Bioinformatics. 21(10), pp 2362–2369.

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