Redalyc.Generation of Pharmacophores and Classification

Revista Colombiana de Química ISSN: 0120-2804 [email protected] Universidad Nacional de Colombia Colombia

Velásquez, Eliana P.; Vásquez, Neil A.; Gutiérrez, Pablo A. Generation of pharmacophores and classification of drugs using protein-ligand complexes Revista Colombiana de Química, vol. 41, núm. 3, 2012, pp. 337-348 Universidad Nacional de Colombia Bogotá, Colombia

Available in: http://www.redalyc.org/articulo.oa?id=309031894001

How to cite Complete issue Scientific Information System More information about this article Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Journal's homepage in redalyc.org Non-profit academic project, developed under the open access initiative Revista Colombiana de Química, Volumen 41, nro. 3 de 2012

Rev. Colomb. Quím., 2012, 41(3): 337-348 Generation of pharmacophores and classification of drugs using protein-ligand complexes

Generación de farmacóforos y clasificación de drogas utilizando complejos proteína-ligando Orgánica y Bioquímica Geração de farmacóforos e classificação de fármacos usando-se complexo proteína-ligando

Eliana P. Velásquez1, Neil A. Vásquez2, Pablo A. Gutiérrez1*.

Recibido: 14/08/12 – Aceptado: 27/11/12

Abstract Resumen

Pharmacophore identification is a very La identificación de farmacóforos es important step in de novo design, lead uno de los pasos más importantes en optimization, chemogenomics, and vir- el diseño de novo, identificación de tual screening of drugs. Unfortunately, compuestos líder, quimiogenómica the high cost of comercial software for y tamizaje virtual de nuevos medica- pharmacophore detection is a common mentos. Sin embargo, el alto costo de limiting factor for researchers with limi- los paquetes comerciales de software ted funding. This paper presents a set of para la detección de farmacóforos es freely available perl routines that were un factor limitante para investigado- designed to help in the process of 3D res con recursos limitados. En este pharmacophore identification and QSAR artículo se presentan un conjuto de studies. These routines also allowed the rutinas en Perl que se diseñaron para classification of ligands based on their la identificación de farmacóforos en tridimensional similarity and binding 3 dimensiones y estudios de QSAR. mechanism. The family of phosphodies- Estas rutinas también permitieron una terases and their inhibitors were used as clasificación de ligandos basada en su test model. similitud tridimensional y mecanismo de unión. La utilidad de estos progra- Key words: pharmacophore, inhibi- mas se probó con los inhibidores de tor, protein, enzyme, drug. las fosfodiesterasas.

1 Group of microbial biotechnology. School of sciences. Universidad Nacional de Colombia. Medellín, Colombia. 2 Group of animal biotechnology. School of sciences. Universidad Nacional de Colombia. Correspondence concerning this paper should be addressed to Pablo A. Gutiérrez, [email protected]

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Palabras clave: farmacóforo, inhibition of pharmacophores is based on the dor, proteína, enzima, droga. alignment of protein-bound ligands and finding their common pharmacophore Resumo (1). This method also gives the highest level of resolution as the output consists A identificação de farmacóforos, é um of a 3D position of an atom associated dos passos mais importantes no desen- with its properties (6-8). The performan- ho de novo, identificação de compostos ce and applicability of pharmacophore líder, quimiogenômica e triagem virtual modeling depends on two main factors: de novos medicamentos. No entanto, o the definition and placement of phar- alto custo dos pacotes comerciais de soft- macophoric features and the alignment ware para a detecção de farmacóforos é techniques used to overlay 3D pharma- um fator limitante para os pesquisadores cophore models and small molecules. com recursos limitados. Neste artigo Identification of the pharmacophore can apresentamos um conjunto de rotinas em be a tedious and cumbersome task when Perl que foram desenhadas para a identi- many protein-ligand complexes are avai- ficação de farmacóforos em 3 dimensões lable. e estudos de QSAR. Estas rotinas permi- tiram uma classificação de ligandos ba- In this case, it is necessary to su- seada na similitude tridimensional e nos perimpose available structures of the mecanismos de união. A utilidade dos protein-ligand complexes. Unfortuna- programas foi testada com os inibidores tely, compounds of different nature can da família das fosfodiesterases. bind to the same protein pocket and, so- metimes, the same compound can bind Palavras-chave: farmacóforo, inibi- in multiple conformations. For these dor, proteína, enzima, medicamento. reasons it is difficult to generalize the chemical features required for binding. Introduction Additionaly, the high cost of comercial software for pharmacophore identifica- The pharmacological effect of drugs is tion can be a limiting factor for resear- generally the result of their interaction chers with limited funding. with a specific protein target. Com- pounds with similar activities at the same This paper presents a set of freely enzyme or receptor must possess related available Perl routines designed to help properties that facilitate their specific in the process of 3D pharmacophore binding. A pharmacophore is defined as identification and QSAR studies. The- the 3D arrangement of ligand features se routines also allow the classification responsible for its activity (1, 2). Iden- of ligands based on their tridimensional tification of the pharmacophore is a very similarity and binding mechanism. The important step in de novo design, lead family of phosphodiesterases (PDE) and optimization, ADME/TOX studies, che- their inhibitors was used as test model, mogenomics, and virtual screening (3-5). which comprises a complex set of li- The simplest approach for the identifica- gands that can also bind in different conformations.

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Materials and methods A∪B − A ∩ B J∂ ()A,B = Equation (1) A ∩ B Scripts |A∩B| corresponds to the number of All scripts were written in Perl 5.10 (9). atoms closer than 0.7 Å between com- Scripts and instructions on how to use pounds A and B. A∪B is the total them are freely available upon request to atom count for both compounds. Clus- Orgánica y Bioquímica the corresponding author. tering was done using the Neighbor- Joining method of Nei and Saitou (11) Data implemented in PHYLIP (12). The tree was drawn using Dendroscope (13). Comparisons were performed on the fo- llowing set of phosphodiesterase complexes obtained from the Protein Data Bank. Pharmacophore detection and scoring • PDE3: 1SO2, 1SOJ. A total of 27 PDE4D inhibitors were • PDE4: 1PTW, 2QYK, 3I8V, 1RO6, used. The pharmacophore was built by 1RO9, 1ROR, 1TB5, 1XLX, 1XLZ, sequentially averaging the position of 1XM4, 1XM6, 1XMU, 1XMY, each pair of atoms closer than a thres- 1XN0, 1XOS, 1XOT, 1Y2H, 1Y2J, hold distance of 1.2 Å. The total number 2QYL, 1MKD, 1OYN, 1Q9M, of atoms used for each average was writ- 1TB7, 1TBB, 1XOM, 1XON, ten into the temperature factor field of 1XOQ, 1XOR, 1Y2B, 1Y2C, 1Y2D, the PDB field. Atom type was assigned 1Y2E, 1Y2K, 1ZKN, 2FM0, 2FM5, to the most frequent atom in the average. 2PW3, 2QYN, 3D3P, 3G45, 3G4G, Scores were assigned to each inhibitor by 3G4I, 3G4K, 3G4L, 3G58, 3IAD, adding the temperature field of each mat- 3IAK, 3K4S, 3KKT. ching atom of the pharmacophore.

• PDE5: 1UDT, 1UDU, 1UHO, 1T9S, 1TBF, 1XOZ, 1XP0. Results and discussion

• PDE7: 1ZKL. Phosphodiesterases are a diverse family of enzymes that hydrolyse cyclic nucleo- A list of all ligands and their structu- tides that play a key role in regulating res is shown in table 1 and Figure 1. intracellular levels of the second mes- sengers cAMP and cGMP (14). PDEs are clinical targets for a range of biolo- Cluster analysis gical disorders, such as congestive heart Ligands were superimposed with PyMOL failure, asthma, chronic obstructive (10). Dissimilarity between compounds pulmonary disease, depression, retinal was measured using the Jaccard distance degradation, and inflammation (15-17). defined by equation 1. Currently there is great interest in de- veloping new phosphodiesterase inhibitors with higher selectivity and lower

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Table 1. Identification codes of compounds used in this study.

ID Name 0CP 5-{3-[(1S,2S,4R)-bicyclo[2.2.1]hept-2-yloxy]-4-methoxyphenyl}tetrahydropyrimidin-2(1H)-one 15X 1-[4-[[2-fluoro-4-methoxy-3-(3-nitrophenyl)phenyl]methyl]phenyl]urea N-benzyl-1-ethyl-4-(tetrahydro-2H-pyran-4-ylamino)-1H-pyrazolo[3,4-b]pyridine-5-carboxami- 20A de 4-(ethoxycarbonyl)-3,5-dimethyl-1-phenyl-1H-pyrazol-2-ium ethyl 3,5-dimethyl-1-phenyl- 3DE pyrazol-2-ium-4-carboxylate 4DE Ethyl 1-(4-methoxyphenyl)-3,5-dimethyl-1H-pyrazole-4-carboxylate 5DE Ethyl 1-(4-aminophenyl)-3,5-dimethyl-1H-pyrazole-4-carboxylate 5GP Guanosine-5’-monophosphate 5RM (R)-Mesopram (5R)-6-(4-{[2-(3-iodobenzyl)-3-oxocyclohex-1-en-1-yl]amino}phenyl)-5-methyl-4,5-dihydro- 666 pyridazin-3(2H)-one 6DE Ethyl 1-(2-chlorophenyl)-3,5-dimethyl-1H-pyrazole-4-carboxylate 7DE Ethyl 3,5-dimethyl-1-(3-nitrophenyl)-1H-pyrazole-4-carboxylate 8BR 8-bromo-adenosine-5’-monophosphate 988 8-(3-nitrophenyl)-6-(pyridin-4-ylmethyl)quinoline AMP Adenosine monophosphate CIA Tadalafil CIO Cilomilast CMP Adenosine-3’,5’-cyclic-monophosphate D71 1-(3-nitrophenyl)-3-(pyridin-4-ylmethyl)pyrido[3,2-e]pyrimidine-2,4-dione DEE ethyl 3,5-dimethyl-1H-pyrazole-4-carboxylate EV1 Papaverine FIL Filamilast IBM 3-isobutyl-1-methylxanthine 2-{2-[(1R)-1-[3-(cyclopropyloxy)-4-(difluoromethoxy)phenyl]-2-(1-oxidopyridin-3-yl)ethyl]-1,3- M98 thiazol-5-yl}-1,1,1,3,3,3-hexafluoropropan-2-ol 2-{2-[(1S)-1-[3-(cyclopropyloxy)-4-(difluoromethoxy)phenyl]-2-(1-oxidopyridin-3-yl)ethyl]-1,3- M99 thiazol-5-yl}-1,1,1,3,3,3-hexafluoropropan-2-ol NPV 4-[8-(3-nitrophenyl)-1,7-naphthyridin-6-yl]benzoic acid 0MO (4R)-4-[(3-butoxy-4-methoxy-phenyl)methyl]imidazolidin-2-one PIL Piclamilast ROF Roflumilast ROL Rolipram VDN Vardenafil VIA Sildenafil ZAR Zardaverine

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N N Br N O O N O O N O O N N O N O N O P O P O O N O O P N N N O N N O O N N OOP O O O O O O N N O O O N CMP AMP 8BR 5GP

O O O O O Cl O O O O N O N O N O N N O O O O N N N Cl ROL FIL CIO PIL 0CP Orgánica y Bioquímica

Cl F O N N O F O O O N N F N O F O N O O O O Cl O O O NN O O O EV1 5RM OMO ROF ZAR

O O F F O O F O F F O F N N F N F N N N F O F O N S S N N F F F F N N F F O N M99 M98 O S N N O S O N N O O O O VDN VIA

O O O O O N O N N O O O N N N O N N N N N O N+ N 5DE 3DE 4DE DEE O O- IBM O- + + - N N N O N N+ O O O- O Cl O N+ O O N O O N O N N N N N N N N O O 6DE 7DE D71 988 NVP

O O O O N+ O O l N N

O N O N N N N N N N F N O N N O O O 15X 666 CIA 20A

Figure 1. Chemical structures of the set compounds used in this study. side effects (18). Our set of routines was ware such as Phylip or dendroUPGMA tested to help understand the binding (12, 19), (in Figure 3). Scripts were mechanisms of PDE inhibitors and sup- tested using 32 PDE inhibitors from a port the in silico evaluation of potential total of 59 PDB files. However some li- novel drugs. A flowchart describing the gands can bind to the same protein tar- sequential use of these routines is shown get in two or three alternative confor- in Figure 2. mations (ROL, CIO, ZAR). In total, 66 ligand conformations were analyzed. As Clustering of compounds The first a first step, protein complexes must be set of scripts performs the structural superimposed. The superposition script, alignment of compounds and calculates superposition.pl, produces a PyMOL a distance matrix that can use as input routine that will automatically perform a many freely available clustering soft- 3D alignment on the selected structures.

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Protein-drug complexes (PDB)

Superposition.pl Pharmacophore.pl Superimposed Pharmacophore ligands (PDB) table

Distance.pl Pharma2PDB.pl Distance Pharmacophore table (PDB) Clustering (Phylip, software DendroUPGMA) Scores.pl Table of scores Tree Superimposed of compounds ligands (PDB)

Figure 2. Flowchart explaining the input and output files of each script

The output will be a set of superimposed Compounds were clustered using the ligands in PDB format. All protein in- distance.tbl file and the Neighbor-Joi- formation and other non-relevant atoms ning algorithm (11). Nine well-defined will be removed at this stage. Output sets of compounds, A-H, were obtained files are named using the compound (in Figure 4). Cluster A corresponds to name, chain id and pdb code from the rolipram and new generation inhibitors protein data bank. For example, file sharing the 4-methoxy-phenyl substruc- ROL_B1OYN_lig.pdb corresponds to ture. Depending on the type of subs- the rolipram (ROL) in conformation B tituents and PDE type, this cluster can as observed in the 1OYN pdb file. Su- be further divided into 5 distinct subsets perimposed ligands are used as input for (A1-A5). It is evident that Rolipram can the distance.pl script that will calculate bind in many different conformations, the Jaccard distance for each pair of as reported previously (20). Cluster B compounds. A value of zero indicates a group inhibitor NVP bound to diferent complete 3D match correspondence for phosphodiesterases: PDE4A (NPV_ all atoms present in both ligands. A Jac- A2QYN), PDE4B (NPV_A 2QYL) and card distance of one corresponds to zero 3D matches. A match is defined as a pair PDE4D (NPV_A 2QYK). The active of atoms separated by a distances small- sites of PDE4B and PDE4D are mostly er than 0.7 Å. The user can adjust this comparable. However, PDE4A shows threshold value. A graphical comparison significant displacements of the residues of Jaccard distance between cilomilast next to the invariant glutamine residue (CIO) and some selected compounds is that is critical for substrate and inhibitor shown in Figure 3. The output of the binding (21). This difference is clearly distance.pl script will be a square matrix seen in tree, were PDEA is an outgroup containing the Jaccard distance for each of compounds PDE4B and PDE4D. pair of compounds saved as a distance. Cluster C is comprised by zardaverine tbl file (in Figure 3). and papaverine.

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CIO vs FIL CIO vs PIL CIO vs ROL CIO vs ROF

CIO vs D71 CIO vs EV1 CIO vs IBM CIO vs NPV Orgánica y Bioquímica

CIO_A1XLX 0.0000 0.7736 0.8400 0.2174 0.8537 0.8462 0.4400 0.5294 0.4667 D71_A3G4I 0.7736 0.0000 0.8868 0.7551 0.9091 0.8909 0.8491 0.8519 0.7500 EV1_A3IAK 0.8400 0.8868 0.0000 0.7826 0.8537 0.7692 0.8400 0.7255 0.9111 FIL_A1XLZ 0.2174 0.7551 0.7826 0.0000 0.8378 0.8333 0.5217 0.4894 0.2195 IBM_A1ZKN 0.8537 0.9091 0.8537 0.8378 0.0000 0.8140 0.8537 0.8571 0.8333 NPV_A2QYN 0.8462 0.8909 0.7692 0.8333 0.8140 0.0000 0.8077 0.8113 0.8298 PIL_A1XM4 0.4400 0.8491 0.8400 0.5217 0.8537 0.8077 0.0000 0.0980 0.6000 ROF_A1XMU 0.5294 0.8519 0.7255 0.4894 0.8571 0.8113 0.0980 0.0000 0.4783 ROL_A1Q9M 0.4667 0.7500 0.9111 0.2195 0.8333 0.8298 0.6000 0.4783 0.0000

Figure 3. Graphical illustration of the Jaccard distance between cilomilast and some selected compounds (above). The corresponding distance.tbl file is shown below.

These compounds share the quire a diferent binding conformation Dialkoxyphenyl group present in ro- (23). Cluster F comprises compounds lipram but bind PDEs with lower 1-7DEE, which have a pyrazole car- affinity. These compounds fill only a boxylic ester scaffold with substitu- portion of the active site pocket and tions at three sites (24). Finally, cluster lack additional functional groups that H is composed by nucleotides and ana- can utilize the remaining empty space logs. Four compounds did not cluster (22). Clusters D and G group varde- into any group: tadalafil (CIA), IBMX nafil (VDN) and sildenafil (VIA). The- (IBM), 666 and 20A. An illustration of se clusters correspond to complexes all clusters is shown in Figure 5. with PDE4B and PDE5 respectively. The binding mechanism for these com- Building of pounds is highly dependent on the type pharmacophores of phosphodiesterase. This is why the same compound belongs to two diffe- Three-dimensional (3D) pharmacophore rent clusters. Inhibitors from cluster E modeling is a technique for describing include compounds with a 3-nitrophen- the interaction of a small molecule ligand yl group such as 988, D71 and 15X. with a macromolecular target. These 3D In spite of being very similar to NPV, pharmacophores can be used to search for these compounds are bulkier and re- similarities between binding situations or

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ROL_B1TBB 0CP_A3KKT ROL_A1XN0 ROL_A1XMY ROL_B1XN0 FIL_A1XLZ ROL_A1TBB A1 ROL_A1OYN ROL_A1Q9M ROL_B1OYN CIO_A1XLX CIO_A1XOM 0MO_A3I8V 0MO_A3K4S 5RM_A1XM6 A2 A CIO_B1XOM ROL_A3G4K M98_A2FM0 M99_A2FM5 A3 PIL_A1XM4 PIL_A1XON ROF_A1XOQ A4 ROF_A1XMU ROF_A3G4L ROL_A1RO6 ROL_B1RO6 A5 CIA_A1UDU CIA_A1XOZ NPV_A2QYN NPV_A2QYK NPV_A2QYL B ZAR_A1XOR ZAR_B1XOR ZAR_C1XOR EV1_A3IAK C ZAR_A1MKD IBM_A1ZKL VDN_A1XOT VIA_A1XOS D71_A3G4G D D71_A3G4I 988_A3G58 15X_A3IAD E 988_A3G45 666_A1SO2 20A_A3D3P IBM_A1ZKN 4DE_A1Y2D DEE_A1Y2B 3DE_A1Y2C 5DE_A1Y2E 6DE_A1Y2H F 7DE_A1Y2J 7DE_A1Y2K IBM_A1SOJ VDN_A1UHO VIA_A1UDT VDN_A1XP0 VIA_A1TBF G 8BR_A1RO9 CMP_A2PW3 AMP_A1ROR AMP_A1PTW 5GP_A1T9S AMP_A1TB5 H 0.1 AMP_A1TB7

Figure 4. Neigbor-Joining tree of PDE inhibitors even for similarities between molecules atom position is weighted by number of (25). The generation of pharmacophores atoms averaged to give the mean their requires the use of the pharmacophore. 3D position. This information is record- pl script that will generate average co- ed under the temperature factor field of ordinate for atoms in the vicinity of a the PDB file (9). The atom type field is threshold value set by the user. In our assigned to the most frequent atom at case a cut-off distance of 1.2 Å gave the that position. The resulting pharmacoph- most satisfactory results. This file con- ore for PDE4D inhibitors is presented in tains an atom count and coordinates for Figure 5 together with examples of how each atom in the pharmacophore. The this pharmacophore fits some selected pharmacophore.tbl file can be converted compounds. Their pharmacophoric score into a PDB file using the pharma2PDB. is shown in parentheses. pl script. In the resulting PDB file, each

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Cluster B Cluster D Orgánica y Bioquímica

Cluster E Cluster G Cluster H B Pharmacophore CMP(415) IBM(400) NPV(616)

EV1(544) ROL(700) CIO(733) M98(834)

Figure 5. Graphical illustration of the clusters from the tree of compounds (A) and the fit between pharmacophore and some selected compounds (B).

A pharmacophore model has to show greatly improve the binding. Our scoring predictive power that enables the design scheme depends on the total number of of novel chemical structures (26). In or- atoms averaging to a given position. It is der to measure how similar a compound obvious that in some cases strong inhibi- is to the pharmacophore, script score.pl tors may have some unique substituents was written which will add the score of that will have low scores in the pharma- each atom of the reference compound. A cophore. The pharmacophore scores can satisfactory model relating scores with be corrected empirically, by inclusion

IC50 data has been obtained (R=0.6) of new parameters or adjustement of (in Figure 6). A closer look at the data weights, to give a more acurate predic- shows that zardaverin, piclomilast, and tion of inhibition constants. However, cilomilast have an IC50 at least two order these corrections are very case-depen- of magnitude lower than the prediction dent and correspond to a second phase from our scoring scheme. These devia- of QSAR studies, which are beyond the tions suggest molecular features that scope of the scripts in this study.

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1000000 IBM R2=0.6 100000 DEE

10000 EV1 4DE 1000 NPV ROL ZAR 3DE (nM) 5DE 50 100 M99 IC D71 10 7DE

1 ROF M98 0.1 PIL CIO 00.1 300 400 500 600 700 800 900 Score

Figure 6. Relationship between pharmacophore scores and IC50 for PDE4D inhibitors.

Conclusions Acknowledgements

A set of useful routines for the classifica- The presentwork was funded by tion of drugs, 3D pharmacophore iden- Universidad Nacional de Colombia, tification and preliminary, and QSAR Dirección de Investigaciones Medellín studies has been presented. These pro- (DIME), research grant 90201020. grams are freely available and can give an accurate overview of the binding References mechanism for large set of compounds. 3D pharmacophore can greatly simplify 1. Podolyan, Y.; Karypis, G. Common the molecular features required for inhi- pharmacophore identification using bitors to interact with a specific protein frequent clique detection algorithm. target. It was also proven that our sco- J Chem Inf Model. 2009; 49(1): 13- ring schemes has predictive power and 21. can be used to help in the de novo de- 2. Holtje, H. D.; Sippl, W.; Rognan, sign, lead optimization, chemogenomics D.; Folkers, G. Molecular Mode- or virtual screening of novel compounds. ling. 1st ed. Weinheim, Germany: The scripts used in this work will be very Wiley-VCH; 2008. 310 p. useful for research groups with limited funding to afford the expensive licenses 3. Dror, O.; Schneidman-Duhovny, of commercial software. D.; Inbar, Y.; Nussinov, R.; Wol-

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fson, H. J. Novel approach for effi- Scientific LLC, San Carlos, CA, cient pharmacophore-based virtual USA. 2002. Available from: http: // screening: method and applications. www.pymol.org. J Chem Inf Model. 2009; 49(10): 2333-43 11. Saitou, N.; Nei, M. The neighbor- joining method: a new method for 4. Tripathi, A.; Surface, J. A.; Ke- reconstructing phylogenetic trees. Orgánica y Bioquímica llogg, G. E. Using active site map- Mol Biol Evol. 1987; 4(4): 406-25. ping and receptor-based pharmaco- 12. Felsenstein, J. PHYLIP - Phylogeny phore tools: prelude to docking and Inference Package (Version 3.2). de novo/fragment-based ligand de- Cladistics. 1989. 5: 164-166 sign. Methods Mol Biol. 2011; 716: 39-54. 13. Huson, D. H.; Richter, D. C.; Rausch, C.; Dezulian, T.; Franz, 5. Klabunde, T. Chemogenomic Ap- M.; Rupp, R. Dendroscope: An proaches to Drug Discovery: Simi- interactive viewer for large phylo- lar Receptors Bind Similar Ligands. genetic trees. BMC Bioinformatics. Br J Pharmacol 2007; 152: 5–7. 2007; 8: 460.

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