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Combined chemical feature-based assessment and Bayesian model studies to identify potential inhibitors for Factor Xa

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Abstract

In our study, we have described chemical feature-based 3D QSAR pharmacophore models with help of known inhibitors of Factor Xa (FXa). The best model, Hypo1, has validated by various techniques to prove its robustness and statistical significance. The well validated Hypo1 was used as 3D query in the virtual screening to retrieve potential leads for FXa inhibition. The hit molecules were sort out by applying drug-like filters and molecular docking. Bayesian model was developed using training set compounds which provides molecular feature that are favoring or not favoring for FXa inhibition.

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Abbreviations

FXa:

Factor Xa

DS:

Discovery studio v2.5

HBA:

Hydrogen bond acceptor

HBD:

Hydrogen bond donor

H:

Hydrophobic

Haro:

Hydrophobic aromatic

Hali:

Hydrophobic aliphatic

R:

Ring aromatic

PI:

Positive ionizable

RMS:

Root mean square

EF:

Enrichment factor

GH:

Goodness of hit

ADMET:

Absorption, distribution, metabolism, excretion, and toxicity

BBB:

Blood–brain barrier

ECFP:

Extended-connectivity fingerprints

ROC:

Receiver operating curve

References

  • Adane L, Patel DS, Bharatam PV (2010) Shape- and chemical feature-based 3D-pharmacophore model generation and virtual screening: identification of potential leads for P. falciparum DHFR enzyme inhibition. Chem Biol Drug Des 75:115–126

    Article  PubMed  CAS  Google Scholar 

  • Adler M, Davey DD, Phillips GB, Kim S-H, Jancarik J, Rumennik G et al (2000) Preparation, characterization, and the crystal structure of the inhibitor ZK-807834 (CI-1031) complexed with factor Xa. Biochemistry 39:12534–12542

    Article  PubMed  CAS  Google Scholar 

  • Alban S (2005) From heparins to factor Xa inhibitors and beyond. Eur J Clin Invest 35:12–20

    Article  PubMed  CAS  Google Scholar 

  • Ansell J (2007) Factor Xa or thrombin: is factor Xa a better target? J Thromb Haemost 5(suppl 1):65–67

    Google Scholar 

  • Bajorath J (2002) Integration of virtual and high-throughput screening. Nat Rev Drug Discov 1:882–894

    Article  PubMed  CAS  Google Scholar 

  • Borensztajn K, Spek CA (2011) Blood coagulation factor Xa as an emerging drug target. Exp Opin Ther Targets 15:341–349

    Article  CAS  Google Scholar 

  • Carreiro J, Ansell J (2008) Apixaban, an oral direct Factor Xa inhibitor: awaiting the verdict. Exp Opin Investig Drugs 17:1937–1945

    Article  CAS  Google Scholar 

  • de Candia M, Lopopolo G, Altomare C (2009) Novel factor Xa inhibitors: a patent review. Exp Opin Ther Patents 19:1535–1580

    Article  Google Scholar 

  • Fischer R (1966) The principle of experimentation illustrated by a psycho-physical experiment, Chap II. Hafner Publishing, New York

    Google Scholar 

  • Gopalakrishnan B, Aparna V, Jeevan J, Ravi M, Desiraju GR (2005) A virtual screening approach for thymidine monophosphate kinase inhibitors as antitubercular agents based on docking and pharmacophore models. J Chem Inf Model 45(4):1101–1108

    Article  PubMed  CAS  Google Scholar 

  • Hahn M (1997) Three-dimensional shape-based searching of conformationally flexible compounds. J Chem Inf Comput Sci 37:80–86

    Article  CAS  Google Scholar 

  • Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267:727–748

    Article  PubMed  CAS  Google Scholar 

  • Kansal N, Silakari O, Ravikumar M (2009) A three dimensional pharmacophore modeling for KDR and Tie-2 receptor tyrosine kinase inhibitors and virtual screening for new multikinase inhibitors. QSAR Comb Sci 28:1130–1147

    Article  CAS  Google Scholar 

  • Klon AE, Lowrie JF, Diller DJ (2006) Improved naïve Bayesian modeling of numerical data for absorption, distribution, metabolism and excretion (ADME) property prediction. J Chem Inf Model 46:1945–1956

    Article  PubMed  CAS  Google Scholar 

  • Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23:3–25

    Article  CAS  Google Scholar 

  • MacKerell AD, Bashford D, Bellott, Dunbrack RL, Evanseck JD, Field MJ et al (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B 102:3586–3616

    Article  CAS  Google Scholar 

  • Putta S, Lemmen C, Beroza P, Greene J (2002) A novel shape-feature based approach to virtual library screening. J Chem Inf Comput Sci 42:1230–1240

    Article  PubMed  CAS  Google Scholar 

  • Ravikumar M, Pavan S, Bairy S, Pramod AB, Sumakanth M, Kishore M et al (2008) Virtual screening of cathepsin K inhibitors using docking and pharmacophore models. Chem Biol Drug Des 72:79–90

    Article  PubMed  CAS  Google Scholar 

  • Roehrig S, Straub A, Pohlmann J, Lampe T, Pernerstorfer J, Schlemmer K-H et al (2005) Discovery of the novel antithrombotic agent 5-chloro-N-({(5S)-2-oxo-3-[4-(3-oxomorpholin-4-yl)phenyl]-1,3-oxazolidin-5-yl}methyl)thiophene-2-carboxamide (BAY 59–7939):  an oral, direct factor Xa inhibitor. J Med Chem 48:5900–5908

    Article  PubMed  CAS  Google Scholar 

  • Rogers D, Brown RD, Hahn M (2005) Using extended-connectivity fingerprints with Laplacian-modified Bayesian analysis in high-throughput screening follow-up. J Biomol Screen 10:682–686

    Article  PubMed  CAS  Google Scholar 

  • Shi Y, Sitkoff D, Zhang J, Klei HE, Kish K, Liu ECK et al (2008) Design, structure–activity relationships, X-ray crystal structure, and energetic contributions of a critical P1 Pharmacophore: 3-chloroindole-7-yl-based factor Xa inhibitors. J Med Chem 51:7541–7551

    Article  PubMed  CAS  Google Scholar 

  • Shi Y, Li C, O’Connor SP, Zhang J, Shi M, Bisaha SN et al (2009) Aroylguanidine-based factor Xa inhibitors: the discovery of BMS-344577. Bioorg Med Chem Lett 19:6882–6889

    Article  PubMed  CAS  Google Scholar 

  • Smellie A, Kahn SD, Teig SL (1995a) Analysis of conformational coverage. 1. Validation and estimation of coverage. J Chem Inf Comput Sci 35:285a–294a

    Article  Google Scholar 

  • Smellie A, Kahn SD, Teig SL (1995b) Analysis of conformational coverage. 2. Applications of conformational models. J Chem Inf Comput Sci 35:295b–304b

    Article  Google Scholar 

  • Smellie A, Teig SL, Towbin P (1995c) Poling: promoting conformational variation. J Comput Chem 16:171c–187c

    Article  Google Scholar 

  • Srivastava HM, Chourasia M, Kumar D, Narahari Sastry G (2011) Comparison of computational methods to model DNA minor groove binders. J Chem Inf Model 51:558–571

    Article  PubMed  CAS  Google Scholar 

  • Taha MO, Qandil AM, Zaki DD, AlDamen MA (2005) Ligand-based assessment of factor Xa binding site flexibility via elaborate pharmacophore exploration and genetic algorithm-based QSAR modeling. Eur J Med Chem 40:701–727

    Article  PubMed  CAS  Google Scholar 

  • Wagner AB (2006) SciFinder Scholar 2006: an empirical analysis of research topic query processing. J Chem Inf Model 46:767–774

    Article  PubMed  CAS  Google Scholar 

  • Walenga JM, Jeske WP, Hoppensteadt D, Fareed J (2003) Factor Xa inhibitors: today and beyond. Curr Opin Investig Drugs 4:272–281

    PubMed  CAS  Google Scholar 

  • Wang Y, Bolton E, Dracheva S, Karapetyan K, Shoemaker BA, Suzek TO et al (2010) An overview of the PubChem BioAssay resource. Nucl Acids Res 38:D255–D266

    Article  PubMed  CAS  Google Scholar 

  • Wolber G, Langer T (2004) LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model 45:160–169

    Article  Google Scholar 

  • Yang Q, Du L, Tsai K-C, Wang X, Li M, You Q (2009) Pharmacophore mapping for Kv1.5 potassium channel blockers. QSAR Comb Sci 28:59–71

    Article  Google Scholar 

  • Yoshikawa K, Kobayashi S, Nakamoto Y, Haginoya N, Komoriya S, Yoshino T et al (2009) Design, synthesis, and SAR of cis-1,2-diaminocyclohexane derivatives as potent factor Xa inhibitors. Part II: Exploration of 6–6 fused rings as alternative S1 moieties. Bioorg Med Chem 17:8221–8233

    Article  PubMed  CAS  Google Scholar 

  • Zhang J, Liu G, Tang Y (2009) Chemical function-based pharmacophore generation of selective κ-opioid receptor agonists by catalyst and phase. J Mol Model 15:1027–1041

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

This research was supported by Basic Science Research Program (2009-0073267), Pioneer Research Center Program (2009-0081539), and Management of Climate Change Program (2010-0029084) through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (MEST) of Republic of Korea. And this study was also supported by the Next-Generation BioGreen21 Program (PJ008038) from Rural Development Administration (RDA) of Republic of Korea. Author thanks to Mr. Sundarapandian Thangapandian, Ph.D Scholar, Department of Biochemistry, and Gyeongsang National University, for his constant support in Bayesian Model part.

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Authors and Affiliations

  1. Division of Applied Life Science (BK21 Program), Systems and Synthetic Agrobiotech Center (SSAC), Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Research Institute of Natural Science (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Gazha-dong, Jinju, 660-701, Republic of Korea

    Meganathan Chandrasekaran, Sugunadevi Sakkiah & Keun Woo Lee

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  1. Meganathan Chandrasekaran
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  2. Sugunadevi Sakkiah
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  3. Keun Woo Lee
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Correspondence to Keun Woo Lee.

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44_2011_9936_MOESM1_ESM.tif

Fig. S1: 20 good molecular fingerprints that are favoring to inhibit FXa activity identified by ECPF_6 fingerprint descriptors. (TIFF 934 kb)

44_2011_9936_MOESM2_ESM.tif

Fig. S2: 20 bad molecular fingerprints that are not favoring to inhibit FXa activity identified by ECFP_6 fingerprint descriptors. (TIFF 932 kb)

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Chandrasekaran, M., Sakkiah, S. & Lee, K.W. Combined chemical feature-based assessment and Bayesian model studies to identify potential inhibitors for Factor Xa. Med Chem Res 21, 4083–4099 (2012). https://doi.org/10.1007/s00044-011-9936-2

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  • DOI: https://doi.org/10.1007/s00044-011-9936-2

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