Highly predictive hologram QSAR models of nitrile-containing cruzain inhibitors

References

• Assis, D. M., Gontijo, V. S., de Oliveira Pereira, I., Santos, J. A., Camps, I., Nagem, T. J., … Juliano, M. A. (2013). Inhibition of cysteine proteases by a natural biflavone: Behavioral evaluation of fukugetin as papain and cruzain inhibitor. Journal of Enzyme Inhibition and Medicinal Chemistry, 28, 661–670. doi:10.3109/14756366.2012.668539
   PubMed Web of Science ®Google Scholar
• Avelar, L. A., Camilo, C. D., de Albuquerque, S., Fernandes, W. B., Goncalez, C., Kenny, P. W., … Saidel, M. E. (2015). Molecular design, synthesis and trypanocidal activity of dipeptidyl nitriles as cruzain inhibitors. PLOS Neglected Tropical Diseases, 9, 1–24. doi:10.1371/journal.pntd.0003916
   Web of Science ®Google Scholar
• Beaulieu, C., Isabel, E., Fortier, A., Masse, F., Mellon, C., Methot, N., … Black, W. C. (2010). Identification of potent and reversible cruzipain inhibitors for the treatment of Chagas disease. Bioorganic & Medicinal Chemistry Letters, 20, 7444–7449. doi:10.1016/j.bmcl.2010.10.015
   PubMed Web of Science ®Google Scholar
• Bento, A. P., Gaulton, A., Hersey, A., Bellis, L. J., Chambers, J., Davies, M., … Overington, J. P. (2014). The ChEMBL bioactivity database: An update. Nucleic Acids Research, 42, D1083–D1090. doi:10.1093/nar/gkt1031
   PubMed Web of Science ®Google Scholar
• Bernstein, F. C., Koetzle, T. F., Williams, G. J. B., Meyer, E. F., Brice, M. D., Rodgers, J. R., … Tasumi, M. (1977). The Protein Data Bank: A computer-based archival file for macromolecular structures. European Journal of Biochemistry, 80, 319–324. doi:10.1111/j.1432-1033.1977.tb11885.x
   PubMedGoogle Scholar
• Boström, J., Greenwood, J. R., & Gottfries, J. (2003). Assessing the performance of OMEGA with respect to retrieving bioactive conformations. Journal of Molecular Graphics and Modelling, 21, 449–462. doi:10.1016/S1093-3263(02)00204-8
   PubMed Web of Science ®Google Scholar
• da Mota, E. G., Silva, D. G., Guimarães, M. C., da Cunha, E. F. F., & Freitas, M. P. (2013). Computer-assisted design of novel 1,4-dihydropyridine calcium channel blockers. Molecular Simulation, 40, 959–965. doi:10.1080/08927022.2013.829220
   Web of Science ®Google Scholar
• de Paula da Silva, C. H. T., Bernardes, L. S. C., da Silva, V. B., Zani, C. L., & Carvalho, I. (2012). Novel aryl β-aminocarbonyl derivatives as inhibitors of Trypanosoma cruzi trypanothione reductase: Binding mode revised by docking and GRIND2-based 3D-QSAR procedures. Journal of Biomolecular Structure and Dynamics, 29, 1206–1220. doi:10.1080/07391102.2011.672633
   Web of Science ®Google Scholar
• Durrant, J. D., Keränen, H., Wilson, B. A., & McCammon, J. A. (2010). Computational identification of uncharacterized cruzain binding sites. PLOS Neglected Tropical Diseases, 4, e676. doi:10.1371/journal.pntd.0000676
   PubMed Web of Science ®Google Scholar
• Freitas, R. F., Oprea, T. I., & Montanari, C. A. (2008). 2D QSAR and similarity studies on cruzain inhibitors aimed at improving selectivity over cathepsin L. Bioorganic & Medicinal Chemistry, 16, 838–853. doi:10.1016/j.bmc.2007.10.048
   PubMed Web of Science ®Google Scholar
• Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., … Shenkin, P. S. (2004). Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. Journal of Medicinal Chemistry, 47, 1739–1749. doi:10.1021/jm0306430
   PubMed Web of Science ®Google Scholar
• Gauthier, J. Y., Chauret, N., Cromlish, W., Desmarais, S., Duong, L. T., Falgueyret, J.-P., … Black, W. C. (2008). The discovery of odanacatib (MK-0822), a selective inhibitor of cathepsin K. Bioorganic & Medicinal Chemistry Letters, 18, 923–928. doi:10.1016/j.bmcl.2007.12.047
   PubMed Web of Science ®Google Scholar
• Geladi, P., & Kowalski, B. R. (1986). Partial least-squares regression – A tutorial. Analytica Chimica Acta, 185, 1–17. doi:10.1016/0003-2670(86)80028-9.
   Web of Science ®Google Scholar
• Gillmor, S. A., Craik, C. S., & Fletterick, R. J. (1997). Structural determinants of specificity in the cysteine protease cruzain. Protein Science, 6, 1603–1611. doi:10.1002/pro.5560060801
   PubMed Web of Science ®Google Scholar
• Golbraikh, A., Shen, M., Xiao, Z., Xiao, Y.-D., Lee, K.-H., & Tropsha, A. (2003). Rational selection of training and test sets for the development of validated QSAR models. Journal of Computer-Aided Molecular Design, 17, 241–253. doi:10.1023/a:1025386326946
   PubMed Web of Science ®Google Scholar
• Golbraikh, A., & Tropsha, A. (2002). Beware of q(2)! Journal of Molecular Graphics & Modelling, 20, 269–276. doi:10.1016/s1093-3263(01)00123-1
   PubMed Web of Science ®Google Scholar
• Gonzalez, M. P., Teran, C., Saiz-Urra, L., & Teijeira, M. (2008). Variable selection methods in QSAR: An overview. Current Topics in Medicinal Chemistry, 8, 1606–1627. doi:10.2174/156802608786786552
   PubMed Web of Science ®Google Scholar
• Grant, J. A., Gallardo, M. A., & Pickup, B. T. (1996). A fast method of molecular shape comparison: A simple application of a Gaussian description of molecular shape. Journal of Computational Chemistry, 17, 1653–1666. doi:10.1002/(sici)1096-987x(19961115)17:14<1653:aid-jcc7>3.0.co;2-k
   Web of Science ®Google Scholar
• Guimarães, M. C., da Mota, E. G., Silva, D. G., & Freitas, M. P. (2014). Aug-MIA-QSPR modelling of the toxicities of anilines and phenols to Vibrio fischeri and Pseudokirchneriella subcapitata. Chemometrics and Intelligent Laboratory Systems, 134, 53–57. doi:10.1016/j.chemolab.2014.03.005
   Web of Science ®Google Scholar
• Guimarães, M., Silva, D., da Mota, E., da Cunha, E. F., & Freitas, M. (2014). Computer-assisted design of dual-target anti-HIV-1 compounds. Medicinal Chemistry Research, 23, 1548–1558. doi:10.1007/s00044-013-0765-3
   Web of Science ®Google Scholar
• Halgren, T. A., Murphy, R. B., Friesner, R. A., Beard, H. S., Frye, L. L., Pollard, W. T., & Banks, J. L. (2004). Glide: A new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. Journal of Medicinal Chemistry, 47, 1750–1759. doi:10.1021/jm030644s
   PubMed Web of Science ®Google Scholar
• Hawkins, P. C. D., Skillman, A. G., & Nicholls, A. (2007). Comparison of shape-matching and docking as virtual screening tools. Journal of Medicinal Chemistry, 50, 74–82. doi:10.1021/jm0603365
   PubMed Web of Science ®Google Scholar
• Hawkins, P. C. D., Skillman, A. G., Warren, G. L., Ellingson, B. A., & Stahl, M. T. (2010). Conformer generation with OMEGA: Algorithm and validation using high quality structures from the protein databank and cambridge structural database. Journal of Chemical Information and Modeling, 50, 572–584. doi:10.1021/ci100031x
   PubMed Web of Science ®Google Scholar
• Heritage Trevor, W., & Lowis David, R. (1999). Molecular hologram QSAR rational drug design (Vol. 719, pp. 212–225). St. Louis, MO: American Chemical Society.
   Google Scholar
• Hoelz, L. V., Leal, V. F., Rodrigues, C. R., Pascutti, P. G., Albuquerque, M. G., Muri, E. M., & Dias, L. R. (2015). Molecular dynamics simulations of the free and inhibitor-Bound Cruzain systems in aqueous solvent: Insights on the inhibition mechanism in acidic pH. Journal of Biomolecular Structure and Dynamics, 28, 1–28. doi:10.1080/07391102.2015.1100139
   Web of Science ®Google Scholar
• Hohman, M., Gregory, K., Chibale, K., Smith, P. J., Ekins, S., & Bunin, B. (2009). Novel web-based tools combining chemistry informatics, biology and social networks for drug discovery. Drug Discovery Today, 14, 261–270. doi:10.1016/j.drudis.2008.11.015
   PubMed Web of Science ®Google Scholar
• Kerr, I. D., Lee, J. H., Farady, C. J., Marion, R., Rickert, M., Sajid, M., … Brinen, L. S. (2009). Vinyl sulfones as antiparasitic agents and a structural basis for drug design. Journal of Biological Chemistry, 284, 25697–25703. doi:10.1074/jbc.M109.014340
   PubMed Web of Science ®Google Scholar
• Khedkar, V. M., Joseph, J., Pissurlenkar, R., Saran, A., & Coutinho, E. C. (2015). How good are ensembles in improving QSAR models? The case with eCoRIA. Journal of Biomolecular Structure and Dynamics, 33, 749–769. doi:10.1080/07391102.2014.909744
   PubMed Web of Science ®Google Scholar
• Lowis, D. R. (1997). HQSAR: A new, highly predictive QSAR technique. Tripos Technical Notes, 1(5), 1–17.
   Google Scholar
• Maestro. (2013). ( Version 9.3). New York, NY: Schrödinger, LLC. Retrieved from http://www.schrodinger.com/citations/41/12/1
   Google Scholar
• Marvin. (2013). ( Version 6.0.4). Budapest: ChemAxon. Retrieved from https://www.chemaxon.com
   Google Scholar
• Mitra, I., Saha, A., & Roy, K. (2010). Exploring quantitative structure–activity relationship studies of antioxidant phenolic compounds obtained from traditional Chinese medicinal plants. Molecular Simulation, 36, 1067–1079. doi:10.1080/08927022.2010.503326
   Web of Science ®Google Scholar
• Myint, K. Z., & Xie, X. Q. (2010). Recent advances in fragment-based QSAR and multi-dimensional QSAR methods. International Journal of Molecular Sciences, 11, 3846–3866. doi:10.3390/ijms11103846
   PubMed Web of Science ®Google Scholar
• OMEGA. (2010). ( Version 2.5.1.4). Santa Fe: OpenEye Scientific Software.
   Google Scholar
• Polticelli, F., Zaini, G., Bolli, A., Antonini, G., Gradoni, L., & Ascenzi, P. (2005). Probing the cruzain S2 recognition subsite: A kinetic and binding energy calculation study. Biochemistry, 44, 2781–2789. doi:10.1021/bi048417v
   PubMed Web of Science ®Google Scholar
• Rodrigues, C. R., Flaherty, T. M., Springer, C., McKerrow, J. H., & Cohen, F. E. (2002). CoMFA and HQSAR of acylhydrazide cruzain inhibitors. Bioorganic & Medicinal Chemistry Letters, 12, 1537–1541. doi:10.1016/S0960-894X(02)00189-0
   PubMed Web of Science ®Google Scholar
• Roy, K., Chakraborty, P., Mitra, I., Ojha, P. K., Kar, S., & Das, R. N. (2013). Some case studies on application of ‘rm2’ metrics for judging quality of quantitative structure–activity relationship predictions: Emphasis on scaling of response data. Journal of Computational Chemistry, 34, 1071–1082. doi:10.1002/jcc.23231
   PubMed Web of Science ®Google Scholar
• Roy, K., Kar, S., & Ambure, P. (2015). On a simple approach for determining applicability domain of QSAR models. Chemometrics and Intelligent Laboratory Systems, 145, 22–29. doi:10.1016/j.chemolab.2015.04.013
   Web of Science ®Google Scholar
• Rush, T. S., 3rd, Grant, J. A., Mosyak, L., & Nicholls, A. (2005). A shape-based 3-D scaffold hopping method and its application to a bacterial protein-protein interaction. Journal of Medicinal Chemistry, 48, 1489–1495. doi:10.1021/jm040163o
   PubMed Web of Science ®Google Scholar
• Santos-Garcia, L., Assis, L. C., Silva, D. R., Ramalho, T. C., & da Cunha, E. F. (2015). QSAR analysis of nicotinamidic compouds and design of potential Bruton’s tyrosine kinase (Btk) inhibitors. Journal of Biomolecular Structure and Dynamics, 25, 1–50. doi:10.1080/07391102.2015.1070750
   Web of Science ®Google Scholar
• Senger, S. (2009). Using Tversky similarity searches for core hopping: Finding the needles in the haystack. Journal of Chemical Information and Modeling, 49, 1514–1524. doi:10.1021/ci900092y
   PubMed Web of Science ®Google Scholar
• Silva, D. G., Freitas, M. P., da Cunha, E. F. F., Ramalho, T. C., & Nunes, C. A. (2012). Rational design of small modified peptides as ACE inhibitors. Medchemcomm, 3, 1290–1293. doi:10.1039/c2md20214j
   Web of Science ®Google Scholar
• SYBYL-X. (2010). ( Version 1.2). St. Louis, MO: Tripos International.
   Google Scholar
• Triballeau, N., Acher, F., Brabet, I., Pin, J. P., & Bertrand, H. O. (2005). Virtual screening workflow development guided by the ‘receiver operating characteristic’ curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. Journal of Medicinal Chemistry, 48, 2534–2547. doi:10.1021/jm049092j
   PubMed Web of Science ®Google Scholar
• Tropsha, A. (2010). Best practices for QSAR model development, validation, and exploitation. Molecular Informatics, 29, 476–488. doi:10.1002/minf.201000061
   PubMed Web of Science ®Google Scholar
• Tropsha, A., & Golbraikh, A. (2007). Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Current Pharmaceutical Design, 13, 3494–3504. doi:10.2174/138161207782794257
   PubMed Web of Science ®Google Scholar
• Trossini, G. H., Malvezzi, A., T-do Amaral, A., Rangel-Yagui, C. O., Izidoro, M. A., Cezari, M. H., … Ferreira, E. I. (2010). Cruzain inhibition by hydroxymethylnitrofurazone and nitrofurazone: Investigation of a new target in Trypanosoma cruzi. Journal of Enzyme Inhibition and Medicinal Chemistry, 25, 62–67. doi:10.3109/14756360902941058
   PubMed Web of Science ®Google Scholar
• vROCS. (2007). ( Version 3.2.0.4). Santa Fe: OpenEye Scientific Software.
   Google Scholar
• WHO – World Health Organization: Chagas disease. (2015). Retrieved April 30, 2015, from http://www.who.int/topics/chagas_disease/en/
   Google Scholar
• Wiggers, H. J., Rocha, J. R., Cheleski, J., & Montanari, C. A. (2011). Integration of ligand- and target-based virtual screening for the discovery of Cruzain inhibitors. Molecular Informatics, 30, 565–578. doi:10.1002/minf.201000146
   PubMed Web of Science ®Google Scholar
• Wiggers, H. J., Rocha, J. R., Fernandes, W. B., Sesti-Costa, R., Carneiro, Z. A., Cheleski, J., … Montanari, C. A. (2013). Non-peptidic Cruzain inhibitors with trypanocidal activity discovered by virtual screening and in vitro assay. PLOS Neglected Tropical Diseases, 7, 1–11. doi:10.1371/journal.pntd.0002370
   Web of Science ®Google Scholar
• Wold, S., Sjostrom, M., & Eriksson, L. (2001). PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58, 109–130. doi:10.1016/s0169-7439(01)00155-1
   Web of Science ®Google Scholar
• Yang, P. Y., Wang, M., He, C. Y., & Yao, S. Q. (2012). Proteomic profiling and potential cellular target identification of K11777, a clinical cysteine protease inhibitor, in Trypanosoma brucei. Chemical Communications, 48, 835–837. doi:10.1039/c1cc16178d
   PubMed Web of Science ®Google Scholar
• Yang, P. Y., Wang, M., Li, L., Wu, H., He, C. Y., & Yao, S. Q. (2012). Design, synthesis and biological evaluation of potent Azadipeptide nitrile inhibitors and activity-based probes as promising anti-trypanosoma brucei agents. Chemistry–A European Journal, 18, 6528–6541. doi:10.1002/chem.201103322
   PubMed Web of Science ®Google Scholar