An updated steroid benchmark set and its application in the discovery of novel nanomolar ligands of sex hormone-binding globulin
Abstract
A benchmark data set of steroids with known affinity for sex hormone-binding globulin (SHBG) has been widely used to validate popular molecular field-based QSAR techniques. We have expanded the data set by adding a number of nonsteroidal SHBG ligands identified both from the literature and in our previous experimental studies. This updated molecular set has been used herein to develop 4D QSAR models based on "inductive" descriptors and to gain insight into the molecular basis of protein-ligand interactions. Molecular alignment was generated by means of docking active compounds into the active site of the SHBG. Surprisingly, the alignment of the benchmark steroids contradicted the classical ligand-based alignment utilized in previous CoMFA and CoMSIA models yet afforded models with higher statistical significance and predictive power. The resulting QSAR models combined with CoMFA and CoMSiA models as well as structure-based virtual screening allowed discovering several low-micromolar to nanomolar nonsteroidal inhibitors for human SHBG.
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SHBG binds sex steroids (including active androgens, oestrogens, precursors and metabolites) with high specificity and affinity (nanomolar ranges).
“In contrast, SHBG binds sex steroids (including active androgens, oestrogens, precursors and metabolites (Avvakumov et al., 2010, Cherkasov et al., 2008, Dunn et al., 1981, Grishkovskaya et al., 2002)) with high specificity and affinity (nanomolar ranges) (reviewed in (Hammond, 2016)).”
From Intracrine androgen biosynthesis, metabolism and action revisited (2018) · cites this paper as Cherkasov et al., 2008
Cites
Adaptation in Natural and Artificial Systems
Holland, J. H. (1992) · The MIT Press eBooks · doi:10.7551/mitpress/1090.001.0001 · Cited by 47,340 · see citing works
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works
Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94
Halgren, T. A. (1996) · Journal of Computational Chemistry · doi:10.1002/(sici)1096-987x(199604)17:5/6 · Cited by 5,321 · see citing works
ZINC − A Free Database of Commercially Available Compounds for Virtual Screening
Irwin, J. J., & Shoichet, B. K. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci049714+ · Cited by 4,058 · see citing works
Cramer, R. D., Patterson, D. E., & Bunce, J. D. (1988) · Journal of the American Chemical Society · doi:10.1021/ja00226a005 · Cited by 3,931 · see citing works
A Critical Assessment of Docking Programs and Scoring Functions
Warren, G. L., Andrews, C. W., Capelli, A., Clarke, B., LaLonde, J. M., Lambert, M. H., Lindvall, M., Nevins, N., Semus, S. F., Senger, S., et al. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm050362n · Cited by 1,840 · see citing works
Klebe, G., Abraham, U., & Mietzner, T. (1994) · Journal of Medicinal Chemistry · doi:10.1021/jm00050a010 · Cited by 1,822 · see citing works
Rogers, D. O. W., & Hopfinger, A. J. (1994) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00020a020 · Cited by 988 · see citing works
Virtual ligand screening: strategies, perspectives and limitations
Klebe, G. (2006) · Drug Discovery Today · doi:10.1016/j.drudis.2006.05.012 · Cited by 669 · see citing works
Westphal, U. (1971) · Monographs on endocrinology · doi:10.1007/978-3-642-46262-7 · Cited by 415 · see citing works
Consensus scoring for protein–ligand interactions
Fehér, M. (2006) · Drug Discovery Today · doi:10.1016/j.drudis.2006.03.009 · Cited by 232 · see citing works
Hammond, G. L., & Lähteenmäki, P. L. A. (1983) · Clinica Chimica Acta · doi:10.1016/0009-8981(83)90237-1 · Cited by 218 · see citing works
Self-citationJain, A. N., Koile, K., & Chapman, D. (1994) · Journal of Medicinal Chemistry · doi:10.1021/jm00041a010 · Cited by 177 · see citing works
Self-Organizing Molecular Field Analysis: A Tool for Structure−Activity Studies
Robinson, D. D., Winn, P. J., Lyne, P. D., & Richards, W. G. (1999) · Journal of Medicinal Chemistry · doi:10.1021/jm9810607 · Cited by 146 · see citing works
Some uses of a best molecular fit routine
Nyburg, S. C. (1974) · Acta Crystallographica Section B · doi:10.1107/s0567740874002603 · Cited by 134 · see citing works
Self-Organizing Molecular Field Analysis (SOMFA): A Tool for Structure-Activity Studies
Winn, P. J., Robinson, D. D., & Richards, W. G. (2001) · Mathematical and computational chemistry · doi:10.1007/978-1-4757-3273-3_13 · Cited by 103 · see citing works
The comparative molecular surface analysis (COMSA): a novel tool for molecular design
Polański, J., & Walczak, B. (2000) · Computers & Chemistry · doi:10.1016/s0097-8485(00)00064-4 · Cited by 93 · see citing works
Grishkovskaya, I., Avvakumov, G. V., Hammond, G. L., Catalano, M. G., & Muller, Y. A. (2002) · Journal of Biological Chemistry · doi:10.1074/jbc.m203999200 · Cited by 73 · see citing works
Self-citationThe problem of the quantitative evaluation of the inductive effect: correlation analysis
Cherkasov, A. R., Galkin, V. I., & Cherkasov, R. А. (1996) · Russian Chemical Reviews · doi:10.1070/rc1996v065n08abeh000227 · Cited by 61 · see citing works
Self-citationInductive Descriptors: 10 Successful Years in QSAR
Cherkasov, A. R. (2005) · Current Computer - Aided Drug Design · doi:10.2174/1573409052952288 · Cited by 58 · see citing works
Self-citationQSAR Study of Steroid Benchmark and Dipeptides Based on MEDV-13
Liu, S., Yin, C., Li, Z., et al. (2001) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0003350 · Cited by 54 · see citing works
Multivariate Adaptive Regression Splines
Kooperberg, C. L. (2014) · Wiley StatsRef: Statistics Reference Online · doi:10.1002/9781118445112.stat07551 · Cited by 48 · see citing works
Turner, D. B., Willett, P., Ferguson, A. M., et al. (1999) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1008012732081 · Cited by 46 · see citing works
Combined MEDV-GA-MLR Method for QSAR of Three Panels of Steroids, Dipeptides, and COX-2 Inhibitors
Liu, S., Yin, C., & Wang, L. (2002) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci010245a · Cited by 45 · see citing works
Karakoç, E., Sahinalp, C., & Cherkasov, A. R. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci0601517 · Cited by 45 · see citing works
Self-citationAsikainen, A. H., Ruuskanen, J., & Tuppurainen, K. A. (2004) · SAR and QSAR in environmental research · doi:10.1080/1062936032000169642 · Cited by 45 · see citing works
Cherkasov, A. R., Ban, F., Li, Y. Y., Fallahi, M., & Hammond, G. L. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm060961+ · Cited by 42 · see citing works
Self-citationTuppurainen, K. A., Viisas, M., Laatikainen, R., et al. (2002) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0103830 · Cited by 42 · see citing works
Successful in Silico Discovery of Novel Nonsteroidal Ligands for Human Sex Hormone Binding Globulin
Cherkasov, A. R., Shi, Z., Fallahi, M., & Hammond, G. L. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm049087f · Cited by 41 · see citing works
Self-citationDistance based algorithms for small biomolecule classification and structural similarity search
Karakoç, E., Cherkasov, A. R., & Sahinalp, C. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl259 · Cited by 36 · see citing works
Self-citationHammond, G. L., Avvakumov, G. V., & Muller, Y. A. (2003) · The Journal of Steroid Biochemistry and Molecular Biology · doi:10.1016/s0960-0760(03)00195-x · Cited by 27 · see citing works
Self-citationCan ‘Bacterial-Metabolite-Likeness' Model Improve Odds of ‘in Silico' Antibiotic Discovery?
Cherkasov, A. R. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci050480j · Cited by 23 · see citing works
Self-citationKorhonen, S., Tuppurainen, K. A., Laatikainen, R., et al. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci034027o · Cited by 20 · see citing works
Tuppurainen, K. A., Viisas, M., Peräkylä, M., et al. (2004) · Journal of Computer-Aided Molecular Design · doi:10.1023/b:jcam.0000035198.11110.48 · Cited by 15 · see citing works
Cherkasov, A. R., Shi, Z., Li, Y. Y., Jones, S. J. M., Fallahi, M., & Hammond, G. L. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci0498158 · Cited by 15 · see citing works
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Intracrine androgen biosynthesis, metabolism and action revisited
Schiffer, L., Arlt, W., & Storbeck, K.-H. (2018) · Molecular and Cellular Endocrinology · doi:10.1016/j.mce.2017.08.016 · Cited by 217 · see citing works
“In contrast, SHBG binds sex steroids (including active androgens, oestrogens, precursors and metabolites (Avvakumov et al., 2010; Cherkasov et al., 2008; Dunn et al., 1981; Grishkovskaya et al., 2002)) with high specificity and affinity (nanomolar ranges) (reviewed in (Hammond, 2016)).”
Mazer, N. A. (2009) · Steroids · doi:10.1016/j.steroids.2009.01.008 · Cited by 189 · see citing works
Structural analyses of sex hormone-binding globulin reveal novel ligands and function
Avvakumov, G. V., Cherkasov, A., Muller, Y. A., & Hammond, G. L. (2010) · Molecular and Cellular Endocrinology · doi:10.1016/j.mce.2009.09.005 · Cited by 91 · see citing works
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- Authors
- Cherkasov, A., Ban, F., Santos-Filho, O., Thorsteinson, N., Fallahi, M., & Hammond, G. L.
- Affiliations
- University of British Columbia (CA)
- Prostate Centre at the Vancouver General Hospital, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, and Department of Obstetrics and Gynecology, University of British Columbia, Child & Family Research Institute, Vancouver, British Columbia
- Corresponding author
- Cherkasov, Artem
- Venue
- Journal of Medicinal Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2008-03-11
- Volume
- 51
- Issue
- 7
- Pages
- 2047-2056
- ISSN
- 0022-2623 (ISSN-L)
- 1520-4804
- OpenAlex
- W2055610345
- Cited by
- 42
- References
- 35
- Citations by year
- 2024: 1 · 2022: 1 · 2021: 1 · 2020: 1 · 2019: 2 · 2018: 3 · 2017: 5 · 2016: 7 · 2014: 2 · 2013: 3 · 2012: 3
- FWCI
- 5.69
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Medicinal Chemistry (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Interaction Studies and Fluorescence Analysis
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- sex hormone-binding globulin, 4D-QSAR, molecular alignment, CoMFA, CoMSIA, structure-based virtual screening, protein-ligand interactions, nanomolar inhibitors
- MeSH terms
- Binding Sites, Binding, Competitive, Computer Simulation, Humans, Ligands, Models, Molecular, Predictive Value of Tests, Protein Binding, Sex Hormone-Binding Globulin, Steroids, Reproducibility of Results, Linear Models, Databases as Topic, Quantitative Structure-Activity Relationship
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