The DOI matches its Crossref record; 14 of the 15 references it cites were found in a registry or an index.
Development of proteo-chemometrics: a novel technology for the analysis of drug-receptor interactions
Abstract
A novel method for the analysis of drug receptor interactions has been developed and used to explore mechanisms involved in the binding of 4-piperidyl oxazole antagonists to alpha1a-, alpha1b- and alpha1d-adrenoceptors. The method exploits affinity data for a series of organic chemical compounds binding to wild-type and artificially mutated receptors. The receptor sequences and compounds are assigned predictor variables that are correlated to the measured pharmacological activities using partial least-squares projections to latent structures. The predictor variables consist of one descriptor block derived from the chemical properties of the receptors' primary amino acid sequences and another descriptor block derived from the chemical properties of the organic compounds. The cross-terms generated from the two descriptor blocks are also derived. Using this approach, very sturdy models were generated describing the interactions of the chemical compounds with the receptors. Models are useful to predict binding affinity and receptor subtype selectivity of compounds prior to their synthesis, and may find use in rational drug design. Moreover, models also give quantitative information about the interactions of the amino acids of the receptors with the ligands, thereby giving an insight into the molecular mechanisms involved in ligand binding.
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“For instance, this approach was used to predict ligand interactions with wild-type and mutated α-adrenoceptors where it has demonstrated superior predictivity in comparison with conventional QSAR methods.118”
From QSAR without borders (2020) · cites this paper as [118]
Citations
Cites
Experimental design and optimization
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Prediction of Drug Binding Affinities by Comparative Binding Energy Analysis
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Computational methods for the structural alignment of molecules
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Modelling G-protein-coupled receptors for drug design
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VALIDATE: A New Method for the Receptor-Based Prediction of Binding Affinities of Novel Ligands
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Progress in protein structure prediction: Assessment of CASP3
Sternberg, M. J. E., Bates, P. A., Kelley, L. A., & MacCallum, R. M. (1999) · Current Opinion in Structural Biology · doi:10.1016/s0959-440x(99)80050-5 · Cited by 117
Computational approaches to structure-based ligand design
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α1-Adrenergic Receptor Subtype Determinants for 4-Piperidyl Oxazole Antagonists
Hamaguchi, N., True, T. A., Goetz, A. S., Stouffer, M. J., Lybrand, T. P., & Jeffs, P. W. (1998) · Biochemistry · doi:10.1021/bi972733a · Cited by 20
Research works printed but not linked: 1
- L. Eriksson, E. Johansson, S. Wold, Quantitative structure-activity relationship model validation, in: F. Chen, G. Schuurmann (Eds.), Quantitative Structure-Activity Relationships in Environmental Sciences – VII, SETAC Press, Pensacola, FL, 1997, p. 381–397.
Cited by
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Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913
T-cell epitope vaccine design by immunoinformatics
Patronov, A., & Doytchinova, I. A. (2013) · Open Biology · doi:10.1098/rsob.120139 · Cited by 426
Computational approaches in target identification and drug discovery
Κάτσιλα, Θ., Spyroulias, G. A., Patrinos, G. P., & Matsoukas, M. (2016) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2016.04.004 · Cited by 408
Koutsoukas, A., Simms, B., Kirchmair, J., Bond, P. J., Whitmore, A. V., Zimmer, S., Young, M. P., Jenkins, J. L., Glick, M., Glen, R. C., et al. (2011) · Journal of Proteomics · doi:10.1016/j.jprot.2011.05.011 · Cited by 291
Deep Learning in Virtual Screening: Recent Applications and Developments
Kimber, T. B., Chen, Y., & Volkamer, A. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22094435 · Cited by 215
Metadata
- Authors
- Lapinsh, M., Prusis, P., Gutcaits, A., Lundstedt, T., & Wikberg, J. E. S.
- Affiliations
- Uppsala University (SE)
- Corresponding author
- Wikberg, Jarl E.S.
- ORCID
- Wikberg, Jarl E.S.
- Venue
- Biochimica et Biophysica Acta (BBA) - General Subjects
- Publisher
- Elsevier BV
- Published
- 2001-02
- Volume
- 1525
- Issue
- 1-2
- Pages
- 180-190
- ISSN
- 0304-4165 (ISSN-L)
- OpenAlex
- W2106440817
- Cited by
- 134
- References
- 15
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 4 · 2023: 5 · 2022: 3 · 2021: 6 · 2020: 13 · 2019: 5 · 2018: 9 · 2017: 4 · 2016: 11 · 2015: 4 · 2014: 6 · 2013: 7 · 2012: 3
- FWCI
- 4.33
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Biochimica et Biophysica Acta (BBA) - General Subjects (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Interaction Studies and Fluorescence Analysis
- Keywords
- drug-receptor interactions, alpha1-adrenoceptors, receptor subtype selectivity, binding affinity prediction, ligand binding mechanism, rational drug design, receptor mutations
- MeSH terms
- Adrenergic alpha-Antagonists, Pharmaceutical Preparations, Humans, Ligands, Models, Biological, Oxazoles, Pharmacology, Receptors, Drug, Recombinant Fusion Proteins, Drug Design, Point Mutation, Receptors, Adrenergic, alpha-1, Quantitative Structure-Activity Relationship, In Vitro Techniques
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