Literature DB >> 11342268

Development of proteo-chemometrics: a novel technology for the analysis of drug-receptor interactions.

M Lapinsh1, P Prusis, A Gutcaits, T Lundstedt, J E Wikberg.   

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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Year:  2001        PMID: 11342268     DOI: 10.1016/s0304-4165(00)00187-2

Source DB:  PubMed          Journal:  Biochim Biophys Acta        ISSN: 0006-3002


  30 in total

1.  Classification of G-protein coupled receptors by alignment-independent extraction of principal chemical properties of primary amino acid sequences.

Authors:  Maris Lapinsh; Alexandrs Gutcaits; Peteris Prusis; Claes Post; Torbjörn Lundstedt; Jarl E S Wikberg
Journal:  Protein Sci       Date:  2002-04       Impact factor: 6.725

2.  Structural modeling extends QSAR analysis of antibody-lysozyme interactions to 3D-QSAR.

Authors:  Eva K Freyhult; Karl Andersson; Mats G Gustafsson
Journal:  Biophys J       Date:  2003-04       Impact factor: 4.033

Review 3.  QSAR without borders.

Authors:  Eugene N Muratov; Jürgen Bajorath; Robert P Sheridan; Igor V Tetko; Dmitry Filimonov; Vladimir Poroikov; Tudor I Oprea; Igor I Baskin; Alexandre Varnek; Adrian Roitberg; Olexandr Isayev; Stefano Curtarolo; Denis Fourches; Yoram Cohen; Alan Aspuru-Guzik; David A Winkler; Dimitris Agrafiotis; Artem Cherkasov; Alexander Tropsha
Journal:  Chem Soc Rev       Date:  2020-05-01       Impact factor: 54.564

4.  Proteochemometric model for predicting the inhibition of penicillin-binding proteins.

Authors:  Sunanta Nabu; Chanin Nantasenamat; Wiwat Owasirikul; Ratana Lawung; Chartchalerm Isarankura-Na-Ayudhya; Maris Lapins; Jarl E S Wikberg; Virapong Prachayasittikul
Journal:  J Comput Aided Mol Des       Date:  2014-10-26       Impact factor: 3.686

Review 5.  Molecular Modeling of Drug-Transporter Interactions-An International Transporter Consortium Perspective.

Authors:  Avner Schlessinger; Matthew A Welch; Herman van Vlijmen; Ken Korzekwa; Peter W Swaan; Pär Matsson
Journal:  Clin Pharmacol Ther       Date:  2018-08-30       Impact factor: 6.875

6.  Computational chemogenomics: is it more than inductive transfer?

Authors:  J B Brown; Yasushi Okuno; Gilles Marcou; Alexandre Varnek; Dragos Horvath
Journal:  J Comput Aided Mol Des       Date:  2014-04-27       Impact factor: 3.686

Review 7.  Machine learning for in silico virtual screening and chemical genomics: new strategies.

Authors:  Jean-Philippe Vert; Laurent Jacob
Journal:  Comb Chem High Throughput Screen       Date:  2008-09       Impact factor: 1.339

8.  Binding affinity prediction with property-encoded shape distribution signatures.

Authors:  Sourav Das; Michael P Krein; Curt M Breneman
Journal:  J Chem Inf Model       Date:  2010-02-22       Impact factor: 4.956

9.  A unified proteochemometric model for prediction of inhibition of cytochrome p450 isoforms.

Authors:  Maris Lapins; Apilak Worachartcheewan; Ola Spjuth; Valentin Georgiev; Virapong Prachayasittikul; Chanin Nantasenamat; Jarl E S Wikberg
Journal:  PLoS One       Date:  2013-06-17       Impact factor: 3.240

Review 10.  T-cell epitope vaccine design by immunoinformatics.

Authors:  Atanas Patronov; Irini Doytchinova
Journal:  Open Biol       Date:  2013-01-08       Impact factor: 6.411

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