Literature DB >> 12021408

Proteochemometrics modeling of the interaction of amine G-protein coupled receptors with a diverse set of ligands.

Maris Lapinsh1, Peteris Prusis, Torbjörn Lundstedt, Jarl E S Wikberg.   

Abstract

We have evaluated the proteochemometrics approach in the analysis of the interactions of a diverse set or organic ligands with subtypes of serotonin, dopamine, histamine, and adrenergic receptors. As used herein, proteochemometrics exploits affinity data for series of organic amines binding to wild-type amine G protein-coupled receptors, correlating it to descriptions and cross-description derived from the primary amino acid sequences of the receptors and the computed structures of the organic compounds. We show that after appropriate data preprocessing, statistically valid models that have good external predictive ability can be created. Evaluation of the models gave important quantitative insight into the mode of interactions of the amine G protein-coupled receptors with their ligands.

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Year:  2002        PMID: 12021408     DOI: 10.1124/mol.61.6.1465

Source DB:  PubMed          Journal:  Mol Pharmacol        ISSN: 0026-895X            Impact factor:   4.436


  22 in total

1.  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

2.  Proteochemometric modeling of the antigen-antibody interaction: new fingerprints for antigen, antibody and epitope-paratope interaction.

Authors:  Tianyi Qiu; Han Xiao; Qingchen Zhang; Jingxuan Qiu; Yiyan Yang; Dingfeng Wu; Zhiwei Cao; Ruixin Zhu
Journal:  PLoS One       Date:  2015-04-22       Impact factor: 3.240

3.  Extensions to amino acid description.

Authors:  Johan Gottfries; Lennart Eriksson
Journal:  Mol Divers       Date:  2009-11-25       Impact factor: 2.943

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

5.  Computer aided selection of candidate vaccine antigens.

Authors:  Darren R Flower; Isabel K Macdonald; Kamna Ramakrishnan; Matthew N Davies; Irini A Doytchinova
Journal:  Immunome Res       Date:  2010-11-03

6.  Proteochemometric modeling of the susceptibility of mutated variants of the HIV-1 virus to reverse transcriptase inhibitors.

Authors:  Muhammad Junaid; Maris Lapins; Martin Eklund; Ola Spjuth; Jarl E S Wikberg
Journal:  PLoS One       Date:  2010-12-15       Impact factor: 3.240

7.  Proteochemometric modeling of the bioactivity spectra of HIV-1 protease inhibitors by introducing protein-ligand interaction fingerprint.

Authors:  Qi Huang; Haixiao Jin; Qi Liu; Qiong Wu; Hong Kang; Zhiwei Cao; Ruixin Zhu
Journal:  PLoS One       Date:  2012-07-27       Impact factor: 3.240

8.  Integrated QSAR study for inhibitors of Hedgehog Signal Pathway against multiple cell lines:a collaborative filtering method.

Authors:  Jun Gao; Dongsheng Che; Vincent W Zheng; Ruixin Zhu; Qi Liu
Journal:  BMC Bioinformatics       Date:  2012-07-31       Impact factor: 3.169

9.  GPCRTree: online hierarchical classification of GPCR function.

Authors:  Matthew N Davies; Andrew Secker; Mark Halling-Brown; David S Moss; Alex A Freitas; Jon Timmis; Edward Clark; Darren R Flower
Journal:  BMC Res Notes       Date:  2008-08-21

10.  Prediction of indirect interactions in proteins.

Authors:  Peteris Prusis; Staffan Uhlén; Ramona Petrovska; Maris Lapinsh; Jarl E S Wikberg
Journal:  BMC Bioinformatics       Date:  2006-03-22       Impact factor: 3.169

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