Literature DB >> 16204343

Improved approach for proteochemometrics modeling: application to organic compound--amine G protein-coupled receptor interactions.

Maris Lapinsh1, Peteris Prusis, Staffan Uhlén, Jarl E S Wikberg.   

Abstract

MOTIVATION: Proteochemometrics is a novel technology for the analysis of interactions of series of proteins with series of ligands. We have here customized it for analysis of large datasets and evaluated it for the modeling of the interaction of psychoactive organic amines with all the five known families of amine G protein-coupled receptors (GPCRs).
RESULTS: The model exploited data for the binding of 22 compounds to 31 amine GPCRs, correlating chemical descriptions and cross-descriptions of compounds and receptors to binding affinity using a novel strategy. A highly valid model (q2 = 0.76) was obtained which was further validated by external predictions using data for 10 other entirely independent compounds, yielding the high q2ext = 0.67. Interpretation of the model reveals molecular interactions that govern psychoactive organic amines overall affinity for amine GPCRs, as well as their selectivity for particular amine GPCRs. The new modeling procedure allows us to obtain fully interpretable proteochemometrics models using essentially unlimited number of ligand and protein descriptors.

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Year:  2005        PMID: 16204343     DOI: 10.1093/bioinformatics/bti703

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  19 in total

1.  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

2.  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

3.  Design of a tripartite network for the prediction of drug targets.

Authors:  Ryo Kunimoto; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2018-01-16       Impact factor: 3.686

4.  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

5.  Kinome-wide interaction modelling using alignment-based and alignment-independent approaches for kinase description and linear and non-linear data analysis techniques.

Authors:  Maris Lapins; Jarl Es Wikberg
Journal:  BMC Bioinformatics       Date:  2010-06-22       Impact factor: 3.169

6.  Virtual screening of GPCRs: an in silico chemogenomics approach.

Authors:  Laurent Jacob; Brice Hoffmann; Véronique Stoven; Jean-Philippe Vert
Journal:  BMC Bioinformatics       Date:  2008-09-06       Impact factor: 3.169

7.  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

8.  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

9.  Insights into an original pocket-ligand pair classification: a promising tool for ligand profile prediction.

Authors:  Stéphanie Pérot; Leslie Regad; Christelle Reynès; Olivier Spérandio; Maria A Miteva; Bruno O Villoutreix; Anne-Claude Camproux
Journal:  PLoS One       Date:  2013-06-20       Impact factor: 3.240

10.  Screening of selective histone deacetylase inhibitors by proteochemometric modeling.

Authors:  Dingfeng Wu; Qi Huang; Yida Zhang; Qingchen Zhang; Qi Liu; Jun Gao; Zhiwei Cao; Ruixin Zhu
Journal:  BMC Bioinformatics       Date:  2012-08-22       Impact factor: 3.169

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