Literature DB >> 12801221

QSAR and proteo-chemometric analysis of the interaction of a series of organic compounds with melanocortin receptor subtypes.

Maris Lapinsh1, Peteris Prusis, Ilze Mutule, Felikss Mutulis, Jarl E S Wikberg.   

Abstract

We have created quantitative structure-activity relationship (QSAR) models describing the interaction of a series of 54 organic compounds with four melanocortin (MC) receptor subtypes, MC(1), MC(3), MC(4), and MC(5). In addition to traditional QSAR analysis, we applied our recently developed proteo-chemometrics approach. Proteo-chemometrics is based on the combined analysis of series of receptors and ligands, wherein descriptions of ligands, proteins, and so-called ligand-protein cross-terms are correlated with interaction activities. The compounds were characterized by structural descriptors, including three-dimensional grid-independent descriptors (GRINDs), topological descriptors, and geometrical descriptors. Description of receptors was obtained by computing the receptors' amino acid sequence identities. Both the QSAR and proteo-chemometrics approaches resulted in models with essentially the same statistical significance: the cross-validated correlation coefficient q(2) for the proteo-chemometric model being 0.71, while for the QSAR models the q(2)s were 0.75, 0.68, 0.63, and 0.71 for the MC(1), MC(3)(-)(5) receptor, respectively. However, the proteo-chemometrics modeling provided more detailed information about receptor-ligand interactions and determinants for receptor subtype selectivity than did QSAR.

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Year:  2003        PMID: 12801221     DOI: 10.1021/jm020945m

Source DB:  PubMed          Journal:  J Med Chem        ISSN: 0022-2623            Impact factor:   7.446


  9 in total

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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

Review 2.  Drug Metabolism in Preclinical Drug Development: A Survey of the Discovery Process, Toxicology, and Computational Tools.

Authors:  Naiem T Issa; Henri Wathieu; Abiola Ojo; Stephen W Byers; Sivanesan Dakshanamurthy
Journal:  Curr Drug Metab       Date:  2017       Impact factor: 3.731

3.  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

4.  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

Review 5.  Current computational methods for predicting protein interactions of natural products.

Authors:  Aurélien F A Moumbock; Jianyu Li; Pankaj Mishra; Mingjie Gao; Stefan Günther
Journal:  Comput Struct Biotechnol J       Date:  2019-10-28       Impact factor: 7.271

6.  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

7.  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

8.  Proteochemometric modeling of HIV protease susceptibility.

Authors:  Maris Lapins; Martin Eklund; Ola Spjuth; Peteris Prusis; Jarl E S Wikberg
Journal:  BMC Bioinformatics       Date:  2008-04-10       Impact factor: 3.169

9.  BioTriangle: a web-accessible platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions.

Authors:  Jie Dong; Zhi-Jiang Yao; Ming Wen; Min-Feng Zhu; Ning-Ning Wang; Hong-Yu Miao; Ai-Ping Lu; Wen-Bin Zeng; Dong-Sheng Cao
Journal:  J Cheminform       Date:  2016-06-21       Impact factor: 5.514

  9 in total

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