2004
DOI: 10.1124/mol.104.002857
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Proteochemometric Mapping of the Interaction of Organic Compounds with Melanocortin Receptor Subtypes

Abstract: Proteochemometrics was applied in the analysis of the binding of organic compounds to wild-type and chimeric melanocortin receptors. Thirteen chimeric melanocortin receptors were designed based on statistical molecular design; each chimera contained parts from three of the MC 1,3-5 receptors. The binding affinities of 18 compounds were determined for these chimeric melanocortin receptors and the four wild-type melanocortin receptors. The data for 14 of these compounds were correlated to the physicochemical and… Show more

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Cited by 39 publications
(48 citation statements)
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“…In reality, protein-ligand interactions are governed by complex processes that depend on the complementarity of the properties of the interacting entities. In PCM, this is accounted for by protein-inhibitor cross-terms [31,36], which in the simplest case is obtained by multiplication of mean centered descriptors of proteins and inhibitors. Therefore, we obtained 11 × 32 = 352, 5 × 32 = 160, 30 × 32 = 960, 11 × 28 = 308, 5 × 28 = 140, 30 × 28 = 840 cross-terms for P0-GD, P1-GD, P2-GD, P0-DLI, P1-DLI, and P2-DLI respectively.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…In reality, protein-ligand interactions are governed by complex processes that depend on the complementarity of the properties of the interacting entities. In PCM, this is accounted for by protein-inhibitor cross-terms [31,36], which in the simplest case is obtained by multiplication of mean centered descriptors of proteins and inhibitors. Therefore, we obtained 11 × 32 = 352, 5 × 32 = 160, 30 × 32 = 960, 11 × 28 = 308, 5 × 28 = 140, 30 × 28 = 840 cross-terms for P0-GD, P1-GD, P2-GD, P0-DLI, P1-DLI, and P2-DLI respectively.…”
Section: Methodsmentioning
confidence: 99%
“…In this area Maris Lapinsh et.al studied melanocortin chimeric receptors using partial least-squares projections (PLS) to deduce PCM models [31,32]; Hanna Geppert et.al derived PCM models of eleven proteases from four different protease families by support vector machine [33]; Ilona Mandrika and Maris Lapinsh et.al applied PLS to model interactions of HIV mutants [30,34] and antibodies [35]. Contrary to traditional QSAR, PCM is based on the similarity of a group of ligands together with that of a group of targets [36].…”
Section: Introductionmentioning
confidence: 99%
“…According to the analysis of single indicator importance, c6A , ATS1v , nCIC , MATS3e and nCrs in the SVR3 model and BELv2 in the SVR4 model appeared to be the most significant descriptors of ARC-111 analogues. ATS1v [10], nCIC [11], MATS3e [1216], nCrs [1723] and BELv2 [10,2426] have been previously reported in different literature models, respectively. To our knowledge, c6A has never been reported as a critical descriptor, so it is unclear what new information is added as an important descriptor.…”
Section: Resultsmentioning
confidence: 99%
“…A recent study has revealed that combinations of descriptors from different aspect may help increase the performance of proteochemometric modeling . PCM has been previously shown to be successful in many protein–ligand interactions studies including melanocortin receptors, G‐protein coupled receptors,, multiple mutated variants of HIV‐1 protease andreverse transcriptase, cytochrome P450,, protein kinases, dengue virus NS3 proteases, histone deacetylases, penicillin‐binding proteins, and carbonic anhydrases ,…”
Section: Introductionmentioning
confidence: 99%