by Julio Caballero, Leyden Fernández, Miguel Garriga, José Abreu, Simona Collina, Michael Fernández
Abstract:
Functional variations on the human ghrelin receptor upon mutations have been associated with a syndrome of short stature and obesity, of which the obesity appears to develop around puberty. In this work, we reported a proteometrics analysis of the constitutive and ghrelin-induced activities of wild-type and mutant ghrelin receptors using amino acid sequence autocorrelation (AASA) approach for protein structural information encoding. AASA vectors were calculated by measuring the autocorrelations at sequence lags ranging from 1 to 15 on the protein primary structure of 48 amino acid/residue properties selected from the AAindex database. Genetic algorithm-based multilinear regression analysis (GA-MRA) and genetic algorithm-based least square support vector machines (GA-LSSVM) were used for building linear and non-linear models of the receptor activity. A genetic optimized radial basis function (RBF) kernel yielded the optimum GA-LSSVM models describing 88% and 95% of the cross-validation variance for the constitutive and ghrelin-induced activities, respectively. AASA vectors in the optimum models mainly appeared weighted by hydrophobicity-related properties. However, differently to the constitutive activity, the ghrelin-induced activity was also highly dependent of the steric features of the receptor. © 2006 Elsevier Inc. All rights reserved.
Reference:
Proteometric study of ghrelin receptor function variations upon mutations using amino acid sequence autocorrelation vectors and genetic algorithm-based least square support vector machines (Julio Caballero, Leyden Fernández, Miguel Garriga, José Abreu, Simona Collina, Michael Fernández), In Journal of Molecular Graphics and Modelling, volume 26, 2007. (http://www.scopus.com/inward/record.url?eid=2-s2.0-34250845013&partnerID=40&md5=da25f40d156ee3b88f6dc7d6629803fd http://www.ncbi.nlm.nih.gov/pubmed/17229584) (cited By (since 1996) 39)
Bibtex Entry:
Title = {Proteometric study of ghrelin receptor function variations upon mutations using amino acid sequence autocorrelation vectors and genetic algorithm-based least square support vector machines},
Author = {Julio Caballero and Leyden Fernández and Miguel Garriga and José Abreu and Simona Collina and Michael Fernández},
Journal = {Journal of Molecular Graphics and Modelling},
Year = {2007},
Note = {cited By (since 1996) 39},
Number = {1},
Pages = {166-178},
Volume = {26},
Abstract = {Functional variations on the human ghrelin receptor upon mutations have been associated with a syndrome of short stature and obesity, of which the obesity appears to develop around puberty. In this work, we reported a proteometrics analysis of the constitutive and ghrelin-induced activities of wild-type and mutant ghrelin receptors using amino acid sequence autocorrelation (AASA) approach for protein structural information encoding. AASA vectors were calculated by measuring the autocorrelations at sequence lags ranging from 1 to 15 on the protein primary structure of 48 amino acid/residue properties selected from the AAindex database. Genetic algorithm-based multilinear regression analysis (GA-MRA) and genetic algorithm-based least square support vector machines (GA-LSSVM) were used for building linear and non-linear models of the receptor activity. A genetic optimized radial basis function (RBF) kernel yielded the optimum GA-LSSVM models describing 88% and 95% of the cross-validation variance for the constitutive and ghrelin-induced activities, respectively. AASA vectors in the optimum models mainly appeared weighted by hydrophobicity-related properties. However, differently to the constitutive activity, the ghrelin-induced activity was also highly dependent of the steric features of the receptor. © 2006 Elsevier Inc. All rights reserved.},
Affiliation = {Molecular Modeling Group, Center for Biotechnological Studies, Faculty of Agronomy, 44740 Matanzas, Cuba; Plant Biotechnology Group, Center for Biotechnological Studies, Faculty of Agronomy, C.P. 44740, Matanzas, Cuba; Artificial Intelligence Lab, Faculty of Informatics, University of Matanzas, 44740 Matanzas, Cuba; Department of Pharmaceutical Chemistry, University of Pavia, via Taramelli, 12, 27100 Pavia, Italy},
Author_keywords = {7TM protein; Autocorrelation vectors; Constitutive activity; Ghrelin; Kernel-based methods; Mutational studies; QSAR},
Comment = {http://www.scopus.com/inward/record.url?eid=2-s2.0-34250845013&partnerID=40&md5=da25f40d156ee3b88f6dc7d6629803fd http://www.ncbi.nlm.nih.gov/pubmed/17229584},
Document_type = {Article},
Doi = {http://dx.doi.org/10.1016/j.jmgm.2006.11.002},
Owner = {2007_J_Mol_Graph_Model_26_166},
Source = {Scopus},
Url = {http://www.sciencedirect.com/science/article/pii/S1093326306001409}
}