caa a22 4500 463185862 CHVBK 20180406164852.0 cr unu---uuuuu 170326e20070201xx s 000 0 eng 10.1007/s00726-006-0341-y doi (NATIONALLICENCE)springer-10.1007/s00726-006-0341-y Delaunay triangulation with partial least squares projection to latent structures: a model for G-protein coupled receptors classification and fast structure recognition [Elektronische Daten] [Z. Wen, M. Li, Y. Li, Y. Guo, K. Wang] Summary.: As an important transmembrane protein family in eukaryon, G-protein coupled receptors (GPCRs) play a significant role in cellular signal transduction and are important targets for drug design. However, it is very difficult to resolve their tertiary structure by X-ray crystallography. In this study, we have developed a Delaunay model, which constructs a series of simplexes with latent variables to classify the families of GPCRs and projects unknown sequences to principle component space (PC-space) to predict their topology. Computational results show that, for the classification of GPCRs, the method achieves the accuracy of 91.0 and 87.6% for Class A, more than 80% for the other three classes in differentiating GPCRs from non-GPCRs and 70% for discriminating between four major classes of GPCR, respectively. When recognizing the structure of GPCRs, all the N-terminals of sequences can be determined correctly. The maximum accuracy of predicting transmembrane segments is achieved in the 7th transmembrane segment of Rhodopsin, which is 99.4%, and the average error is 2.1 amino acids, which is the lowest in all of the segments prediction. This method could provide structural information of a novel GPCR as a tool for experiments and other algorithms of structure prediction of GPCRs. Academic users should send their request for the MATLAB program for classifying GPCRs and predicting the topology of them at liml@scu.edu.cn . Springer-Verlag, 2006 Keywords: Delaunay triangulation model - Partial least squares - G-protein coupled receptor - Classification - Structure recognition nationallicence Wen Z. College of Chemistry, Sichuan University, Chengdu, Sichuan, China aut Li M. College of Chemistry, Sichuan University, Chengdu, Sichuan, China aut Li Y. College of Chemistry, Sichuan University, Chengdu, Sichuan, China aut Guo Y. College of Chemistry, Sichuan University, Chengdu, Sichuan, China aut Wang K. College of Chemistry, Sichuan University, Chengdu, Sichuan, China aut Amino Acids Springer-Verlag 32/2(2007-02-01), 277-283 0939-4451 32:2<277 2007 32 726 https://doi.org/10.1007/s00726-006-0341-y text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s00726-006-0341-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Wen Z. College of Chemistry, Sichuan University, Chengdu, Sichuan, China aut NATIONALLICENCE 700 1- Li M. College of Chemistry, Sichuan University, Chengdu, Sichuan, China aut NATIONALLICENCE 700 1- Li Y. College of Chemistry, Sichuan University, Chengdu, Sichuan, China aut NATIONALLICENCE 700 1- Guo Y. College of Chemistry, Sichuan University, Chengdu, Sichuan, China aut NATIONALLICENCE 700 1- Wang K. College of Chemistry, Sichuan University, Chengdu, Sichuan, China aut NATIONALLICENCE 773 0- Amino Acids Springer-Verlag 32/2(2007-02-01), 277-283 0939-4451 32:2<277 2007 32 726 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer