Prediction of G-protein-coupled receptor classes in low homology using Chou's pseudo amino acid composition with approximate entropy and hydrophobicity patterns.
We use approximate entropy and hydrophobicity patterns to predict G-protein-coupled receptors. Adaboost classifier is adopted as the prediction engine. A low homology dataset is used to validate the proposed method. Compared with the results reported, the successful rate is encouraging. The source code is written by Matlab.
Gu
Q
Q
College of Information Sciences and Technology, Donghua University, Shanghai 201620, China.
Ding
Y S
YS
Zhang
T L
TL
D000465:Algorithms; D000595:Amino Acid Sequence; D000596:Amino Acids / Q000032:analysis*; D000818:Animals; D002417:Cattle; D019295:Computational Biology / Q000379:methods; D003627:Data Interpretation, Statistical; D030562:Databases, Protein; D019277:Entropy; D057927:Hydrophobic and Hydrophilic Interactions; D008969:Molecular Sequence Data; D043562:Receptors, G-Protein-Coupled / Q000737:chemistry* / Q000145:classification; D012243:Rhodopsin / Q000737:chemistry
D000596:Amino Acids; D043562:Receptors, G-Protein-Coupled; D012243:Rhodopsin