Prediction of G-protein-coupled receptor classes in low homology using Chou's pseudo amino acid composition with approximate entropy and hydrophobicity patterns

Q Gu; Y S Ding; T L Zhang

doi:10.2174/092986610791112693

Prediction of G-protein-coupled receptor classes in low homology using Chou's pseudo amino acid composition with approximate entropy and hydrophobicity patterns

Protein Pept Lett. 2010 May;17(5):559-67. doi: 10.2174/092986610791112693.

Authors

Q Gu¹, Y S Ding, T L Zhang

Affiliation

¹ College of Information Sciences and Technology, Donghua University, Shanghai 201620, China.

PMID: 19594431
DOI: 10.2174/092986610791112693

Abstract

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Amino Acid Sequence
Amino Acids / analysis*
Animals
Cattle
Computational Biology / methods
Data Interpretation, Statistical
Databases, Protein
Entropy
Hydrophobic and Hydrophilic Interactions
Molecular Sequence Data
Receptors, G-Protein-Coupled / chemistry*
Receptors, G-Protein-Coupled / classification
Rhodopsin / chemistry

Substances

Amino Acids
Receptors, G-Protein-Coupled
Rhodopsin