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Published on: November 15, 2013
NR-2L: a two-level predictor for identifying nuclear receptor subfamilies based on sequence-derived features
Pu Wang1, Xuan Xiao, Kuo-Chen Chou
1Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China.
Plos One
|August 23, 2011
Summary
A new tool, NR-2L, accurately identifies nuclear receptors (NRs) and their subfamilies using protein sequence data. This predictor offers high success rates for NR classification, aiding drug development research.
Area of Science:
- Biochemistry and Molecular Biology
- Bioinformatics and Computational Biology
- Genomics and Proteomics
Background:
- Nuclear receptors (NRs) are crucial transcriptional regulators involved in homeostasis, reproduction, development, and metabolism.
- Their importance in diverse biological functions makes them significant targets for drug development.
- NRs form a superfamily of related proteins, classified into distinct subfamilies based on domain diversity.
Purpose of the Study:
- To develop a computational tool, NR-2L, for identifying nuclear receptors and their specific subfamilies solely from protein sequence information.
- To provide a reliable and efficient method for NR classification to support biological research and drug discovery efforts.
Main Methods:
- A two-level prediction strategy was employed using the Fuzzy K-nearest neighbor (FK-NN) classifier.
- The predictor utilizes pseudo amino acid composition, incorporating features like amino acid composition, dipeptide composition, complexity factor, and Fourier spectrum components.
- Sequence data from benchmark datasets (NucleaRDB, UniProt) with low redundancy were used for training and validation.
Main Results:
- The NR-2L predictor achieved high overall success rates: approximately 93% for the first level (NR identification) and 89% for the second level (subfamily classification) via jackknife testing.
- These high accuracy rates demonstrate the efficacy of the sequence-based prediction method.
- The NR-2L tool is available as a user-friendly web server, processing up to 500 sequences in under 2 minutes.
Conclusions:
- The developed NR-2L predictor is a valuable tool for accurately identifying nuclear receptors and their subfamilies based on protein sequence data.
- Its high performance and accessibility via a web server facilitate research in NR biology and drug development.
- The program codes are available for non-commercial use, promoting further research and application.
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About 48 different soluble family members of nuclear receptors are identified that can be divided into two main classes:
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