Title:Recent Progresses in Identifying Nuclear Receptors and Their Families
VOLUME: 13 ISSUE: 10
Author(s):Xuan Xiao, Pu Wang and Kuo-Chen Chou
Affiliation:Computer Department, Jing- De-Zhen Ceramic Institute, China.
Keywords:Pseudo amino acid composition, physical-chemical property matrix, NR-2L, iNR-PhysChem, covariant discriminant,
chou’s invariance theorem, web-server.
Abstract:Nuclear receptors (NRs) are members of a large superfamily of evolutionarily related DNA-binding transcription
factors. They regulate diverse functions, such as homeostasis, reproduction, development and metabolism. As nuclear
receptors bind small molecules that can easily be modified by drug design, and control functions associated with major
diseases (e.g. cancer, osteoporosis and diabetes), they are promising pharmacological targets. According to their different
action mechanisms or functions, NR superfamily has been classified into seven families: NR1 (thyroid hormone like),
NR2 (HNF4-like), NR3 (estrogen like), NR4 (nerve growth factor IB-like), NR5 (fushi tarazu-F1 like), NR6 (germ cell
nuclear factor like), and NR0 (knirps or DAX like). With the avalanche of protein sequences generated in the postgenomic
age, Scientists are facing the following challenging problems. Given an uncharacterized protein sequence, how can we
identify whether it is a nuclear receptor? If it is, what family even subfamily it belongs to? To address these problems,
many cheminformatics tools have been developed for nuclear receptor prediction. The current review is mainly focused
on this field, including the functions, computational methods and limitations of these tools.
