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.
Keywords: Pseudo amino acid composition, physical-chemical property matrix, NR-2L, iNR-PhysChem, covariant discriminant, chou’s invariance theorem, web-server.
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