Xeno-oestrogens and phyto-oestrogens are alternative ligands for the androgen receptor

The androgen receptor （AR） plays a critical role in prostate cancer development and progression. This study aimed to use a computerized docking approach to examine the interactions between the human AR and phytooestrogens （genistein, daidzein, and flavone） and xeno-oestrogens （bisphenol A, 4-nonylphenol, dichlorodiphenyl trichloroethane [DDT], diethylstilbestrol [DES]）. The predicted three-dimensional structure of AR and androgens was established using X-ray diffraction. The binding of four xeno-oestrogens and three phyto-oestrogens to AR was analysed. The steroids estradiol and dihydrotestosterone （DHT） were used as positive controls and thyroxine as negative control. All the ligands shared the same binding site except for thyroxine. The endogenous hormones DHT and 17β-oestradiol showed the strongest binding with the lowest affinity energy （〈 -10 kcal mol-1）. All three phyto- oestrogens and two xeno-oestrogens （bisphenol A and DES） showed strong binding to AR. The affinities offlavone, genistein, and daidzein were between -8.8 and -8.5 kcal mol 1, while that of bisphenol A was -8.1 kcal mol-l and DES -8.3 kcal mol-1. Another two xeno-oestrogens, 4-nonylphenol and DDT, although they fit within the binding domain of AR, showed weak affinity （-6.4 and -6.7 kcal mol 1, respectively）. The phyto-oestrogens genistein, daidzein and flavone, and the xeno-oestrogens bisphenol A and DES can be regarded as androgenic effectors. The xenooestrogens DDT and 4-nonylphenol bind only weakly to AR.

Towards a better prediction of subcellular location of long non-coding RNA

The spatial distribution pattern of long non-coding RNA(lncRNA)in cell is tightly related to their function.With the increment of publicly available subcellular location data,a number of computational methods have been developed for the recognition of the subcellular localization of lncRNA.Unfortunately,these computational methods suffer from the low discriminative power of redundant features or overfitting of oversampling.To address those issues and enhance the prediction performance,we present a support vector machine-based approach by incorporating mutual information algorithm and incremental feature selection strategy.As a result,the new predictor could achieve the overall accuracy of 91.60%.The highly automated web-tool is available at lin-group.cn/server/iLoc-LncRNA(2.0)/website.It will help to get the knowledge of lncRNA subcellular localization.