In silico studies on potential TNKS inhibitors: a combination of pharmacophore and 3D-QSAR modelling, virtual screening, molecular docking and molecular dynamics.

Maladjustment of the Wnt-signalling pathway can lead to a variety of cancers. Axin can be expressed stably through the Tankyrases (TNKS) inhibitor, thus inducing the degradation of ß-catenin, antagonizing the Wnt-signalling pathway and consequently inhibiting tumour growth. Thus, TNKS has become a hot research target for anticancer drugs, and a systematic study of tetrazoloquinoxaline analogues as TNKS inhibitors is of considerable value. In this paper, three-dimensional (3D)-quantitative structure-activity relationship (QSAR), molecular docking and DISCOtech were applied to study a series of tetrazoloquinoxaline and establish a good comparative molecular field analysis (CoMFA) (q2 = 0.701, r2 = 0.968 and rpred2 = 0.754), comparative molecular similarity index analysis (CoMSIA) (q2 = 0.572, r2 = 0.991 and rpred2 = 0.721) and Topomer CoMFA (q2 = 0.692, r2 = 0.979 and rpred2 = 0.532) models, which offer high predictability. The effect of steric hindrance, electrostatic interaction, hydrophobic interaction and hydrogen-bonding acceptor of the molecular group on molecular activity was revealed through contour map. Molecular docking revealed the binding pattern between acceptor and ligand and determined that the effect of hydrogen-bond interaction between the inhibitor and residue GLY1032 was critical to the molecular activity. The pharmacophore features were consistent with the contour map and the molecular-docking result. Through filtering the ZINC database (including 8777 micro-molecule structures), we obtained candidate compounds TS1 and TS2, which exhibit a novel scaffold structure and potential inhibitory activities against TNKS. Molecular docking and molecular dynamics studies of compounds TS1 and TS2 were used to confirm that the binding interaction between ligand and receptor is mainly hydrogen bonding between the compound and residue GLY1032, as well as the hydrophobic interaction with TYR1071. Molecular dynamics studies showed that compounds TS1 and TS2 can stably bind with receptors. These results provide favourable theoretical guidance for the development of novel TNKS inhibitors.

Predicting protein subcellular locations with feature selection and analysis.

In this paper, we propose a strategy to predict the subcellular locations of proteins by combining various feature selection methods. Firstly, proteins are coded by amino-acid composition and physicochemical properties, then these features are arranged by Minimum Redundancy Maximum Relevance method and further filtered by feature selection procedure. Nearest Neighbor Algorithm is used as a prediction model to predict the protein subcellular locations, and gains a correct prediction rate of 70.63%, evaluated by Jackknife cross-validation. Results of feature selection also enable us to identify the most important protein properties. The prediction software is available for public access on the website http://chemdata.shu.edu.cn/sub22/, which may play a important complementary role to a series of web-server predictors summarized recently in a review by Chou and Shen (Chou, K.C., Shen, H.B. Natural Science, 2009, 2, 63-92, http://www.scirp.org/journal/NS/).

The Simcyp population-based ADME simulator.

The Simcyp population-based absorption, distribution, metabolism and excretion simulator is a platform and database for 'bottom-up' mechanistic modelling and simulation of the processes of oral absorption, tissue distribution, metabolism and excretion of drugs and drug candidates in healthy and disease populations. It combines experimental data generated routinely during preclinical drug discovery and development from in vitro enzyme and cellular systems and relevant physicochemical attributes of compound and dosage form with demographic, physiological and genetic information on different patient populations. The mechanistic approach implemented in the Simcyp Simulator allows simulation of complex absorption, distribution, metabolism and excretion outcomes, particularly those involving multiple drug interactions, parent drug and metabolite profiles and time- and dose-dependent phenomena such as auto-induction and auto-inhibition.This review describes the framework and organisation of the simulator and how it combines the different categories of information.