Chemometrics approach for the prediction of structure–activity relationship for membrane transporter bilitranslocase

£ Presented at the 16th International Workshop on Quantitative Structure–Activity Relationships in Environmental and Health Sciences (QSAR2014), 16–20 June 2014, Milan, Italy.

Abstract

Membrane transport proteins are essential for cellular uptake of numerous salts, nutrients and drugs. Bilitranslocase is a transporter, specific for water-soluble organic anions, and is the only known carrier of nucleotides and nucleotide-like compounds. Experimental data of bilitranslocase ligand specificity for 120 compounds were used to construct classification models using counter-propagation artificial neural networks (CP-ANNs) and support vector machines (SVMs). A subset of active compounds with experimentally determined transport rates was used to build predictive QSAR models for estimation of transport rates of unknown compounds. Several modelling methods and techniques were applied, i.e. CP-ANN, genetic algorithm, self-organizing mapping and multiple linear regression method. The best predictions were achieved using CP-ANN coupled with a genetic algorithm, with the external validation parameter Q_V² of 0.96. The applicability domains of the models were defined to determine the chemical space in which reliable predictions can be obtained. The models were applied for the estimation of bilitranslocase transport activity for two sets of pharmaceutically interesting compounds, antioxidants and antiprions. We found that the relative planarity and a high potential for hydrogen bond formation are the common structural features of anticipated substrates of bilitranslocase. These features may serve as guidelines in the design of new pharmaceuticals transported by bilitranslocase.

Keywords:

• QSAR models
• bilitranslocase
• transmembrane transport activity
• antioxidants
• antiprions

Acknowledgements

The study was financially supported through the grants P1-0017 and 34547 from the Agency of Research of the Republic of Slovenia (ARRS) and an EU grant from the Transregional Network for Innovation and Technology Transfer to Improve Health Care (CB 101 – TRANS2CARE). Professor P. Gramatica is greatly acknowledged for providing the QSARINS software.

Notes

£ Presented at the 16th International Workshop on Quantitative Structure–Activity Relationships in Environmental and Health Sciences (QSAR2014), 16–20 June 2014, Milan, Italy.