Molecular Determinants of Ligand Selectivity for the Human Multidrug and Toxin Extruder Proteins MATE1 and MATE2-K

Abstract

The present study compared the selectivity of two homologous transport proteins, multidrug and toxin extruders 1 and 2-K (MATE1 and MATE2-K), and developed three-dimensional pharmacophores for inhibitory ligand interaction with human MATE1 (hMATE1). The human orthologs of MATE1 and MATE2-K were stably expressed in Chinese hamster ovary cells, and transport function was determined by measuring uptake of the prototypic organic cation (OC) substrate 1-methyl-4-phenylpyridinium (MPP). Both MATEs had similar apparent affinities for MPP, with K tapp values of 4.4 and 3.7 M for MATE1 and MATE2-K, respectively. Selectivity was assessed for both transporters from IC 50 values for 59 structurally diverse compounds. Whereas the two transporters discriminated markedly between a few of the test compounds, the IC 50 values for MATE1 and MATE2-K were within a factor of 3 for most of them. For hMATE1 there was little or no correlation between IC 50 values and the individual molecular descriptors LogP, total polar surface area, or pK a . The IC 50 values were used to generate a common-features pharmacophore, quantitative pharmacophores for hMATE1, and a Bayesian model suggesting molecular features favoring and not favoring the interaction of ligands with hMATE1. The models identified hydrophobic regions, hydrogen bond donor and hydrogen bond acceptor sites, and an ionizable (cationic) feature as key determinants for ligand binding to MATE1. In summary, using a combined in vitro and computational approach, MATE1 and MATE2-K were found to have markedly overlapping selectivities for a broad range of cationic compounds, including representatives from seven novel drug classes of Food and Drug Administration-approved drugs.

Making Transporter Models for Drug–Drug Interaction Prediction Mobile

“…We have described the use of seven data sets representing six transporters of interest (MATE1, MATE2K, OCT2, OCTN2, ASBT, and NTCP) due to their potential involvement in drug-drug interactions (Diao et al, 2009Zheng et al, 2009;Kido et al, 2011;Astorga et al, 2012;Ekins et al, 2012b;Greupink et al, 2012;Dong et al, 2013Dong et al, , 2014Sedykh et al, 2013;Wittwer et al, 2013;Xu et al, 2013). We focused on Bayesian models with ECFP6 and FCFP6 descriptors because these were previously used to develop models that were successfully validated using prospective testing.…”

“…We previously published several transporter models and described Bayesian models generated using Discovery Studio (Biovia, San Diego, CA) for MATE1, MATE2K, OCTN2, ASBT, and NTCP (Diao et al, 2009Zheng et al, 2010;Astorga et al, 2012;Dong et al, 2013Dong et al, , 2014. We have now analyzed several larger published data sets from other groups for MATE1 (Wittwer et al, 2013) and OCT2 (Kido et al, 2011), which we have also used to generate Bayesian models with Discovery Studio to compare the different fingerprints.…”

“…Receiver operator curve (ROC) values were produced, in which a value of 1 is ideal and a value greater than 0.7 is considered good. Cutoffs for actives and inactives were as previously described (Diao et al, 2009Zheng et al, 2010;Kido et al, 2011;Astorga et al, 2012;Dong et al, 2013Dong et al, , 2014Wittwer et al, 2013). Open transporter models were developed using open-source software (Clark et al, 2014 and loaded into the MMDS app (http://molmatinf.com/) (Fig.…”

“…We are increasingly seeing medium-or high-throughput screens used to develop ligand-based models for individual transporters (Diao et al, 2009Zheng et al, 2009;Kido et al, 2011;Astorga et al, 2012;Ekins et al, 2012b;Greupink et al, 2012;Dong et al, 2013Dong et al, , 2014Sedykh et al, 2013;Wittwer et al, 2013;Xu et al, 2013). One of the significant limitations of this is that the models developed are rarely accessible outside of the research group developing them, likely because of the commercial software required.…”

“…The models with the larger training sets, such as for MATE1 and OCT2, had very comparable ROC values across models. The combination of published NTCP data sets (Dong et al, 2013(Dong et al, , 2014 for the first time here consistently produced less desirable models.We previously suggested that hMATE1 may have a complex binding surface, rather than a single binding site (Astorga et al, 2012). Because data sets have been developed for different substrate probes, there is now the potential to build substrate-specific models for a single transporter.…”