Characterization of differences in substrate specificity among CYP1A1, CYP1A2 and CYP1B1: an integrated approach employing molecular docking and molecular dynamics simulations.

Recent trends in new drug discovery of anticancer drugs have made oncologists more aware of the fact that the new drug discovery must target the developing mechanism of tumorigenesis to improve the therapeutic efficacy of antineoplastic drugs. The drugs designed are expected to have high affinity towards the novel targets selectively. Current research highlights overexpression of CYP450s, particularly cytochrome P450 1A1 (CYP1A1), in tumour cells, representing a novel target for anticancer therapy. However, the CYP1 family is identified as posing significant problems in selectivity of anticancer molecules towards CYP1A1. Three members have been identified in the human CYP1 family: CYP1A1, CYP1A2 and CYP1B1. Although sequences of the three isoform have high sequence identity, they have distinct substrate specificities. The understanding of macromolecular features that govern substrate specificity is required to understand the interplay between the protein function and dynamics, design novel antitumour compounds that could be specifically metabolized by only CYP1A1 to mediate their antitumour activity and elucidate the reasons for differences in substrate specificity profile among the three proteins. In the present study, we employed a combination of computational methodologies: molecular docking and molecular dynamics simulations. We utilized eight substrates for elucidating the difference in substrate specificity of the three isoforms. Lastly, we conclude that the substrate specificity of a particular substrate depends upon the type of the active site residues, the dynamic motions in the protein structure upon ligand binding and the physico-chemical characteristics of a particular ligand. Copyright © 2016 John Wiley & Sons, Ltd.

Comparative proteomics among cytochrome p450 family 1 for differential substrate specificity.

Apart from playing key roles in drug metabolism and adverse drug-drug interactions, CYPs are potential drug targets to treat a variety of diseases. The intervention of over expression of P450 1A1 (CYP1A1) in tumor cells is identified as a novel strategy for anticancer therapy. We investigated three isoforms of CYP1 family (CYP1A1, CYP1A2, and CYP1B1) for their substrate specificity. The understanding of macromolecular features that govern substrate specificity is required to understand the interplay between the protein function and dynamics. This can help in design of new antitumor molecule specifically metabolized by CYP1A1 to mediate their antitumor activity. In the present study, we carried out the comparative protein structure analysis of the three isoforms. Sequence alignment, root mean square deviation (RMSD) analysis, B-factor analysis was performed to give a better understanding of the macromolecular features involved in substrate specificity and to understand the interplay between protein dynamics and functions which will have important implications on rational design of anticancer drugs. We identified the differences in amino acid residues among the three isoforms of CYP1 family, which may account for differential substrate specificity. Six putative substrate recognition sequences are characterized along with the regions they form in the protein structure. Further the RMSD and B-factor analysis provides the information about the identified residues having the maximum RMSD and B-factor deviations.

Intestinal transport of TRH analogs through PepT1: the role of in silico and in vitro modeling.

The present study involves molecular docking, molecular dynamics (MD) simulation studies, and Caco-2 cell monolayer permeability assay to investigate the effect of structural modifications on PepT1-mediated transport of thyrotropin releasing hormone (TRH) analogs. Molecular docking of four TRH analogs was performed using a homology model of human PepT1 followed by subsequent MD simulation studies. Caco-2 cell monolayer permeability studies of four TRH analogs were performed at apical to basolateral and basolateral to apical directions. Inhibition experiments were carried out using Gly-Sar, a typical PepT1 substrate, to confirm the PepT1-mediated transport mechanism of TRH analogs. Papp of the four analogs follows the order: NP-1894 < NP-2378 < NP-1896 < NP-1895. Higher absorptive transport was observed in the case of TRH analogs, indicating the possibility of a carrier-mediated transport mechanism. Further, the significant inhibition of the uptake of Gly-Sar by TRH analogs confirmed the PepT1-mediated transport mechanism. Glide docking scores of all the four analogues were in good agreement with their transport rates, suggesting the role of substrate binding affinity in the PepT1-mediated transport of TRH analogs. MD simulation studies revealed that the polar interactions with amino acid residues present in the active site are primarily responsible for substrate binding, and a downward trend was observed with the increase in bulkiness at the N-histidyl moiety of TRH analogs.

Predicting drug metabolism by CYP1A1, CYP1A2, and CYP1B1: insights from MetaSite, molecular docking and quantum chemical calculations.

Recently, CYP1 enzymes are documented for selective metabolism of anticancer leads in cancer prevention and/or progression. Elucidation of specificity of substrates/inhibitors of CYP1 isoforms plays a vital role in design of more selective and potent anticancer leads. However, an area of concern is the broad range of substrate specificities and planar nature of substrates with limited dataset which makes it difficult to predict their site of metabolism (SOM) accurately. In the present study, various models for prediction of site of metabolism in case of CYP1A1, CYP1A2, and CYP1B1 substrates were developed using MetaSite, molecular docking, and quantum chemical descriptors. The predictive accuracy of MetaSite, molecular docking, and quantum chemical descriptors in identifying experimental site of metabolism was analyzed at three levels; top rank, top three ranks, and top five ranks. Two quantum chemical descriptors, chemical hardness and local nucleophilicity are proposed for the prediction of CYP-mediated SOM for the first time. The predictive accuracy shown by chemical hardness at top three ranks was 83.3, 85.7, and 84.6 % for CYP1A1, CYP1A2 and CYP1B1, respectively, whereas local nucleophilicity gave poor predictions of 50, 42.8, and 46.2 %, respectively. The predictability of chemical hardness descriptor outperformed at all three levels of ranks for CYP1A1, CYP1A2, and CYP1B1. Hence, we propose chemical hardness as an useful quantum chemical descriptor for prediction of metabolically vulnerable prints in CYP1A1, CYP1A2, and CYP1B1 mediated metabolism and support the optimization efforts in drug discovery and development programs.