Purification and characterization of arginine deiminase from Pseudomonas putida: Structural insights of the differential affinities of l-arginine analogues.

Arginine deiminase (ADI) from Pseudomonas putida was purified using ammonium sulphate precipitation, anion exchange and hydrophobic interaction chromatography. Influence of various chemical compounds (metal ions, reducing agents, sulphydryl agents, and surfactants) on the catalytic activity of ADI was determined was evaluated on the purified ADI. The enzyme displayed high sensitivity towards thiol binding metal ions, chemicals acting on sulfhydryl group, and most of the surfactants. Substrate specificity studies exhibited that among the eight substrate analogues tested, canavanine had the highest affinity for ADI, followed by d-arginine and guanidine. Canavanine decreased the ADI activity up to 50% at its lowest concentration tested (10 mM), while d-arginine decreased the ADI activity up to ∼4% at its highest tested concentration (200 mM). Differential affinities of the structural analogues of arginine towards ADI were further studied by molecular modeling methods, which included homology modeling, molecular docking and molecular dynamic simulations. The molecular docking studies revealed the critical importance of residues Arg 243, Asp 166, Asp 280, Gly 299 and His 278. RMSDs for protein-ligand complexes were within a range of 1-3 Å, suggesting that the complexes were stable throughout the molecular dynamic simulation. The formation of strong hydrogen bonds by residues Asn 160, Asp166, Arg 185, Arg243, Asp280 and Gly 399 in l-arginine were preserved in the case of d-arginine and canavanine and was responsible for higher affinity towards ADI. Calculations of the substrate binding energies revealed that binding energies ΔGbind and ΔGvdw play a critical role for the differential affinities of various substrate analogues towards P. putida ADI.

Identification of leads for antiproliferative activity on MDA-MB-435 human breast cancer cells through pharmacophore and CYP1A1-mediated metabolism.

CYP1A1 is a potential target for anticancer drug development due to its overexpression in certain cancer cells and role in cancer progression. To identify new leads for CYP1A1 mediated anticancer action, we attempted ligand based pharmacophore mapping, virtual screening of databases, molecular docking, MetaSite based filtering, and molecular dynamics simulations. Initial computational and in vitro screening identified 11 compounds from which we identified two lead compounds, ZINC33468944 and ZINC32101539, showed potential antitumor activity on MDA-MB-435 cell lines (GI50 < 0.1 µM) and CYP1A1 inhibition of 0.13 and 0.3 µM, respectively. Furthermore, the lead compounds were evaluated for CYP1A1 mediated metabolism, showing N-hydroxylated metabolites, which have potential of DNA adduct formation and cause cancerous cell death. Analysis of molecular dynamics simulations provided important guidelines for the further modification of the lead compounds. Hence, we claim the lead molecules for further development in anticancer drug discovery.

Classification of Human Pregnane X Receptor (hPXR) Activators and Non-Activators by Machine Learning Techniques: A Multifaceted Approach.

The Human Pregnane X Receptor (hPXR) is a regulator of drug metabolising enzymes (DME) and efflux transporters (ET). The prediction of hPXR activators and non-activators has pharmaceutical importance to predict the multiple drug resistance (MDR) and drug-drug interactions (DDI). In this study, we developed and validated the computational prediction models to classify hPXR activators and non-activators. We employed four machine learning methods support vector machine (SVM), k-nearest neighbour (k-NN), random forest (RF) and naïve bayesian (NB). These methods were used to develop molecular and fingerprint based descriptors for the prediction of hPXR activators and non-activators. Total 529 molecules consitsting of 317 activators and 212 non-activators were used for model development. The overall prediction accuracy of models was 69% to 99% to classify hPXR activators and nonactivators using RDkit descriptors. In case of 5 and 10-fold cross validation the prediction accuracy for training set is 74% to 82% and 79% to 83% for hPXR activators respectively and 50% to 62% and 49% to 65% non-activators, respectively. The external test prediction is between 59% to 73% for hPXR activators and 55% to 68% for hPXR non-activators. In addition, consensus models were developed in which the best model shows overall 75% to 83% accuracy for fingerprint and RDkit descriptors, respectively. The best developed model will be utilized for the prediction of hPXR activators and non-activators.

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.

Pregnane X Receptor and P-glycoprotein: a connexion for Alzheimer's disease management.

The translational failure between preclinical animal models and clinical outcome has alarmed us to search for a new strategy in the treatment of Alzheimer's disease (AD). Interlink between Pregnane X Receptor (PXR) and P-glycoprotein (Pgp) at the blood brain barrier (BBB) has raised hope toward a new disease modifying therapy in AD. Pgp is a major efflux transporter for beta amyloid (Aß) at human BBB. A literature survey reveals diminished expression of Pgp transporter at the BBB in AD patients. Pregnane X Receptor is a major transcriptional regulator of Pgp. Restoration of Pgp at the BBB enhances the elimination of the Aß from brain alongside and inhibits the apical to basolateral movement of Aß from the circulatory blood. This review concentrates on in vitro, in vivo, and in silico advancements on the study of the PXR in context to Pgp and discusses the substrate and inhibitor specificity between PXR and Pgp.

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.

Human pregnane X receptor: a novel target for anticancer drug development.

Multidrug resistance (MDR), a significant barrier to effective pharmacokinetics and pharmacodynamics of anticancer drugs, is mainly due to the induction potential of anticancer drugs for drug metabolizing enzymes (DMEs) and efflux transporters through nuclear receptors. Human Pregnane X Receptor (hPXR) is master transcription factor for cytochrome P450 3A4 (CYP3A4) and multidrug resistance protein 1 (MDR1). The hPXR is capable of being activated by structurally diverse ligands. Several studies, like in silico modeling, in vitro assays, and in vivo experimentation have been conducted to identify the structural features of ligand for activation of hPXR. This review highlights hPXR as an appealing target for both the development of novel anticancer drugs and the improvement in preclinical and clinical evaluation of anticancer drugs.