Ligand binding site superposition and comparison based on Atomic Property Fields: identification of distant homologues, convergent evolution and PDB-wide clustering of binding sites

A new binding site comparison algorithm using optimal superposition of the continuous pharmacophoric property distributions is reported. The method demonstrates high sensitivity in discovering both, distantly homologous and convergent binding sites. Good quality of superposition is also observed on multiple examples. Using the new approach, a measure of site similarity is derived and applied to clustering of ligand binding pockets in PDB

Structural Biology of Human H3K9 Methyltransferases

SET domain methyltransferases deposit methyl marks on specific histone tail lysine residues and play a major role in epigenetic regulation of gene transcription. We solved the structures of the catalytic domains of GLP, G9a, Suv39H2 and PRDM2, four of the eight known human H3K9 methyltransferases in their apo conformation or in complex with the methyl donating cofactor, and peptide substrates. We analyzed the structural determinants for methylation state specificity, and designed a G9a mutant able to tri-methylate H3K9. We show that the I-SET domain acts as a rigid docking platform, while induced-fit of the Post-SET domain is necessary to achieve a catalytically competent conformation. We also propose a model where long-range electrostatics bring enzyme and histone substrate together, while the presence of an arginine upstream of the target lysine is critical for binding and specificity. Enhanced version: This article can also be viewed as an enhanced version in which the text of the article is integrated with interactive 3D representations and animated transitions. Please note that a web plugin is required to access this enhanced functionality. Instructions for the installation and use of the web plugin are available i

Population based models of cortical drug response: insights from anaesthesia

A great explanatory gap lies between the molecular pharmacology of psychoactive agents and the neurophysiological changes they induce, as recorded by neuroimaging modalities. Causally relating the cellular actions of psychoactive compounds to their influence on population activity is experimentally challenging. Recent developments in the dynamical modelling of neural tissue have attempted to span this explanatory gap between microscopic targets and their macroscopic neurophysiological effects via a range of biologically plausible dynamical models of cortical tissue. Such theoretical models allow exploration of neural dynamics, in particular their modification by drug action. The ability to theoretically bridge scales is due to a biologically plausible averaging of cortical tissue properties. In the resulting macroscopic neural field, individual neurons need not be explicitly represented (as in neural networks). The following paper aims to provide a non-technical introduction to the mean field population modelling of drug action and its recent successes in modelling anaesthesia

Bioinformatics in translational drug discovery

Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. Here, we highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse ‘big data’ that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications

Dissociation reactions of protonated anthracycline antibiotics following electrospray ionization-tandem mass spectrometry

Fragmentation pathways of doxorubicin, a common cancer therapy agent, and three closely related analogs (epirubicin, daunorubicin, idarubicin) were compared using electrospray ionization with tandem mass spectrometry. This class of antibiotics with anti-tumour activity has important structural features, with a tetracyclic aromatic, polyketide portion, which is glycosylated with an amino sugar in order to exhibit its biological activity. Collision-induced dissociation spectra revealed very similar product ions for each analog, however, important differences were seen in the relative abundances and the ease at which certain fragments were formed. Fragment ions observed included those from cleavage of the glycosidic bond, loss of the side chain from the aglycone moiety, water losses and loss of a methyl radical. Following cleavage of the glycosidic bond, the charge can either reside on the aglycone portion or the sugar moiety, and each of these primary fragments undergoes several secondary dissociation pathways, depending on the collision energy. By ramping the collision voltage, we were able to correlate the changes in fragmentation behavior with small alterations in the structure of the precursor ion. The detailed study of the fragmentation behavior of doxorubicin was supported by accurate mass measurements, using an electrospray-time of flight instrument, as well as MS3 data from a quadrupole-linear ion trap mass spectrometer. Computational studies were also performed to help explain the role of certain functional groups in the fragmentation reactions.Peer reviewed: YesNRC publication: Ye

Dissociation reactions of protonated anthracycline antibiotics following electrospray ionization-tandem mass spectrometry

Fragmentation pathways of doxorubicin, a common cancer therapy agent, and three closely related analogs (epirubicin, daunorubicin, idarubicin) were compared using electrospray ionization with tandem mass spectrometry. This class of antibiotics with anti-tumour activity has important structural features, with a tetracyclic aromatic, polyketide portion, which is glycosylated with an amino sugar in order to exhibit its biological activity. Collision-induced dissociation spectra revealed very similar product ions for each analog, however, important differences were seen in the relative abundances and the ease at which certain fragments were formed. Fragment ions observed included those from cleavage of the glycosidic bond, loss of the side chain from the aglycone moiety, water losses and loss of a methyl radical. Following cleavage of the glycosidic bond, the charge can either reside on the aglycone portion or the sugar moiety, and each of these primary fragments undergoes several secondary dissociation pathways, depending on the collision energy. By ramping the collision voltage, we were able to correlate the changes in fragmentation behavior with small alterations in the structure of the precursor ion. The detailed study of the fragmentation behavior of doxorubicin was supported by accurate mass measurements, using an electrospray-time of flight instrument, as well as MS3 data from a quadrupole-linear ion trap mass spectrometer. Computational studies were also performed to help explain the role of certain functional groups in the fragmentation reactions.Peer reviewed: YesNRC publication: Ye

Development of a Computational Tool to Rival Experts in the Prediction of Sites of Metabolism of Xenobiotics by P450s

The metabolism of xenobioticsand more specifically drugsin the liver is a critical process controlling their half-life. Although there exist experimental methods, which measure the metabolic stability of xenobiotics and identify their metabolites, developing higher throughput predictive methods is an avenue of research. It is expected that predicting the chemical nature of the metabolites would be an asset for designing safer drugs and/or drugs with modulated half-lives. We have developed IMPACTS (In-silico Metabolism Prediction by Activated Cytochromes and Transition States), a computational tool combining docking to metabolic enzymes, transition state modeling, and rule-based substrate reactivity prediction to predict the site of metabolism (SoM) of xenobiotics. Its application to sets of CYP1A2, CYP2C9, CYP2D6, and CYP3A4 substrates and comparison to experts’ predictions demonstrates its accuracy and significance. IMPACTS identified an experimentally observed SoM in the top 2 predicted sites for 77% of the substrates, while the accuracy of biotransformation experts’ prediction was 65%. Application of IMPACTS to external sets and comparison of its accuracy to those of eleven other methods further validated the method implemented in IMPACTS

Development of a Computational Tool to Rival Experts in the Prediction of Sites of Metabolism of Xenobiotics by P450s

The metabolism of xenobioticsand more specifically drugsin the liver is a critical process controlling their half-life. Although there exist experimental methods, which measure the metabolic stability of xenobiotics and identify their metabolites, developing higher throughput predictive methods is an avenue of research. It is expected that predicting the chemical nature of the metabolites would be an asset for designing safer drugs and/or drugs with modulated half-lives. We have developed IMPACTS (In-silico Metabolism Prediction by Activated Cytochromes and Transition States), a computational tool combining docking to metabolic enzymes, transition state modeling, and rule-based substrate reactivity prediction to predict the site of metabolism (SoM) of xenobiotics. Its application to sets of CYP1A2, CYP2C9, CYP2D6, and CYP3A4 substrates and comparison to experts’ predictions demonstrates its accuracy and significance. IMPACTS identified an experimentally observed SoM in the top 2 predicted sites for 77% of the substrates, while the accuracy of biotransformation experts’ prediction was 65%. Application of IMPACTS to external sets and comparison of its accuracy to those of eleven other methods further validated the method implemented in IMPACTS