Recognition of 5-Hydroxymethylcytosine by the Uhrf1 SRA Domain

Recent discovery of 5-hydroxymethylcytosine (5hmC) in genomic DNA raises the question how this sixth base is recognized by cellular proteins. In contrast to the methyl-CpG binding domain (MBD) of MeCP2, we found that the SRA domain of Uhrf1, an essential factor in DNA maintenance methylation, binds 5hmC and 5-methylcytosine containing substrates with similar affinity. Based on the co-crystal structure, we performed molecular dynamics simulations of the SRA:DNA complex with the flipped cytosine base carrying either of these epigenetic modifications. Our data indicate that the SRA binding pocket can accommodate 5hmC and stabilizes the flipped base by hydrogen bond formation with the hydroxyl group

Free energies of binding of R- and S-propranolol to wild-type and F483A mutant cytochrome P450 2D6 from molecular dynamics simulations

Detailed molecular dynamics (MD) simulations have been performed to reproduce and rationalize the experimental finding that the F483A mutant of CYP2D6 has lower affinity for R-propranolol than for S-propranolol. Wild-type (WT) CYP2D6 does not show this stereospecificity. Four different approaches to calculate the free energy differences have been investigated and were compared to the experimental binding data. From the differences between calculations based on forward and backward processes and the closure of thermodynamic cycles, it was clear that not all simulations converged sufficiently. The approach that calculates the free energies of exchanging R-propranolol with S-propranolol in the F483A mutant relative to the exchange free energy in WT CYP2D6 accurately reproduced the experimental binding data. Careful inspection of the end-points of the MD simulations involved in this approach, allowed for a molecular interpretation of the observed differences

eTOX ALLIES:an automated pipeLine for linear interaction energy-based simulations

Abstract Background Computational methods to predict binding affinities of small ligands toward relevant biological (off-)targets are helpful in prioritizing the screening and synthesis of new drug candidates, thereby speeding up the drug discovery process. However, use of ligand-based approaches can lead to erroneous predictions when structural and dynamic features of the target substantially affect ligand binding. Free energy methods for affinity computation can include steric and electrostatic protein–ligand interactions, solvent effects, and thermal fluctuations, but often they are computationally demanding and require a high level of supervision. As a result their application is typically limited to the screening of small sets of compounds by experts in molecular modeling. Results We have developed eTOX ALLIES, an open source framework that allows the automated prediction of ligand-binding free energies requiring the ligand structure as only input. eTOX ALLIES is based on the linear interaction energy approach, an efficient end-point free energy method derived from Free Energy Perturbation theory. Upon submission of a ligand or dataset of compounds, the tool performs the multiple steps required for binding free-energy prediction (docking, ligand topology creation, molecular dynamics simulations, data analysis), making use of external open source software where necessary. Moreover, functionalities are also available to enable and assist the creation and calibration of new models. In addition, a web graphical user interface has been developed to allow use of free-energy based models to users that are not an expert in molecular modeling. Conclusions Because of the user-friendliness, efficiency and free-software licensing, eTOX ALLIES represents a novel extension of the toolbox for computational chemists, pharmaceutical scientists and toxicologists, who are interested in fast affinity predictions of small molecules toward biological (off-)targets for which protein flexibility, solvent and binding site interactions directly affect the strength of ligand-protein binding

Recent Trends in In-silico Drug Discovery

A Drug designing is a process in which new leads (potential drugs) are discovered which have therapeutic benefits in diseased condition. With development of various computational tools and availability of databases (having information about 3D structure of various molecules) discovery of drugs became comparatively, a faster process. The two major drug development methods are structure based drug designing and ligand based drug designing. Structure based methods try to make predictions based on three dimensional structure of the target molecules. The major approach of structure based drug designing is Molecular docking, a method based on several sampling algorithms and scoring functions. Docking can be performed in several ways depending upon whether ligand and receptors are rigid or flexible. Hotspot grafting, is another method of drug designing. It is preferred when the structure of a native binding protein and target protein complex is available and the hotspots on the interface are known. In absence of information of three Dimensional structure of target molecule, Ligand based methods are used. Two common methods used in ligand based drug designing are Pharmacophore modelling and QSAR. Pharmacophore modelling explains only essential features of an active ligand whereas QSAR model determines effect of certain property on activity of ligand. Fragment based drug designing is a de novo approach of building new lead compounds using fragments within the active site of the protein. All the candidate leads obtained by various drug designing method need to satisfy ADMET properties for its development as a drug. In-silico ADMET prediction tools have made ADMET profiling an easier and faster process. In this review, various softwares available for drug designing and ADMET property predictions have also been listed

Identification and characterization of a ribose 2′-O-methyltransferase encoded by the ronivirus branch of Nidovirales.

Peer reviewe

PHOTOCATALYTIC DEGRADATION OF PHENOL IN WATER BY SILVER/TITANIUM DIOXIDE NANORODS COATED WITH AN ULTRATHIN MAGNESIUM OXIDE LAYER

Phenol is one of the most widespread, toxic, and recalcitrant compounds commonly found in water sources. Due to its persistent nature, conventional wastewater treatment methods are not effective to remove or degrade phenol from water. In this work, a novel photocatalyst is developed to degrade phenol under simulated sunlight. The catalyst is composed of a 1D titanium dioxide (TiOv2) nanorod decorated with elemental silver (Ag) nanoparticles, coated in an ultrathin magnesium oxide (MgO) layer through an atomic layer deposition (ALD) method. The prepared catalyst was characterized by scanning electron microscopy (SEM), x-ray diffraction (XRD), and UV-vis diffuse reflectance spectroscopy (UV-Vis DRS). The solar light photocatalytic performance of the material was evaluated and correlated with the material properties. The Ag decoration promoted light absorption and transfer of photo-induced electron-hole pairs from within TiOv2 nanorods to the catalyst surface. The ultrathin MgO layer with a subnanometer thickness further increased the light absorption and inhibited surface charge recombination through a surface passivation effect, promoting phenol degradation. The photocatalytic reaction mechanism was investigated by the examination of hydroxyl and superoxide radical production in the photocatalytic system. The results from this work demonstrate a new strategy for fabricating efficient sunlight-driven photocatalysts for the degradation of persistent water contaminants

Modeling Soft Supramolecular Nanostructures by Molecular Simulations

The design and assembly of soft supramolecular structures based on small building blocks are governed by non-covalent interactions, selective host-guest interactions, or a combination of different interaction types. There is a surprising number of studies supporting the use of computational models for mimicking supramolecular nanosystems and studying the underlying patterns of molecular recognition and binding, in multi-dimensional approaches. Based on physical properties and mathematical concepts, these models are able to provide rationales for the conformation, solvation and thermodynamic characterization of this type of systems. Molecular dynamics (MD), including free-energy calculations, yield a direct coupling between experimental and computational investigation. This chapter provides an overview of the available MD-based methods, including path-based and alchemical free-energy calculations. The theoretical background is briefly reviewed and practical instructions are introduced on the selection of methods and post-treatment procedures. Relevant examples in which non-covalent interactions dominate are presented

Integral membrane pyrophosphatases : A novel drug target for human pathogens?

Membrane-integral pyrophosphatases (mPPases) are found in several human pathogens, including Plasmodium species, the protozoan parasites that cause malaria. These enzymes hydrolyze pyrophosphate and couple this to the pumping of ions (H+ and/or Na+) across a membrane to generate an electrochemical gradient. mPPases play an important role in stress tolerance in plants, protozoan parasites, and bacteria. The solved structures of mPPases from Vigna radiata and Thermotoga maritima open the possibility of using structure-based drug design to generate novel molecules or repurpose known molecules against this enzyme. Here, we review the current state of knowledge regarding mPPases, focusing on their structure, the proposed mechanism of action, and their role in human pathogens. We also summarize different methodologies in structure-based drug design and propose an example region on the mPPase structure that can be exploited by these structure-based methods for drug targeting. Since mPPases are not found in animals and humans, this enzyme is a promising potential drug target against livestock and human pathogens. © 2016, Adrian Goldman, et al.Peer reviewe

In Vitro Reconstitution of SARS-Coronavirus mRNA Cap Methylation

SARS-coronavirus (SARS-CoV) genome expression depends on the synthesis of a set of mRNAs, which presumably are capped at their 5′ end and direct the synthesis of all viral proteins in the infected cell. Sixteen viral non-structural proteins (nsp1 to nsp16) constitute an unusually large replicase complex, which includes two methyltransferases putatively involved in viral mRNA cap formation. The S-adenosyl-L-methionine (AdoMet)-dependent (guanine-N7)-methyltransferase (N7-MTase) activity was recently attributed to nsp14, whereas nsp16 has been predicted to be the AdoMet-dependent (nucleoside-2′O)-methyltransferase. Here, we have reconstituted complete SARS-CoV mRNA cap methylation in vitro. We show that mRNA cap methylation requires a third viral protein, nsp10, which acts as an essential trigger to complete RNA cap-1 formation. The obligate sequence of methylation events is initiated by nsp14, which first methylates capped RNA transcripts to generate cap-0 7MeGpppA-RNAs. The latter are then selectively 2′O-methylated by the 2′O-MTase nsp16 in complex with its activator nsp10 to give rise to cap-1 7MeGpppA2′OMe-RNAs. Furthermore, sensitive in vitro inhibition assays of both activities show that aurintricarboxylic acid, active in SARS-CoV infected cells, targets both MTases with IC50 values in the micromolar range, providing a validated basis for anti-coronavirus drug design

Symmetric Allosteric Mechanism of Hexameric Escherichia coli Arginine Repressor Exploits Competition between L-Arginine Ligands and Resident Arginine Residues

An elegantly simple and probably ancient molecular mechanism of allostery is described for the Escherichia coli arginine repressor ArgR, the master feedback regulator of transcription in L-arginine metabolism. Molecular dynamics simulations with ArgRC, the hexameric domain that binds L-arginine with negative cooperativity, reveal that conserved arginine and aspartate residues in each ligand-binding pocket promote rotational oscillation of apoArgRC trimers by engagement and release of hydrogen-bonded salt bridges. Binding of exogenous L-arginine displaces resident arginine residues and arrests oscillation, shifting the equilibrium quaternary ensemble and promoting motions that maintain the configurational entropy of the system. A single L-arg ligand is necessary and sufficient to arrest oscillation, and enables formation of a cooperative hydrogen-bond network at the subunit interface. The results are used to construct a free-energy reaction coordinate that accounts for the negative cooperativity and distinctive thermodynamic signature of L-arginine binding detected by calorimetry. The symmetry of the hexamer is maintained as each ligand binds, despite the conceptual asymmetry of partially-liganded states. The results thus offer the first opportunity to describe in structural and thermodynamic terms the symmetric relaxed state predicted by the concerted allostery model of Monod, Wyman, and Changeux, revealing that this state is achieved by exploiting the dynamics of the assembly and the distributed nature of its cohesive free energy. The ArgR example reveals that symmetry can be maintained even when binding sites fill sequentially due to negative cooperativity, which was not anticipated by the Monod, Wyman, and Changeux model. The molecular mechanism identified here neither specifies nor requires a pathway for transmission of the allosteric signal through the protein, and it suggests the possibility that binding of free amino acids was an early innovation in the evolution of allostery