Category: 2016

Urea lesions are formed in DNA because of free radical damage of the thymine base, and their occurrence in DNA blocks DNA polymerases, which has deleterious consequences. Recently, it has been shown that urea is capable of forming hydrogen bonding and stacking interactions with nucleobases, which are responsible for the unfolding of RNA in aqueous urea. Base pairing and stacking are inherent properties of nucleobases; because urea is able to form both, this study attempts to investigate if urea can mimic nucleobases in the context of nucleic acid structures by examining the effect of introducing urea lesions complementary to the four different nucleobases on the overall helical integrity of B-DNA duplexes and their thermodynamic stabilities using molecular dynamics (MD) simulations. The MD simulations resulted in stable duplexes without significant changes in the global B-DNA conformation.

Urea transporters are membrane proteins that selectively allow urea molecules to pass through. It is not clear how these transporters allow rapid conduction of urea, a polar molecule, in spite of the presence of a hydrophobic constriction lined by aromatic rings. The current study elucidates the mechanism that is responsible for this rapid conduction by performing free energy calculations on the transporter dvUT with a cumulative sampling time of about 1.3 μs.

Riboswitches are structured RNA motifs that control gene expression by sensing the concentrations of specific metabolites and make up a promising new class of antibiotic targets. S-Adenosylmethionine (SAM)-III riboswitch, mainly found in lactic acid bacteria, is involved in regulating methionine and SAM biosynthetic pathways. SAM-III riboswitch regulates the gene expression by switching the translation process on and off with respect to the absence and presence of the SAM ligand, respectively. In this study, an attempt is made to understand the key conformational transitions involved in ligand binding using atomistic molecular dynamics (MD) simulations performed in an explicit solvent environment. G26 is found to recognize the SAM ligand by forming hydrogen bonds, whereas the absence of the ligand leads to opening of the binding pocket.

Bimetallic nanoparticles (NPs) have been shown to exhibit certain advantages over pure NPs in catalysis due to a synergistic effect. It is common to disperse NPs in a polymer matrix such as polyvinylpyrrolidone (PVP) to prevent flocculation, which imparts considerable electronic effects on the NPs. In the present study, the interactions between aqueous solutions of N-ethylpyrrolidone (EP, system chosen to model the monomeric form of PVP) and Au/Pd bimetallic NPs, which are relevant in catalysis, have been investigated using molecular dynamics simulations and density functional theory (DFT) method. The adequacy of the force fields used was assessed based on their ability to reproduce the structures and adsorption energies obtained using DFT calculations. 

Ion channels selectively allow certain ions to pass through at much higher rates than others, and thereby modulate ionic concentrations across cell membranes. The current molecular dynamics study elucidates the intricate mechanisms that render ion selectivity to the viral channel p7 by employing free energy calculations. Free energy barriers of 5.4 and 19.4 kcal mol^–1 for K⁺ and Ca²⁺, respectively, explain the selectivity of the channel reported in experiments. Initially, the permeating ions encounter a hydrophobic barrier followed by stabilization in an ion-binding site. 

Multidrug resistance in Mycobacterium tuberculosis (M. Tb) and its coexistence with HIV are the biggest therapeutic challenges in anti-M. Tb drug discovery. The current study reports a Virtual Screening (VS) strategy to identify potential inhibitors of Mycobacterial cyclopropane synthase (CmaA1), an important M. Tb target considering the above challenges. Five ligand-based pharmacophore models were generated from 40 different conformations of the cofactors of CmaA1 taken from molecular dynamics (MD) simulations trajectories of CmaA1. The screening abilities of these models were validated by screening 23 inhibitors and 1398 non-inhibitors of CmaA1. A VS protocol was designed with four levels of screening i.e., ligand-based pharmacophore screening, structure-based pharmacophore screening, docking and absorption, distribution, metabolism, excretion and the toxicity (ADMET) filters.

Hexadecahydro-1H-cyclopenta[a]phenanthrene framework (HHCPF) has been considered as one of the privileged scaffolds due to its versatile presence in many biologically essential molecules. In our quest to unravel the privileged nature of this framework, we undertook a systematic analysis of target binding and Absorption, Distribution, Metabolism, Elimination, Toxicity (ADMET)/physicochemical properties of 110 drugs containing HHCPF reported in DrugBank. Effect of number and positions of double bonds in the framework and substitutions at each carbon position on the target selectivity as well as drug like properties of these drugs were studied. Fifteen different scaffolds based on the numbers and positions of double bonds in the HHCPF were identified among these drugs. The optimum number of double bonds present in the HHCPF scaffolds was observed to be one to three, and one particular positional isomer is predominant among many scaffolds with same numbers of double bonds.

Gold clusters are currently regarded as new-generation catalysts owing to their exceptional efficiency in accelerating several classes of reactions. Density functional theory (DFT) is the method of choice for the investigation of energy pathways of reactions assisted by metal nanoparticles due to their computational efficiency. However, the reliability of such theoretical studies depends to a large extent on the choice of the DFT functional used. In the present work, the performance of a series of DFT-based functionals to accurately model the prototypical CO oxidation reaction catalyzed by a Au3Au3 cluster has been examined by comparing the results with those obtained from high-level ab initio CCSD(T) method.