L-Type Calcium Channels: Structure and Functions

Voltage-gated calcium channels (VGCCs) manage the electrical signaling of cells by allowing the selective-diffusion of calcium ions in response to the changes in the cellular membrane potential. Among the different VGCCs, the long-lasting or the L-type calcium channels (LTCCs) are prevalently expressed in a variety of cells, such as skeletal muscle, ventricular myocytes, smooth muscles and dendritic cells and forms the largest family of the VGCCs. Their wide expression pattern and significant role in diverse cellular events, including neurotransmission, cell cycle, muscular contraction, cardiac action potential and gene expression, has made these channels the major targets for drug development. In this book chapter, we aim to provide a comprehensive overview of the different VGCCs and focus on the sequence-structure–function properties of the LTCCs. Our chapter will summarize and review the various experimental and computational analyses performed on the structures of the LTCCs and their implications in drug discovery applications

Fixed soft point theorems for generalized contractive mapping on soft metric spaces

In this paper, we introduce new notions in a soft metric space. We study a fixed soft point under generalized contractive conditions without mappings continuity. Further, we prove some results related to our generalization. Moreover, we provide one example to present the application.Publisher's Versio

Improved inference for the generalized Pareto distribution under linear, power and exponential normalization

summary:We discuss three estimation methods: the method of moments, probability weighted moments, and L-moments for the scale parameter and the extreme value index in the generalized Pareto distribution under linear normalization. Moreover, we adapt these methods to use for the generalized Pareto distribution under power and exponential normalizations. A simulation study is conducted to compare the three methods on the three models and determine which is the best, which turned out to be the probability weighted moments. A new computational technique for improving fitting quality is proposed and tested on two real-world data sets using the probability weighted moments. We looked back at various maximal data sets that had previously been addressed in the literature and for which the generalized extreme value distribution under linear normalization had failed to adequately explain them. We use the suggested procedure to find good fits

VERSATILE CATALYTIC TRANSFER HYDROGENATIONS IN ORGANIC SYNTHESIS

Catalytic transfer hydrogenation reactions are extremely useful in organic synthesis. We have investigated numerous reactions with ammonium formate (and other hydrogen gas donor) and 10% Pd/C successfully without using hydrogen gas. The reactions are very fast and produced products with high yields. Reduction of unsaturated groups, hydrogenolysis, reductive bond cleavage, allylic deacetoxylation, and dehalogenation are conducted using this method. In some instances, useful selectivity of reactions is observed. Most of the reactions are investigated with β-lactams as the substrates

Effects of Temperature on the p53-DNA Binding Interactions and Their Dynamical Behavior: Comparing the Wild Type to the R248Q Mutant

Background: The protein p53 plays an active role in the regulation of cell cycle. In about half of human cancers, the protein is inactivated by mutations located primarily in its DNA-binding domain. Interestingly, a number of these mutations possess temperature-induced DNA-binding characteristics. A striking example is the mutation of Arg248 into glutamine or tryptophan. These mutants are defective for binding to DNA at 310 K although they have been shown to bind specifically to several p53 response elements at sub-physiological temperatures (298-306 K). Methodology/Principal Findings: This important experimental finding motivated us to examine the effects of temperature on the structure and configuration of R248Q mutant and compare it to the wild type protein. Our aim is to determine how and where structural changes of mutant variants take place due to temperature changes. To answer these questions, we compared the mutant to the wild-type proteins from two different aspects. First, we investigated the systems at the atomistic level through their DNA-binding affinity, hydrogen bond networks and spatial distribution of water molecules. Next, we assessed changes in their long-lived conformational motions at the coarse-grained level through the collective dynamics of their side-chain and backbone atoms separately. Conclusions: The experimentally observed effect of temperature on the DNA-binding properties of p53 is reproduced. Analysis of atomistic and coarse-grained data reveal that changes in binding are determined by a few key residues and provide a rationale for the mutant-loss of binding at physiological temperatures. The findings can potentially enable a rescue strategy for the mutant structure. \ua9 2011 Barakat et al.Peer reviewed: YesNRC publication: Ye

Applicable anode based on Co3O4–SrCO3 heterostructure nanorods-incorporated CNFs with low-onset potential for DUFCs

Besides the high-current density, lower onset potential of urea electrooxidation is key parameter which influences the direct urea fuel cell performance. In the present article, low-onset potential has been reported for nickel-free (NF) electrocatalyst in urea electrooxidation. The nickel-free electrocatalyst: Co3O4–SrCO3 heterostructure nanorods-incorporated carbon nanofibers (CNFs) were synthesized by electrospinning technique, followed by calcination of electrospun mat composed of strontium acetate, cobalt acetate, and poly(vinyl alcohol) sol–gel in inert environment at 750 °C. Physiochemical characterizations confirmed the formation of Co3O4–SrCO3 heterostructure nanorods-incorporated CNFs. The electrochemical activity of resultant nickel-free electrocatalyst toward the electrooxidation of urea in alkaline medium is evaluated using cyclic voltammetry measurements (CV). Co3O4–SrCO3 heterostructure nanorods-incorporated CNFs reveals high-current density of 21.33 mA/cm2 at low-fuel concentration. Notably, the low-onset potential has been observed, showing a good application prospect in direct urea fuel cells.This Publication was made possible by NPRP grant # [8-1344-1-246] from the Qatar National Research Fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of authors

Molecular ‘time-machines’ to unravel key biological events for drug design

Molecular dynamics (MD) has become a routine tool in structural biology andstructure-based drug design (SBDD). MD offers extraordinary insights into thestructures and dynamics of biological systems. With the current capabilities ofhigh-performance supercomputers, it is now possible to perform MD simula-tions of systems as large as millions of atoms and for several nanoseconds time-scale. Nevertheless, many complicated molecular mechanisms, including ligandbinding/unbinding and protein folding, usually take place on timescales of sev-eral microseconds to milliseconds, which are beyond the practical limits of stand-ard MD simulations. Such issues with traditional MD approaches can beeffectively tackled with new generation MD methods, such as enhanced sam-pling MD approaches and coarse-grained MD (CG-MD) scheme. The formeremploy a bias to steer the simulations and reveal biological events that are usu-ally very slow, while the latter groups atoms as interaction beads, thereby redu-cing the system size and facilitating longer MD simulations that can witnesslarge conformational changes in biological systems. In this review, we outlinemany of such advanced MD methods, and discuss how their applications areproviding significant insights into important biological processes, particularlythose relevant to drug design and discovery.This work has been funded through the Alberta Cancer Foundation (ACF), Li Ka Shing Applied Virology Insti-tute (LKSAVI), and The Natural Sciences and Engineering Research Council of Canada (NSERC

Bulked segregant analysis to detect quantitative trait loci (QTL) related to heat tolerance at grain filling rate in wheat using simple sequence repeat (SSR) markers

The grain-filling rate (GFR) plays an important role in determining grain yield. An F2 population of wheat was developed from a cross between the 2 wheat cultivars, Ksu106 (heat-tolerant) and Yecora Rojo (heat-sensitive). The parents and 205 F2 plants were planted on the 20th of January during the winter season of 2009 to evaluate heat tolerance during the grain-filling period. The sowing date in the present investigation represents the heat stress conditions in Saudi Arabia. Bulked – segregant analyses (BSA) was used in conjunction with simple sequence repeats (SSR) analysis to find markers linked to genes of heat tolerance. Composite interval mapping was used for mapping quantitative trait loci (QTL). The results reveal that 12 SSR markers: Wmc24, Wmc168, Wmc326, Xgwm30, Xgwm456, Wmc25, Wmc44, Wmc94, Wmc161, Wmc273, Wmc327 and Xgwm566 were linked to GFR by QTLs analysis of the F2 population. The results show that regression analysis for the relationship between the 12 markers and the phenotypes of F2 individuals were highly significant. The results demonstrate that SSR markers combined with bulked segregant analysis could be used to identify molecular markers linked to the grain filling rate as an indicator for heat tolerance in wheat.Keywords: Grain filling rate, QTL analysis, SSR marker, whea

Identification of new SSR markers linked to leaf chlorophyll content, flag leaf senescence and cell membrane stability traits in wheat under water stressed condition

Segregating F4 families from the cross between drought sensitive (Yecora Rojo) and drought tolerant (Pavon 76) genotypes were made to identify SSR markers linked to leaf chlorophyll content, flag leaf senescence and cell membrane stability traits in wheat (Triticum aestivum L.) under water-stressed condition and to map quantitative trait locus (QTL) for the three physiological traits. The parents and 150 F4 families were evaluated phenotypically for drought tolerance using two irrigation treatments (2500 and 7500 m3/ha). Using 400 SSR primers tested for polymorphism in testing parental and F4 families genotypes, the results revealed that QTL for leaf chlorophyll content, flag leaf senescence and cell membrane stability traits were associated with 12, 5 and 12 SSR markers, respectively and explained phenotypic variation ranged from 6 to 42%. The SSR markers for physiological traits had genetic distances ranged from 12.5 to 25.5 cM. These SSR markers can be further used in breeding programs for drought tolerance in wheat

Targeting the Aryl Hydrocarbon Receptor (AhR): A Review of the In-Silico Screening Approaches to Identify AhR Modulators

Aryl hydrocarbon receptor (AhR) is a biological sensor that integrates environmental, metabolic, and endogenous signals to control complex cellular responses in physiological and pathophysiological functions. The full-length AhR encompasses various domains, including a bHLH, a PAS A, a PAS B, and transactivation domains. With the exception of the PAS B and transactivation domains, the available 3D structures of AhR revealed structural details of its subdomains interactions as well as its interaction with other protein partners. Towards screening for novel AhR modulators homology modeling was employed to develop AhR-PAS B domain models. These models were validated using molecular dynamics simulations and binding site identification methods. Furthermore, docking of well-known AhR ligands assisted in confirming these binding pockets and discovering critical residues to host these ligands. In this context, virtual screening utilizing both ligand-based and structure-based methods screened large databases of small molecules to identify novel AhR agonists or antagonists and suggest hits from these screens for validation in an experimental biological test. Recently, machine-learning algorithms are being explored as a tool to enhance the screening process of AhR modulators and to minimize the errors associated with structure-based methods. This chapter reviews all in silico screening that were focused on identifying AhR modulators and discusses future perspectives towards this goal