Docking studies and anti-inflammatory activity of beta-hydroxy-beta-arylpropanoic acids

The article describes a two-step synthesis of diastereomeric 3-hydroxy-2-methyl-3-(4-biphenylyl)butanoic acids. In the first step an intermediate alpha-bromo propanoic acid 1-ethoxyethyl ester was synthesized. The second step is a new modified Reformatsky reaction in presence of Zn in tetrahydrofuran (THF) at -5 to 10 degrees C between the previously synthesized intermediate and 4-acetylbiphenyl. Synthesis of the other studied beta-hydroxy-beta-arylpropanoic acids has already been reported. These beta-hydroxy-beta-arylpropanoic acids belong to the arylpropanoic acid class of compounds, structurally similar to the NSAIDs such as ibuprofen. The anti-inflammatory activity and gastric tolerability of the synthesized compounds were evaluated. Molecular docking experiments were carried out to identify potential COX-2 inhibitors among the beta-hydroxy-beta-aryl-alkanoic acids class. The results indicate that all compounds possess significant anti-inflammatory activity after oral administration and that the compounds 2-(9-(9-hydroxy-fluorenyl))-2-methylpropanoic acid (5) and 3-hydroxy-3,3-diphenyl-propanoic acid (3) possess the strongest anti-inflammatory activity, comparable to that of ibuprofen, a standard NSAID,and that none of tested substances or ibuprofen produced any significant gastric lesions.     

On the Adjacency-Jacobsthal numbers

In this article, we define the adjacency-Jacobsthal sequence and then we obtain the combinatorial representations and the sums of adjacency-Jacobsthal numbers by the aid of generating function and generating matrix of the adjacency-Jacobsthal sequence. Also, we derive the determinantal and the permanental representations of adjacency-Jacobsthal numbers by using certain matrices which are obtained from generating matrix of adjacency-Jacobsthal numbers. Furthermore, using the roots of characteristic polynomial of the adjacency-Jacobsthal sequence, we produce the Binet formula for adjacency-Jacobsthal numbers. Finally, we give the relationships between adjacency-Jacobsthal numbers and Fibonacci, Pell, and Jacobsthal numbers.     

Super solutions of the dynamical Yang-Baxter equation

Solutions of the classical dynamical Yang-Baxter equation on a Lie superalgebra are called super dynamical matrices. A super dynamical matrix satisfies the zero weight condition if

    for all 

In this paper we classify super dynamical matrices with zero weight.

     

The synthesis and characterization of a new (E,E)-dioxime and its mono and heterotrinuclear complexes containing dioxadithiadiazamacrobicyclic moieties

A new (E,E)-dioxime (2Z,3Z)-1,4,7,8,15,16-hexahydro-9,14-(ethanothioethanothioethano)quinoxalino[6,7-e] [4,7,1,10]benzodioxadiazacyclododecine-2,3,19,26-tetrone2,3-dioxime (H₂L) has been synthesized by reacting cyanogen-di-N-oxide with 2,3-diamino-6,7,14,15-tetrahydro-8,13-(ethanothioethanothioethano)dibenzo[b,h] [1,4,7,10]dioxadiazacyclodecine-17,24-dione (6). Mononuclear complexes (8, 9) of this ligand have been synthesized by reacting the vic-dioxime (H₂L) with NiCl₂ · 6H_2O and COCl₂ · 6H₂O respectively. The BF ₂ ⁺ capped cobalt(III) complex (10) of the new (E,E) vic-dioxime has been synthesized by using as precursor a hydrogen-bridged mononuclear cobalt(III) complex (9). The heterotrinuclear complex (11) has been prepared by reacting one mononuclear cobalt(III) complex (10) with [Cu(MeCN)₄]PF₆. The new compounds were characterized by a combination of elemental analysis, ¹H- and ¹³C-n.m.r, i.r. and m.s. spectral data.     

Structural rearrangement of Neisseria meningitidis transferrin binding protein A (TbpA) prior to human transferrin protein (hTf) binding

Gram-negative bacterium Neisseria meningitidis, responsible for human infectious disease meningitis, acquires the iron (Fe³⁺) ion needed for its survival from human transferrin protein (hTf). For this transport, transferrin binding proteins TbpA and TbpB are facilitated by the bacterium. The transfer cannot occur without TbpA, while the absence of TbpB only slows down the transfer. Thus, understanding the TbpA-hTf binding at the atomic level is crucial for the fight against bacterial meningitis infections. In this study, atomistic level of mechanism for TbpA-hTf binding is elucidated through 100 ns long all-atom classical MD simulations on free (uncomplexed) TbpA. TbpA protein underwent conformational change from ‘open’ state to ‘closed’ state, where two loop domains, loops 5 and 8, were very close to each other. This state clearly cannot accommodate hTf in the cleft between these two loops. Moreover, the helix finger domain, which might play a critical role in Fe³⁺ ion uptake, also shifted downwards leading to unfavorable Tbp-hTf binding. Results of this study indicated that TbpA must switch between ‘closed’ state to ‘open’ state, where loops 5 and 8 are far from each other creating a cleft for hTf binding. The atomistic level of understanding to conformational switch is crucial for TbpA-hTf complex inhibition strategies. Drug candidates can be designed to prevent this conformational switch, keeping TbpA locked in ‘closed’ state.     

Expected escape times from attractor basins due to low intensity noise

In this paper, a numerical approach is described to estimate escape times from attractor basins when a dynamical system is subjected to noise or stochastic perturbations. Noise can affect nonlinear system response by driving solution trajectories to different attractors. The changes in physical behavior can be observed as amplitude and phase change of periodic oscillations, initiation or annihilation of chaotic motion, phase synchronization, and so on. Estimating probability of transitions from one attractor to another, and predicting escape times are essential for quantifying the effects of noise on the system response. In this paper, a numerical approach is outlined where probability transition maps are generated between grids. Then, these maps are iterated to find the probability distribution after long durations, wherein, a constant escape rate can be observed between basins. The constant escape rate is then used to estimate the average escape times. The approach is applicable to systems subjected to low-intensity stochastic disturbances and with long escape times, where Monte Carlo simulations are impractical. Escape times up to \(10^{13}\) periods are estimated without relying on computationally expensive computations.