How conformational changes can affect catalysis, inhibition and drug resistance of enzymes with induced-fit binding mechanism such as the HIV-1 protease

A central question is how the conformational changes of proteins affect their function and the inhibition of this function by drug molecules. Many enzymes change from an open to a closed conformation upon binding of substrate or inhibitor molecules. These conformational changes have been suggested to follow an induced-fit mechanism in which the molecules first bind in the open conformation in those cases where binding in the closed conformation appears to be sterically obstructed such as for the HIV-1 protease. In this article, we present a general model for the catalysis and inhibition of enzymes with induced-fit binding mechanism. We derive general expressions that specify how the overall catalytic rate of the enzymes depends on the rates for binding, for the conformational changes, and for the chemical reaction. Based on these expressions, we analyze the effect of mutations that mainly shift the conformational equilibrium on catalysis and inhibition. If the overall catalytic rate is limited by product unbinding, we find that mutations that destabilize the closed conformation relative to the open conformation increase the catalytic rate in the presence of inhibitors by a factor exp(ddG/RT) where ddG is the mutation-induced shift of the free-energy difference between the conformations. This increase in the catalytic rate due to changes in the conformational equilibrium is independent of the inhibitor molecule and, thus, may help to understand how non-active-site mutations can contribute to the multi-drug-resistance that has been observed for the HIV-1 protease. A comparison to experimental data for the non-active-site mutation L90M of the HIV-1 protease indicates that the mutation slightly destabilizes the closed conformation of the enzyme.Comment: 9 pages, 2 figures, 3 tables; to appear in "BBA Proteins and Proteomics" as part of a special issue with the title "The emerging dynamic view of proteins: Protein plasticity in allostery, evolution and self-assembly.

The Effect of Charge at the Surface of Silver Nanoparticles on Antimicrobial Activity against Gram-Positive and Gram-Negative Bacteria: A Preliminary Study

The bactericidal efficiency of various positively and negatively charged silver nanoparticles has been extensively evaluated in literature, but there is no report on efficacy of neutrally charged silver nanoparticles. The goal of this study is to evaluate the role of electrical charge at the surface of silver nanoparticles on antibacterial activity against a panel of microorganisms. Three different silver nanoparticles were synthesized by different methods, providing three different electrical surface charges (positive, neutral, and negative). The antibacterial activity of these nanoparticles was tested against gram-positive (i.e., Staphylococcus aureus, Streptococcus mutans, and Streptococcus pyogenes) and gram-negative (i.e., Escherichia coli and Proteus vulgaris) bacteria. Well diffusion and micro-dilution tests were used to evaluate the bactericidal activity of the nanoparticles. According to the obtained results, the positively-charged silver nanoparticles showed the highest bactericidal activity against all microorganisms tested. The negatively charged silver nanoparticles had the least and the neutral nanoparticles had intermediate antibacterial activity. The most resistant bacteria were Proteus vulgaris. We found that the surface charge of the silver nanoparticles was a significant factor affecting bactericidal activity on these surfaces. Although the positively charged nanoparticles showed the highest level of effectiveness against the organisms tested, the neutrally charged particles were also potent against most bacterial species

Quantitative Structure-Electrochemistry Relationship Study of Some Organic Compounds Using PC-ANN and PCR

Motivation. A QSPR analysis has been conducted on the half-wave reduction potential (E1/2) of a diverse set of organic compounds by means of principal component regression (PCR) and principal component-artificial neural network (PC-ANN) modeling method. Genetic algorithm was employed as a factor selection procedure for both modeling methods. The results were compared with two other factor selection methods namely eigen-value ranking (EV) and correlation ranking (CR) procedures. Method. By using the Dragon software more than 1000 structural descriptors were calculated for each molecule. The descriptor data matrix was subjected to principal component analysis and the most significant principal components (PC) were extracted. Multiple linear regression and artificial neural network were employed for the respective linear and nonlinear modeling between the extracted principal components and E1/2. First, the principal components were ranked by decreasing eigen-values and entered successively to each modeling method separately. In addition, the factors were ranked by their corresponding correlation (linear correlation for PCR and nonlinear correlation for PC-ANN models) with the half-wave potentials and entered to the models. Finally, genetic algorithm (GA) was also employed to select the best set of factors for both models. Results. The 96 % of variances in the descriptor data matrix could be explained by 30 first extracted PCs. Amon

An All-in-One Solid State Thin-Layer Potentiometric Sensor and Biosensor Based on Three-Dimensional Origami Paper Microfluidics

An origami three-dimensional design of a paper-based potentiometric sensor is described. In its simplest form, this electrochemical paper-based analytical device (ePAD) is made from three small parts of the paper. Paper layers are folded on each other for the integration of a solid contact ion selective electrode (here a carbon-paste composite electrode) and a solid-state pseudo-reference electrode (here writing pencil 6B on the paper), which are in contact with a hydrophilic channel fabricated on the middle part (third part) of the paper. In this case, the pseudo-reference and working electrodes are connected to the two sides of the hydrophilic channel and hence the distance between them is as low as the width of paper. The unmodified carbon paste electrode (UCPE) and modification with the crown ether benzo15-crown-5 (B15C5) represented a very high sensitivity to Cu (II) and Cd2+ ions, respectively. The sensor responded to H2O2 using MnO2-doped carbon paste electrode (CPE). Furthermore, a biosensor was achieved by the addition of glucose oxidase to the MnO2-doped CPE and hence made it selective to glucose with ultra-sensitivity. In addition to very high sensitivity, our device benefits from consuming a very low volume of sample (10.0 µL) and automatic sampling without need for sampling devices

Novel amino acids indices based on quantum topological molecular similarity and their application to QSAR study of peptides

A new source of amino acid (AA) indices based on quantum topological molecular similarity (QTMS) descriptors has been proposed for use in QSAR study of peptides. For each bond of the chemical structure of AA, eight electronic properties were calculated using the approaches of bond critical point and theory of atom in molecule. Thus, for each molecule a data matrix of QTMS descriptors (having information from both topology and electronic features) were calculated. Using four different criterion based on principal component analysis of the QTMS data matrices, four different sets of AA indices were generated. The indices were used as the input variables for QSAR study (employing genetic algorithm-partial least squares) of three peptides' data sets, namely, angiotensin-converting enzyme inhibitors, bactericidal peptides and the peptides binding to the HLA-A*0201 molecule. The obtained models had better prediction ability or a comparable one with respect to the previously reported models. In addition, by using the proposed indices and analysis of the variable important in projection, the active site of the peptides which plays a significant role in the biological activity of interest, was identified

QSAR studies on the anesthetic action of some polyhalogenated ethers

There has been an on-going debate about the mode of action of general anesthetics and until now, many sites have been postulated as target site for action of these compounds. Here, some quantum chemical-based quantitative structure-activity relationship (QSAR) models were developed for a set of polyhalogenated ethers in order to investigate the aspects of their anesthetic action, which is not completely defined yet, although some hypotheses have been suggested. A data set including 25 polyhalogenated methyl ethyl ethers were selected, and different descriptors were calculated for each molecule using density functional theory calculations, and subsequently some multilinear QSAR models were built by using different sets of the calculated molecular descriptors. The result showed that polar (polarizability) and non-polar (log P) parameters have mixed role on the anesthetic activity i.e. models with high statistical quality were obtained in combination with these two parameters. Also a good model between anesthetic action and electrostatic potentials was obtained, which may imply the important role of electronic interactions in the anesthetic activity of the compounds. Finally, a four-parametric QSAR model containing log P, molecular polarizability, most positive charge and an electrostatic potential parameters was obtained, which indicated that the anesthetic action of the polyhaloganted ethers may be proceeded through lipophilic, steric and columbic interactions

Identification of the Source of Geographical Origin of Iranian Crude Oil by Chemometrics Analysis of Fourier Transform Infrared Spectra

In this paper, we analyzed the crude oils of five different regions of Iran to identify their sources of geographical origin. The Fourier transform infrared (FTIR) spectra of samples were analyzed by chemometrics methods to make discrimination between the different crude oil sources. Infrared (IR) spectroscopy in conjunction with chemometrics techniques allows for online monitoring in real time, which can be of considerable use in the petroleum industry. Principal component analysis (PCA) and extended canonical variates analysis (ECVA), as unsupervised and supervised classification methods, respectively, were employed. The PCA scores made a relative discrimination between the different crude oil sources; however, the degree of classification was not satisfactory. Instead, more accurate classification results were achieved by ECVA. The results show that the spectral region 1350–1490 cm^–1 possessed much better performances for classification by ECVA. This spectral region, which is attributed to the SO, aromatic CC, and methylene C–C vibrations, suggests that the difference between crude oils of these geographical origins is primarily attributed to the difference in sulfoxide and aromatic compounds. The ECVA technique was found as a promising classification model and has shown good classification power for crude oil sources

Quantitative Structure–Property Relationship Study to Predict Speed of Sound in Diverse Organic Solvents from Solvent Structural Information

The interaction of solvents with ultrasonic waves is of drastic importance and has been the subject of many studies in recent years. In this study, the effect of solvent structural parameters on the speed of sound in chemical solvents was investigated through a quantitative structure–property relationship (QSPR). Genetic algorithm–multiple linear regression (GA-MLR) analysis was employed to select the most relevant subset of descriptors and, then, to develop the model. The validity of the obtained 10-parameter model was assessed by most widely used validation techniques. The predictive power of the model was evaluated by use of an external data set. The high level of accuracy of results approved the model. According to the model, those solvents that have stronger solvent–solvent interactions can create a more appropriate medium for passing and propagating sound waves and will result in higher speed of sounds

Comparative QSAR studies on toxicity of phenol derivatives using quantum topological molecular similarity indices

Quantitative structure activity relationship (QSAR) analyses using a novel type of electronic descriptors called quantum topological molecular similarity (QTMS) indices were operated to describe and compare the mechanisms of toxicity of phenols toward five different strains (i.e., Tetrahymena pyriformis, L1210 Leukemia, Pseudomonas putida, Raja japonica and Cucumis sativus). The appropriate QSAR models for the toxicity data were obtained separately employing partial least squares (PLS) regression combined with genetic algorithms (GA), as a variable selection method. The resulting QSAR models were used to identify molecular fragments of phenol derivatives whose electronic properties contribute significantly to the observed toxicities. Using this information, it was feasible to discriminate between the mechanisms of action of phenol toxicity to the studied strains. It was found that toxicities of phenols to all strains, except with L1210 Leukemia, are significantly affected by electronic features of the phenolic hydroxyl group (C-O-H). Meanwhile, the resulting models can describe the inductive and resonance effects of substituents on various toxicities