A molecular dynamics simulation study of the local structures and H-bond distribution for water–dimethyl sulfoxide (DMSO) mixtures over the entire composition range is presented. Analysis of several site-site pair distribution functions reveals that two well-defined kinds of aggregates characterize the molecular association between water and DMSO in solution. One of them, already identified through recent neutron diffraction experiments and computer simulations, consists of two water molecules H-bonded to the oxygen atom of a DMSO molecule, such that the angle between the two H-bonds is nearly tetrahedral. The other complex features a central water molecule and two DMSO, making H-bonds to water hydrogens. According to the simulation data, these molecular aggregates coexist with each other in the mixture, but their proportions change with composition. 1DMSO-2water complexes predominate over 2DMSO-1water aggregates for water-rich mixtures (water mole fractions >50%), whereas the opposite is true for DMSO-rich mixtures. The present simulations also seem to indicate that an association between a pair of DMSO molecules through their oxygen atoms is made possible by the formation of the 2DMSO-1water complexes. Implications of the existence of these aggregates to the mobility and other dynamic properties of these mixtures are briefly discussed.

SigrafW is Windows-compatible software developed using the Microsoft® Visual Basic Studio program that uses the simplified Hill equation for fitting kinetic data from allosteric and Michaelian enzymes. SigrafW uses a modified Fibonacci search to calculate maximal velocity (V), the Hill coefficient (n), and the enzyme-substrate apparent dissociation constant (K). The estimation of V, K, and the sum of the squares of residuals is performed using a Wilkinson nonlinear regression at any Hill coefficient (n). In contrast to many currently available kinetic analysis programs, SigrafW shows several advantages for the determination of kinetic parameters of both hyperbolic and nonhyperbolic saturation curves. No initial estimates of the kinetic parameters are required, a measure of the goodness-of-the-fit for each calculation performed is provided, the nonlinear regression used for calculations eliminates the statistical bias inherent in linear transformations, and the software can be used for enzyme kinetic simulations either for educational or research purposes.Keywords: Enzyme kinetics, allosteric enzyme, Michaelian enzyme, nonlinear fitting.The accurate estimation of kinetic parameters is of fundamental importance for biochemical studies. The use of partially purified enzyme preparations and the apparently complex relationship between velocity and substrate concentration are perhaps the main reasons that encourage enzyme characterization to be carried out in a simplified manner. In addition, enzyme kinetics analyses are often difficult to comprehend and apply because of confusing theoretical explanations and excessive use of mathematical extrapolation. Furthermore, enzyme kinetics teaching requires the association of theoretical lectures with time consuming experiments and calculations. In addition, it is frequently difficult and expensive to obtain enzymes with a specific and known mechanism of action. As a consequence, most aspects of enzymatic kinetics are often superficially exploited for teaching purposes [1,2].According to their kinetic behavior enzymes are classified as Michaelian [3] or allosteric [4]. For allosteric enzymes, fitting and plotting of data are usually performed according the simplified Hill equation [5]:where v is the reaction rate for the substrate concentration [S], V is the maximal rate, and K is the enzyme-substrate complex dissociation constant. The plot of log (v/(V Ϫ v)) versus log [S] results in a straight line that allows the determination of the kinetic parameters after linear regression data treatment. However, fitting experimental data for allosteric enzyme kinetics using linear regression of the Hill plot can produce unreliable results due to the uncertainty of the estimates of V for the reaction. The Hill treatment has been successfully applied to steady-state kinetics by Monod and colleagues in their classical work on allosteric enzymes [6]. However, it is well known that there are limitations in the interpretation of Hill coefficients determined from steady-state kinetics as com...

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Atomic force microscopy (AFM) is one of the most commonly used scanning probe microscopy techniques for nanoscale imaging and characterization of lipid-based particles. However, obtaining images of such particles using AFM is still a challenge. The present study extends the capabilities of AFM to the characterization of proteoliposomes, a special class of liposomes composed of lipids and proteins, mimicking matrix vesicles (MVs) involved in the biomineralization process. To this end, proteoliposomes were synthesized, composed of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) and 1,2-dipalmitoyl-sn-glycero-3-phospho-l-serine (DPPS), with inserted tissue-nonspecific alkaline phosphatase (TNAP) and/or annexin V (AnxA5), both characteristic proteins of osteoblast-derived MVs. We then aimed to study how TNAP and AnxA5 insertion affects the proteoliposomes’ membrane properties and, in turn, interactions with type II collagen, thus mimicking early MV activity during biomineralization. AFM images of these proteoliposomes, acquired in dynamic mode, revealed the presence of surface protrusions with distinct viscoelasticity, thus suggesting that the presence of the proteins induced local changes in membrane fluidity. Surface protrusions were measurable in TNAP-proteoliposomes but barely detectable in AnxA5-proteoliposomes. More complex surface structures were observed for proteoliposomes harboring both TNAP and AnxA5 concomitantly, resulting in a lower affinity for type II collagen fibers compared to proteoliposomes harboring AnxA5 alone. The present study achieved the topographic analysis of lipid vesicles by direct visualization of structural changes, resulting from protein incorporation, without the need for fluorescent probes.