Threshold dissociation and molecular modeling of transition metal complexes of flavonoids

The relative threshold dissociation energies of a series of flavonoid/transition metal/auxiliary ligand complexes of the type [MII (flavonoid − H) auxiliary ligand]+ formed by electrospray ionization (ESI) were measured by energy-variable collisionally activated dissociation (CAD) in a quadrupole ion trap (QIT). For each of the isomeric flavonoid diglycoside pairs, the rutinoside (with a 1–6 inter-saccharide linkage) requires a greater CAD energy and thus has a higher dissociation threshold than its neohesperidoside (with a 1–2 inter-saccharide linkage) isomer. Likewise, the threshold energies of complexes containing flavones are higher than those containing flavanones. The monoglycoside isomers also have characteristic threshold energies. The flavonoids that are glycosylated at the 3-O- position tend to have lower threshold energies than those glycosylated at the 7-O- or 4′-O- position, and those that are C- bonded have lower threshold energies than the O- bonded isomers. The structural features that substantially influence the threshold energies include the aglycon type (flavanone versus flavone), the type of disaccharide (rutinose versus neohesperidose), and the linkage type (O- bonded versus C- bonded). Various computational means were applied to probe the structures and conformations of the complexes and to rationalize the differences in threshold energies of isomeric flavonoids. The most favorable coordination geometry of the complexes has a plane-angle of about 62°, which means that the deprotonated flavonoid and 2,2′-bipyridine within a complex do not reside on the same plane. Stable conformations of five cobalt complexes and five deprotonated flavonoids were identified. The conformations were combined with the point charges and helium accessible surface areas to explain qualitatively the differences in threshold energies for isomeric flavonoids

FKBP5 as a Selection Biomarker for Gemcitabine and Akt Inhibitors in Treatment of Pancreatic Cancer

We have recently shown that the immunophilin FKBP5 (also known as FKBP51) is a scaffolding protein that can enhance PHLPP-AKT interaction and facilitate PHLPP-mediated dephosphorylation of Akt Ser473, negatively regulating Akt activation in vitro. Therefore, FKBP5 might function as a tumor suppressor, and levels of FKBP5 would affect cell response to chemotherapy. In the current study, we have taken a step forward by using a pancreatic cancer xenograft mice model to show that down regulation of FKBP5 in shFKBP5 xenograft mice promotes tumor growth and resistance to gemcitabine, a phenomenon consistent with our previous findings in pancreatic cell lines. In addition, we also found that inhibitors targeting the Akt pathway, including PI3K inhibitor, Akt inhibitor and mTOR inhibitor had a different effect on sensitization to gemcitabine and other chemotherapeutic agents in cell lines, with a specific Akt inhibitor, triciribine, having the greatest sensitization effect. We then tested the hypothesis that addition of triciribine can sensitize gemcitabine treatment, especially in shFKBP5 pancreatic cancer xenograft mice. We found that combination treatment with gemcitabine and triciribine has a better effect on tumor inhibition than either drug alone (p<0.005) and that the inhibition effect is more significant in shFKBP5 xenograft mice than wt mice (p<0.05). These effects were correlated with level of Akt 473 phosphorylation as well as proliferation rate, as indicated by Ki67 staining in xenograft tumor tissues. These results provide evidence in support of future clinical trials designed to tailor therapy based on our observations

A Continuum Poisson-Boltzmann Model for Membrane Channel Proteins

Membrane proteins constitute a large portion of the human proteome and perform a variety of important functions as membrane receptors, transport proteins, enzymes, signaling proteins, and more. The computational studies of membrane proteins are usually much more complicated than those of globular proteins. Here we propose a new continuum model for Poisson-Boltzmann calculations of membrane channel proteins. Major improvements over the existing continuum slab model are as follows: 1) The location and thickness of the slab model are fine-tuned based on explicit-solvent MD simulations. 2) The highly different accessibility in the membrane and water regions are addressed with a two-step, two-probe grid labeling procedure, and 3) The water pores/channels are automatically identified. The new continuum membrane model is optimized (by adjusting the membrane probe, as well as the slab thickness and center) to best reproduce the distributions of buried water molecules in the membrane region as sampled in explicit water simulations. Our optimization also shows that the widely adopted water probe of 1.4 {\AA} for globular proteins is a very reasonable default value for membrane protein simulations. It gives an overall minimum number of inconsistencies between the continuum and explicit representations of water distributions in membrane channel proteins, at least in the water accessible pore/channel regions that we focus on. Finally, we validate the new membrane model by carrying out binding affinity calculations for a potassium channel, and we observe a good agreement with experiment results.Comment: 40 pages, 6 figures, 5 table

Plasticity and Axonal Sprouting of Contralateral Cortex after Unilateral Traumatic Brain Injury

poster abstractAbstract According to the Centers for Disease Control and Prevention, an estimated 1.7 million Americans experience Traumatic Brain Injury (TBI) annually and about 52,000 of them die. TBI results in a primary injury of brain tissue. It can also cause a secondary damage as well depending on the severity of the injury, which could lead to different types of dysfunctions such as persistent motor or cognitive deficits. We hypothesize that cortical injury from unilateral TBI will cause plasticity and axon sprouting of the contralateral cortex, which may contribute to functional compensation and recovery. Controlled cortical impact (CCI) is a methods used in our research laboratory to create TBI models in rats and mice. To test our hypothesis, one hemisphere of each mouse brain is moderately injured by the CCI technique to allow us to determine if there is significant axon sprouting in the contralateral cortex. Axon sprouting is expected to occur at certain time period after the injury. To determine the existence and the timing of axon sprouting, two sets of CCI and sham mice were used for histological analysis at two different time points after CCI. The first set contains 4 sham and 6 CCI mice and examined at 6 weeks post-injury; the second set contains 4 sham and 6 CCI mice and examined at 3 weeks post-injury. Immunostaining to growth-associated protein-43 (GAP-43) will be used to detect sprouting axons in the injured cortex. However, the brains are currently in process for the staining. . Further data collection and image analysis will be needed to obtain the results and findings of the research

Association pattern mining in spatio-temporal databases

Ph.DDOCTOR OF PHILOSOPH

Hierarchical Structure with Highly Ordered Macroporous-Mesoporous Metal-Organic Frameworks as Dual Function for CO2 Fixation.

As a major greenhouse gas, the continuous increase of carbon dioxide (CO2) in the atmosphere has caused serious environmental problems, although CO2 is also an abundant, inexpensive, and nontoxic carbon source. Here, we use metal-organic framework (MOF) with highly ordered hierarchical structure as adsorbent and catalyst for chemical fixation of CO2 at atmospheric pressure, and the CO2 can be converted to the formate in excellent yields. Meanwhile, we have successfully integrated highly ordered macroporous and mesoporous structures into MOFs, and the macro-, meso-, and microporous structures have all been presented in one framework. Based on the unique hierarchical pores, high surface area (592 m2/g), and high CO2 adsorption capacity (49.51 cm3/g), the ordered macroporous-mesoporous MOFs possess high activity for chemical fixation of CO2 (yield of 77%). These results provide a promising route of chemical CO2 fixation through MOF materials

Direct Cr (VI) bio-reduction with organics as electron donor by anaerobic sludge

Industrial activities produce lots of Cr (VI)-containing wastewater. This study presented a detailed work on direct Cr (VI) bio-reduction (i.e. Cr (VI) is reduced with organics as electron donor directly) by anaerobic sludge through both batch and long-term experiments. Effects of pH and initial Cr (VI) concentrations on direct Cr (VI) bio-reduction activity were evaluated. The highest direct Cr (VI) bio-reduction rate was achieved at pH 8.0 at 104\ua0mg Cr (VI)/g MLVSS/d (MLVSS: mixed liquor volatile suspended solids), corresponding to the highest protein release (124\ua0mg/g MLVSS) and cell viability (71%). In contrast, the direct Cr (VI) bio-reduction rates were 46, 70 and 82\ua0mg Cr (VI)/g MLVSS/d, respectively, at pH 6.0, 7.0 and 9.0. Also, the direct Cr (VI) bio-reduction activity decreased by 74% when initial Cr (VI) concentration increased from 10\ua0mg/L to 50\ua0mg/L. The contribution of chemical adsorption to Cr (VI) removal was found to be negligible, whereas biosorption played a role in Cr (VI) removal although its role was insignificant. Indirect Cr (VI) bio-reduction (i.e. Cr (VI) is chemically reduced by sulfide produced from biological sulfate reduction) rate (990\ua0mg Cr (VI)/g MLVSS/d) was faster than that (210\ua0mg Cr (VI)/g MLVSS/d) of direct Cr (VI) bio-reduction, indicating that indirect Cr (VI) bio-reduction would dominate the Cr (VI) bio-reduction pathway if both Cr (VI) and sulfate were present. The direct Cr (VI) bio-reduction was then successfully demonstrated in an up-flow anaerobic sludge bed (UASB) reactor, where the Cr (VI) was completely removed with a Cr (VI) removal rate of 1.0\ua0mg Cr (VI)/L/h. 454 pyrosequencing results revealed that direct Cr (VI) bio-reduction related genera were Desulfovibrio, Ochrobactrum and Anaerovorax

Efficient Formulation of Polarizable Gaussian Multipole Electrostatics for Biomolecular Simulations

Molecular dynamics simulations of biomolecules have been widely adopted in biomedical studies. As classical point-charge models continue to be used in routine biomolecular applications, there have been growing demands on developing polarizable force fields for handling more complicated biomolecular processes. Here we focus on a recently proposed polarizable Gaussian Multipole (pGM) model for biomolecular simulations. A key benefit of pGM is its screening of all short-range electrostatic interactions in a physically consistent manner, which is critical for stable charge-fitting and is needed to reproduce molecular anisotropy. Another advantage of pGM is that each atom's multipoles are represented by a single Gaussian function or its derivatives, allowing for more efficient electrostatics than other Gaussian-based models. In this study we present an efficient formulation for the pGM model defined with respect to a local frame formed with a set of covalent basis vectors. The covalent basis vectors are chosen to be along each atom's covalent bonding directions. The new local frame allows molecular flexibility during molecular simulations and facilitates an efficient formulation of analytical electrostatic forces without explicit torque computation. Subsequent numerical tests show that analytical atomic forces agree excellently with numerical finite-difference forces for the tested system. Finally, the new pGM electrostatics algorithm is interfaced with the PME implementation in Amber for molecular simulations under the periodic boundary conditions. To validate the overall pGM/PME electrostatics, we conducted an NVE simulation for a small water box of 512 water molecules. Our results show that, to achieve energy conservation in the polarizable model, it is important to ensure enough accuracy on both PME and induction iteration

Spatial Discretization for Stochastic Semi-Linear Subdiffusion Equations Driven by Fractionally Integrated Multiplicative Space-Time White Noise

From MDPI via Jisc Publications RouterHistory: accepted 2021-08-03, pub-electronic 2021-08-12Publication status: PublishedSpatial discretization of the stochastic semi-linear subdiffusion equations driven by fractionally integrated multiplicative space-time white noise is considered. The nonlinear terms f and σ satisfy the global Lipschitz conditions and the linear growth conditions. The space derivative and the fractionally integrated multiplicative space-time white noise are discretized by using the finite difference methods. Based on the approximations of the Green functions expressed by the Mittag–Leffler functions, the optimal spatial convergence rates of the proposed numerical method are proved uniformly in space under some suitable smoothness assumptions of the initial value

ADME Evaluation in Drug Discovery. 8. The Prediction of Human Intestinal Absorption by a Support Vector Machine

Human intestinal absorption (HIA) is an important roadblock in the formulation of new drug substances. In silico models for predicting the percentage of HIA based on calculated molecular descriptors are highly needed for the rapid estimation of this property. Here, we have studied the performance of a support vector machine (SVM) to classify compounds with high or low fractional absorption (%FA > 30% or %FA ≤ 30%). The analyzed data set consists of 578 structural diverse druglike molecules, which have been divided into a 480-molecule training set and a 98-molecule test set. Ten SVM classification models have been generated to investigate the impact of different individual molecular properties on %FA. Among these studied important molecule descriptors, topological polar surface area (TPSA) and predicted apparent octanol−water distribution coefficient at pH 6.5 (logD_(6.5)) show better classification performance than the others. To obtain the best SVM classifier, the influences of different kernel functions and different combinations of molecular descriptors were investigated using a rigorous training-validation procedure. The best SVM classifier can give satisfactory predictions for the training set (97.8% for the poor-absorption class and 94.5% for the good-absorption class). Moreover, 100% of the poor-absorption class and 97.8% of the good-absorption class in the external test set could be correctly classified. Finally, the influence of the size of the training set and the unbalanced nature of the data set have been studied. The analysis demonstrates that large data set is necessary for the stability of the classification models. Furthermore, the weights for the poor-absorption class and the good-absorption class should be properly balanced to generate unbiased classification models. Our work illustrates that SVMs used in combination with simple molecular descriptors can provide an extremely reliable assessment of intestinal absorption in an early in silico filtering process