Channel-Forming Activities in the Glycosomal Fraction from the Bloodstream Form of Trypanosoma brucei

Background: Glycosomes are a specialized form of peroxisomes (microbodies) present in unicellular eukaryotes that belong to the Kinetoplastea order, such as Trypanosoma and Leishmania species, parasitic protists causing severe diseases of livestock and humans in subtropical and tropical countries. The organelles harbour most enzymes of the glycolytic pathway that is responsible for substrate-level ATP production in the cell. Glycolysis is essential for bloodstream-form Trypanosoma brucei and enzymes comprising this pathway have been validated as drug targets. Glycosomes are surrounded by a single membrane. How glycolytic metabolites are transported across the glycosomal membrane is unclear. Methods/Principal Findings: We hypothesized that glycosomal membrane, similarly to membranes of yeast and mammalian peroxisomes, contains channel-forming proteins involved in the selective transfer of metabolites. To verify this prediction, we isolated a glycosomal fraction from bloodstream-form T.brucei and reconstituted solubilized membrane proteins into planar lipid bilayers. The electrophysiological characteristics of the channels were studied using multiple channel recording and single channel analysis. Three main channel-forming activities were detected with current amplitudes 70–80 pA, 20–25 pA, and 8–11 pA, respectively (holding potential +10 mV and 3.0 M KCl as an electrolyte). All channels were in fully open state in a range of voltages 6150 mV and showed no sub-conductance transitions. The channel with current amplitude 20–25 pA is anion-selective (P K+/P Cl2,0.31), while the other two types of channels are slightl

Concentration Dependent Ion Selectivity in VDAC: A Molecular Dynamics Simulation Study

The voltage-dependent anion channel (VDAC) forms the major pore in the outer mitochondrial membrane. Its high conducting open state features a moderate anion selectivity. There is some evidence indicating that the electrophysiological properties of VDAC vary with the salt concentration. Using a theoretical approach the molecular basis for this concentration dependence was investigated. Molecular dynamics simulations and continuum electrostatic calculations performed on the mouse VDAC1 isoform clearly demonstrate that the distribution of fixed charges in the channel creates an electric field, which determines the anion preference of VDAC at low salt concentration. Increasing the salt concentration in the bulk results in a higher concentration of ions in the VDAC wide pore. This event induces a large electrostatic screening of the charged residues promoting a less anion selective channel. Residues that are responsible for the electrostatic pattern of the channel were identified using the molecular dynamics trajectories. Some of these residues are found to be conserved suggesting that ion permeation between different VDAC species occurs through a common mechanism. This inference is buttressed by electrophysiological experiments performed on bean VDAC32 protein akin to mouse VDAC

Systems Pharmacology – Machine Learning Approaches in Profiling Oncology Drug Candidates

While the thesis is framed from the systems thinking perspective, however, the main focus is on the drug discovery and application of machine learning approaches in profiling oncology drug candidates for a select subset of validated targets in the oncogenesis pathways. In this study, we built in-silico predictive models to predict prospective drug candidates from compound libraries. Robust predictive models help in saving enormous experimental, and resource overheads and compress product cycle times. We used several machine learning algorithms, in building models that include logistic regression (LR), support vector machines (SVMs), Naïve Bayes, Artificial neural nets (ANN), and Decision trees – classification and regression tree (CART) and multi-tree majority voting ensemble techniques i.e., random forest and XGBoost.The feature sets for building these models were extracted by computing chemical fingerprints and quantum chemical descriptors. We generated both sparse and dense matrices for modeling. We cross-validated, parameter hypertuned, and evaluated model performance on different statistical performance metrics, including Receiver-Operating Characteristic (ROC) curves. We investigated the full and reduced model through feature engineering for model stability with LR models. We evaluated model regularization techniques, namely, LASSO, Ridge, Elastic Net, and Neural drop to prevent model overfitting both for LR and ANN models. We evaluated SVM kernels and showed non-linear radial basis function (RBF) performed better than others. We also showed that adding additional hidden layers, beyond three, to the ANN model with ADAM optimizer did not improve performance. Besides, multi-tree ensemble models were superior to single tree models (CART). Finally, we benchmarked the performance metrics of each of these machine learning algorithms in a side-by-side comparison and conclude that the ensemble random forest produced the lowest mean misclassification error.S.M

Reconstitution of Membrane Proteins into Platforms Suitable for Biophysical and Structural Analyses

Integral membrane proteins historically have been challenging targets for biophysical research due to their low solubility in aqueous solution. Their importance for chemical and electrical signaling between cells, however, makes them fascinating targets for investigators interested in the regulation of cellular and physiological processes. Since membrane proteins shunt the barrier imposed by the cell membrane, they also serve as entry points for drugs, adding pharmaceutical research and development to the interests. In recent years, detailed understanding of membrane protein function has significantly increased due to high-resolution structural information obtained from single-particle cryoEM, X-ray crystallography, and NMR. In order to further advance our mechanistic understanding on membrane proteins as well as foster drug development, it is crucial to generate more biophysical and functional data on these proteins under defined conditions. To that end, different techniques have been developed to stabilize integral membrane proteins in native-like environments that allow both structural and biophysical investigations – amphipols, lipid bicelles, and lipid nanodiscs. In this chapter, we provide detailed protocols for the reconstitution of membrane proteins according to these three techniques. We also outline some of the possible applications of each technique and discuss their advantages and possible caveats

Alzheimer's disease related genes during primate evolution

During primate evolution, the neuronal and cognition-related genes have evolved rapidly. These genes seem to induce neurological illnesses such as Alzheimer's disease (AD). In this study, we analyzed genes APOE, TOMM40, and PICALM known as the risk factors of AD. We performed bioinformatics analyses in relation to evolution, phylogeny, and protein structure for those genes in humans, Neanderthals, chimpanzees, bonobos, gorillas, orangutans, crab-eating monkeys, and rhesus monkeys. Cholesterol-related genes showed relatively rapid evolution toward a lower risk of AD. Neanderthals showed relatively higher polymorphism in genes APOE, TOMM40, and PICALM than humans did. Phylogeny indicated different topologies in the trichotomy of humans, chimpanzees, and gorillas in terms of genes APOE, TOMM40, and PICALM. These results provide to hominin-specific patterns in three genes, and give clues to the modern human-specific traits of AD and shed light on further functional research helping to understand AD