Structural dynamics and allostery of Rab proteins: strategies for drug discovery and design.

Rab proteins represent the largest family of the Rab superfamily guanosine triphosphatase (GTPase). Aberrant human Rab proteins are associated with multiple diseases, including cancers and neurological disorders. Rab subfamily members display subtle conformational variations that render specificity in their physiological functions and can be targeted for subfamily-specific drug design.

Computational and Experimental Approaches to Design Inhibitors of Amylin Aggregation.

Amylin is a neuroendocrine peptide hormone secreted by pancreatic ß-cells; however, amylin is toxic to ß-cells when it is aggregated in type 2 diabetes mellitus (T2DM). It is important to understand amylin's structures and aggregation mechanism for the discovery and design of effective drugs to inhibit amylin aggregation. In this review, we investigated experimental and computational studies on amylin structures and inhibitors.

Allosteric binding sites in Rab11 for potential drug candidates.

Rab11 is an important protein subfamily in the RabGTPase family. These proteins physiologically function as key regulators of intracellular membrane trafficking processes. Pathologically, Rab11 proteins are implicated in many diseases including cancers, neurodegenerative diseases and type 2 diabetes.

Structural analysis of protein tyrosine phosphatase 1B reveals potentially druggable allosteric binding sites.

Catalytic proteins such as human protein tyrosine phosphatase 1B (PTP1B), with conserved and highly polar active sites, warrant the discovery of druggable nonactive sites, such as allosteric sites, and potentially, therapeutic small molecules that can bind to these sites. Catalyzing the dephosphorylation of numerous substrates, PTP1B is physiologically important in intracellular signal transduction pathways in diverse cell types and tissues. Aberrant PTP1B is associated with obesity, diabetes, cancers, and neurodegenerative disorders.

Feature Selection for high Dimensional DNA Microarray data using hybrid approaches.

Feature selection from DNA microarray data is a major challenge due to high dimensionality in expression data. The number of samples in the microarray data set is much smaller compared to the number of genes. Hence the data is improper to be used as the training set of a classifier.