Application of computational methods for predicting protein interactions

Protein interactions with other proteins or small molecules are critical to most physiological processes. These interactions may be characterized experimentally, but this can be time consuming and expensive; computational methods for predicting how two proteins interact, or which regions of a protein are most favorable for binding, are thus valuable tools for understanding how proteins of interest function, and have applications in drug discovery and identifying proteins of therapeutic interest. The ClusPro and FTMap algorithms for docking or solvent mapping, respectively, model protein-protein and protein-small molecule interactions, and can be used to identify the most likely orientations of a protein complex or the regions on a protein surface with the greatest propensity for binding. Here we describe three applications of ClusPro and FTMap. ClusPro was used to develop a method for determining whether a protein-protein interface is biologically relevant, by docking the proteins and comparing the results to the given interface; a larger number of near-native structures--which have interfaces similar to that of the given complex--was found to correspond to a greater probability that an interface is biological. In another project, ClusPro was used to predict whether a mutation in a multimeric complex would trigger the formation of a supramolecular assembly, based on how often that mutated residue appeared in the interfaces of the docking results; if a mutation caused such a residue to be present in the docked interfaces more often, in comparison to those of the wild-type structure, then it was likely to induce self-assembly. FTMap was used to detect and analyze the druggability of potential allosteric sites in kinases, with mapping performed on all available kinase structures to identify and determine the potential binding affinity of binding hot spots located outside of the active site. Discrimination of proteins as dimers or monomers was implemented as an addition to the ClusPro server, ClusPro-DC, and the results of the druggability analysis of kinases were organized into an online resource, the Kinase Atlas.2019-02-20T00:00:00

Molecular reductions in glucokinase activity increase counter-regulatory responses to hypoglycemia in mice and humans with diabetes.

OBJECTIVE: Appropriate glucose levels are essential for survival; thus, the detection and correction of low blood glucose is of paramount importance. Hypoglycemia prompts an integrated response involving reduction in insulin release and secretion of key counter-regulatory hormones glucagon and epinephrine that together promote endogenous glucose production to restore normoglycemia. However, specifically how this response is orchestrated remains to be fully clarified. The low affinity hexokinase glucokinase is found in glucose-sensing cells involved in glucose homeostasis including pancreatic β-cells and in certain brain areas. Here, we aimed to examine the role of glucokinase in triggering counter-regulatory hormonal responses to hypoglycemia, hypothesizing that reduced glucokinase activity would lead to increased and/or earlier triggering of responses. METHODS: Hyperinsulinemic glucose clamps were performed to examine counter-regulatory responses to controlled hypoglycemic challenges created in humans with monogenic diabetes resulting from heterozygous glucokinase mutations (GCK-MODY). To examine the relative importance of glucokinase in different sensing areas, we then examined responses to clamped hypoglycemia in mice with molecularly defined disruption of whole body and/or brain glucokinase. RESULTS: GCK-MODY patients displayed increased and earlier glucagon responses during hypoglycemia compared with a group of glycemia-matched patients with type 2 diabetes. Consistent with this, glucagon responses to hypoglycemia were also increased in I366F mice with mutated glucokinase and in streptozotocin-treated β-cell ablated diabetic I366F mice. Glucagon responses were normal in conditional brain glucokinase-knockout mice, suggesting that glucagon release during hypoglycemia is controlled by glucokinase-mediated glucose sensing outside the brain but not in β-cells. For epinephrine, we found increased responses in GCK-MODY patients, in β-cell ablated diabetic I366F mice and in conditional (nestin lineage) brain glucokinase-knockout mice, supporting a role for brain glucokinase in triggering epinephrine release. CONCLUSIONS: Our data suggest that glucokinase in brain and other non β-cell peripheral hypoglycemia sensors is important in glucose homeostasis, allowing the body to detect and respond to a falling blood glucose.Yousef Jameel Fund Sir Jukes Thorn Trust Elmore Fund Chang Gung University College of Medicin

New insights into the genetic etiology of Alzheimer's disease and related dementias

Characterization of the genetic landscape of Alzheimer's disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/'proxy' AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis