Short Abstract: FABIA is a biclustering algorithm that clusters rows and columns of a matrix simultaneously. Consequently, members of a row cluster are similar to each other on a subset of columns and, analogously, members of a column cluster are similar to each other on a subset of rows. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. FABIA is used to detect transcriptomic modules from gene expression data that is obtained from drugdesign studies.

Short Abstract: The poster is based on in silico design of a peptide based drug against Nipah virus and predicting its mode of action in silico. Around late twentieth century, a new variety of zoonotic paramyxovirus struck the peninsular Malaysia, then consecutively Bangladesh and India causing. Subsequent investigation of this virus (Nipah virus) indicated to its host Pteropus bats. No treatment poses it as a threat.

By docking strategy we have predicted a peptide, active against Influenza viral replication; also responsible for inhibition of attachment protein (score=8016), fusion protein (score=8342), matrix protein (score=7078), nucleocapsid (score=8380) as well as C protein (score=7838) of Nipah virus which is even in case of Hemaglutinin of influenza; scoring only 6000.

All the data about the genetic makeup of Nipah virus was collected from the NCBI website. Several servers such as CPHmodel 3.0, PatchDock, Pocket Finder, Q-site Finder, SignalP 4.0 and software such as Cn3D 4.1, SPDBV_PC_4.01, Rasmol, Motif Finder, Chime plug-in, ligand Scout, Mega 5.0 was used in our process which involved quest for signal peptide and subsequently nucleic acid binding motif for better insight to explain docking results from a different point of view besides attachment inhibition.

Along with other computational data and the energy change of the docking reactions; some clever hypotheses about NiV packaging and mode of action against nucleocapsid were also impressive supporting the aspiration of defeating Nipah virus.. In fact, the scores are indicating more effective interaction of the peptide with surface proteins of Nipah virus than influenza!

Short Abstract: State of the art supercomputers, specifically designed to simulate the dynamics of molecules, are nowadays capable of calculating the motions of small solvated proteins over a temporal range of milliseconds. They provide detail both in high temporal and spatial resolution. The costs of these productions however is unnecessarily massive, since such methodology represents a brute-force attempt, and, depending on the targeted question, might be produced with lean and significantly faster methods. One such method is the focus of this poster. In the presented multiscale framework, we show how the sampling problem of free energy calculations is tackled. This approach uses a thermodynamic cycle in which, instead of evaluating directly the free energies of a reaction, the same is performed in low resolution using a coarse-grained force-field. Unavoidable disagreements due to the coarse-grained nature of the force-field are corrected with a free energy perturbation, in which the difference in free energy of going from low detail to high detail is determined. The coarse-grained force-field that is used here is called AmberCG. It is functionally based on Warshel's coarse-grained force-field, but was further modified to be compatible to the Amber force-field series by a reparametrization obtained from a genetic algorithm. The resulting parametrization was validated by using a databank of 21 proteins, representing all prominent folds.

Short Abstract: Here we show that the clinical side-effects (SEs) provide a human phenotypic profile for the drug, and this profile can suggest additional disease indications. Closer attention should be paid to the SEs observed in trials not just to evaluate the harmful effects, but also to explore the repositioning potential.

Short Abstract: Chemical-Protein Interactome is a computational methodology with a focus on characterizing differential drug efficacy and side effects through the analysis of the chemical-protein interactions and the down stream gene expression perturbations. The methodology opens opportunities for developing patient-specific medication in terms of decreasing adverse reactions and broadening the therapeutic profile.

Short Abstract: Recently, there has been much interest in gene-disease networks and polypharmacology as a basis for drug repositioning. Here, we integrate data from structural and chemical databases to create a drug-target-disease network for 147 promiscuous drugs, their 553 protein targets, and 44 disease indications. Visualizing and analyzing such complex networks is still an open problem. We approach it by mining the network for network motifs of bi-cliques. In our case, a bi-clique is a subnetwork in which every drug is linked to every target and disease. Since the data is incomplete, we identify incomplete bi-cliques, whose completion introduces novel, predicted links from drugs to targets and diseases. We demonstrate the power of this approach by repositioning cardiovascular drugs to parasitic diseases, by predicting the cancer-related kinase PIK3CG as novel target of resveratrol, and by identifying for five drugs a shared binding site in four serine proteases and novel links to cancer, cardiovascular, and parasitic diseases.