From malaria to cancer: Computational drug repositioning of amodiaquine using PLIP interaction patterns.

Drug repositioning identifies new indications for known drugs. Here we report repositioning of the malaria drug amodiaquine as a potential anti-cancer agent. While most repositioning efforts emerge through serendipity, we have devised a computational approach, which exploits interaction patterns shared between compounds.

Pioneering topological methods for network-based drug-target prediction by exploiting a brain-network self-organization theory.

The bipartite network representation of the drug-target interactions (DTIs) in a biosystem enhances understanding of the drugs' multifaceted action modes, suggests therapeutic switching for approved drugs and unveils possible side effects. As experimental testing of DTIs is costly and time-consuming, computational predictors are of great aid. Here, for the first time, state-of-the-art DTI supervised predictors custom-made in network biology were compared-using standard and innovative validation frameworks-with unsupervised pure topological-based models designed for general-purpose link prediction in bipartite networks.

Discovery of Mycobacterium tuberculosis InhA Inhibitors by Binding Sites Comparison and Ligands Prediction.

Drug discovery is usually focused on a single protein target; in this process, existing compounds that bind to related proteins are often ignored. We describe ProBiS plugin, extension of our earlier ProBiS-ligands approach, which for a given protein structure allows prediction of its binding sites and, for each binding site, the ligands from similar binding sites in the Protein Data Bank. We developed a new database of precalculated binding site comparisons of about 290000 proteins to allow fast prediction of binding sites in existing proteins.

Computational Drug Repositioning by Target Hopping: A Use Case in Chagas Disease.

Background: Drug repositioning aims to identify novel indications for existing drugs. One approach to repositioning exploits shared binding sites between the drug targets and other proteins. Here, we review the principle and algorithms of such target hopping and illustrate them in Chagas disease, an in Latin America widely spread, but neglected disease.

Polypharmacology rescored: protein-ligand interaction profiles for remote binding site similarity assessment.

Detection of remote binding site similarity in proteins plays an important role for drug repositioning and off-target effect prediction. Various non-covalent interactions such as hydrogen bonds and van-der-Waals forces drive ligands' molecular recognition by binding sites in proteins. The increasing amount of available structures of protein-small molecule complexes enabled the development of comparative approaches.

Drug Promiscuity in PDB: Protein Binding Site Similarity Is Key.

Drug repositioning applies established drugs to new disease indications with increasing success. A pre-requisite for drug repurposing is drug promiscuity (polypharmacology) - a drug's ability to bind to several targets. There is a long standing debate on the reasons for drug promiscuity.

Drug repositioning through incomplete bi-cliques in an integrated drug-target-disease network.

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.

Extraction methods of red blood cell membrane proteins for Multidimensional Protein Identification Technology (MudPIT) analysis.

Since red blood cells (RBCs) lack nuclei and organelles, cell membrane is their main load-bearing component and, according to a dynamic interaction with the cytoskeleton compartment, plays a pivotal role in their functioning. Even if erythrocyte membranes are available in large quantities, the low abundance and the hydrophobic nature of cell membrane proteins complicate their purification and detection by conventional 2D gel-based proteomic approaches. So, in order to increase the efficiency of RBC membrane proteome identification, here we took advantage of a simple and reproducible membrane sub-fractionation method coupled to Multidimensional Protein Identification Technology (MudPIT).