Following major advances in the field of medicinal chemistry, novel drugs can now be designed systematically, instead of relying on old trial and error approaches. Current drug design strategies can be classified as being either ligand- or structure-based depending on the design process. In this paper, by describing the search for an ATP synthase inhibitor, we review two frequently used approaches in ligand-based drug design: The pharmacophore model and the quantitative structure-activity relationship (QSAR) method. Moreover, since ATP synthase ligands are potentially useful drugs in cancer therapy, pharmacophore models were constructed to pave the way for novel inhibitor designs.

MicroRNAs, which are small endogenous RNA regulators, have been associated with various types of cancer. Breast cancer is a major health threat for women worldwide. Many miRNAs were reported to be associated with the progression and carcinogenesis of breast cancer. In this study, we aimed to discover novel breast cancer-related miRNAs and to elucidate their functions. First, we identified confident miRNA-target pairs by combining data from miRNA target prediction databases and expression profiles of miRNA and mRNA. Then, miRNA-regulated protein interaction networks (PINs) were constructed with confident pairs and known interaction data in the human protein reference database (HPRD). Finally, the functions of miRNA-regulated PINs were elucidated by functional enrichment analysis. From the results, we identified some previously reported breast cancer-related miRNAs and functions of the PINs, e.g., miR-125b, miR-125a, miR-21, and miR-497. Some novel miRNAs without known association to breast cancer were also found, and the putative functions of their PINs were also elucidated. These include miR-139 and miR-383. Furthermore, we validated our results by receiver operating characteristic (ROC) curve analysis using our miRNA expression profile data, gene expression-based outcome for breast cancer online (GOBO) survival analysis, and a literature search. Our results may provide new insights for research in breast cancer-associated miRNAs.

Fluorescent liposomal nanovesicles (liposomes) are commonly used for lipid research and/or signal enhancement. However, the problem of self-quenching with conventional fluorescent liposomes limits their applications because these liposomes must be lysed to detect the fluorescent signals. Here, we developed a nonquenched fluorescent (NQF) 1 liposome by optimizing the proportion of sulforhodamine B (SRB) encapsulant and lissamine rhodamine B-dipalmitoyl phosphatidylethanol (LRB-DPPE) on a liposomal surface for signal amplification. Our study showed that 0.3% of LRB-DPPE with 200 M of SRB provided the maximal fluorescent signal without the need to lyse the liposomes. We also observed that the NQF liposomes largely eliminated self-quenching effects and produced greatly enhanced signals than SRB-only liposomes by 5.3-fold. To show their application in proteomics research, we constructed NQF liposomes that contained phosphatidylinositol 3,5-bisphosphate (PI(3,5)P 2 ) and profiled its protein interactome using a yeast proteome microarray. Our profiling led to the identification of 162 PI(3,5)P 2 -specific binding proteins (PI(3,5)P 2 -BPs). We not only recovered many proteins that possessed known PI(3,5)P 2 -binding domains, but we also found two unknown Pfam domains (Pfam-B_8509 and Pfam-B_10446) that were enriched in our dataset. The validation of many newly discovered PI(3,5)P 2 -BPs was performed using a bead-based affinity assay. Further bioinformatics analyses revealed that the functional roles of 22 PI(3,5)P 2 -BPs were similar to those associated with PI(3,5)P 2 , including vesicle-mediated transport, GTPase, cytoskeleton, and kinase. Among the 162 PI(3,5)P 2 -BPs, we found a novel motif, HRDIKP[ES]NJLL that showed statistical significance. A docking simulation showed that PI(3,5)P 2 interacted primarily with lysine or arginine side chains of the newly identified PI(3,5)P 2 -binding kinases. Our study showed that this new tool would greatly benefit profiling lipid-protein interactions in high-throughput studies. Cell viability and physiological functions are maintained through a complex biomolecular interaction network. One of the key components in the regulatory system includes lipidprotein interactions that mediate various cell responses and metabolisms. Increasing evidence shows that such interactions have profound influences on cell polarization, the cell cycle, and other cellular processes. To date, in vitro characterizations of lipid interactions with other biomolecules are often conducted using artificial membrane models, such as liposomal nanovesicles, to mimic biological membranes. Liposomal nanovesicles, termed liposomes, are spherical vesicles that are surrounded by phospholipid bilayers in which the lipid of interest can be incorporated. An important benefit of liposomes is the ease in which a large number of fluorescent molecules can be encapsulated so that the liposome binding signals can be greatly enhanced for detection (1-4). Therefore, liposomes have become a practical and popular tool for

Many biological networks are signed molecular networks which consist of positive and negative links. To reveal the distinct features between links with different signs, we proposed signed link-clustering coefficients that assess the similarity of inter-action profiles between linked molecules. We found that positive links tended to cluster together, while negative links usually behaved like bridges between positive clusters. Positive links with higher adhesiveness tended to share protein domains, be associated with protein-protein interactions and make intra-connections within protein complexes. Negative links that were more bridge-like tended to make interconnections between protein complexes. Utilizing the proposed measures to group positive links, we observed hierarchical modules that could be well characterized by functional annotations or known protein complexes. Our results imply that the proposed sign-specific measures can help reveal the network structural characteristics and the embedded biological contexts of signed links, as well as the functional organization of signed molecular networks.