Protein-protein interactions (PPIs) are central to most biological processes. Although efforts have been devoted to the development of methodology for predicting PPIs and protein interaction networks, the application of most existing methods is limited because they need information about protein homology or the interaction marks of the protein partners. In the present work, we propose a method for PPI prediction using only the information of protein sequences. This method was developed based on a learning algorithm-support vector machine combined with a kernel function and a conjoint triad feature for describing amino acids. More than 16,000 diverse PPI pairs were used to construct the universal model. The prediction ability of our approach is better than that of other sequence-based PPI prediction methods because it is able to predict PPI networks. Different types of PPI networks have been effectively mapped with our method, suggesting that, even with only sequence information, this method could be applied to the exploration of networks for any newly discovered protein with unknown biological relativity. In addition, such supplementary experimental information can enhance the prediction ability of the method.conjoint triad ͉ support vector machine T he molecular bases of cellular operations are sustained largely by different types of interactions among proteins. Thus, a major goal of functional genomics is to determine protein interaction networks for whole organisms (1). However, only recently has it become possible to combine the traditional study of proteins as isolated entities with the analysis of large protein interaction networks by using microarray and proteomic approaches (2, 3). Such kinds of studies are significantly important because many of the functions of complex systems seem to be more closely determined by their interactions rather than by the characteristics of their individual components (4). For example, metabolic pathways, signaling cascades, and transcription control processes involve complicated interaction networks (5). Recently, interaction networks have begun to be appreciated because it is necessary to address the general principles of biological systems by means of systems biology (6). Moreover, the study of protein interaction networks has been driven by potentially practical applications in drug discovery, because it might provide great insights into mechanisms of human diseases. This study may revolutionize the pipeline of drug discovery, because drugs discovered based on the protein interaction network may specifically modulate the disease-related pathway rather than simply inhibit or activate the functions of an individual target protein (7,8). Determining accurate cellular protein interaction networks with experimental methods in combination with computational approaches therefore has become a major theme of functional genomics and proteomics efforts (9).An impressive set of experimental techniques has been developed for the systematic analysis of protein-protein interactions (...

Fatty acid binding protein 4 (FABP4) is expressed in adipocytes and macrophages, and modulates inflammatory and metabolic response. Studies in FABP4-deficient mice have shown that this lipid carrier has a significant role within the field of metabolic syndrome, inflammation and atherosclerosis; thus, its inhibition may open up new opportunities to develop novel therapeutic agents. A number of potent small molecule inhibitors of FABP4 have been identified and found to have the potential to prevent and treat metabolic diseases such as type-2 diabetes and atherosclerosis. Due to the ubiquity of endogenous fatty acids and the high intracellular concentration of FABP4, the inhibitors need to have significantly greater intrinsic potency than endogenous fatty acids. Furthermore, heart-type FABP (FABP3), which is expressed in both heart and skeletal muscle, is involved in active fatty acid metabolism where it transports fatty acids from the cell membrane to mitochondria for oxidation. However, FABP3 shares high overall sequence identity and similar 3D structure with FABP4, but has a potential problem with selectivity. In this review, we would like to analyze the main inhibitors that have appeared in the literature in the last decade, focusing on chemical structures, biological properties, selectivity and structure-activity relationships.