Three-dimensional local structures located near active sites are responsible for protein functions. To predict protein functions, we developed a method to extract three-dimensional structural characteristics of functions. We chose EF-hand calcium binding proteins as the model target. We defined a local structure as the structure within 12Å from an arbitrary Asp(C^α). Analysis of the amino acid propensities in the active sites showed that there were clear preferences for certain amino acids to locate at the binding sites. Furthermore, the analysis of the distributions of the distances between Asp(C^α) and any other amino acids(C^α) showed that there were preferred distances for respective amino acids. Using such structural data, we derived a DB (distance based) score and a DPB (distance and propensity based) score for the prediction of functional sites. A search for EF-hand calcium binding sites was performed to test the method. EF-hand calcium binding sites were successfully predicted. Key Words: protein local structures, calcium binding protein, EF-hand calcium binding site, amino acid propensity, distance distribution

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We have implemented an efficient, user-friendly, and web-based "biosimulator" named BEST-KIT (Biochemical Engineering System analyzing Tool-KIT: http://www.best-kit.org) for analyzing large-scale nonlinear reaction networks such as metabolic pathways. The BEST-KIT mainly consists of a module, "MassAction++, " that can construct and analyze a reaction scheme represented by both mass action law (mass balance) and approximated velocity functions of enzyme kinetics at steady state. This module was developed in Java applet style and can be carried out on "any" platform machine through a web browser. In this study, we developed the parameter-estimation module for MassAction++. This module can estimate the values of unknown kinetic parameters based on the experimentally observed time-course data of state variables. We adopted three optimization techniques, the modified Powell method, the genetic algorithm, and the Hybrid method, which incorporates the genetic algorithm into the modified Powell method. The user can use an appropriate method for each purpose.

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Nuclear receptors form a superfamily of proteins that function as ligand-activated transcription factors. One class of these receptors is hormone receptors to which steroid/thyroid hormones/retinoid bind, and thus is involved in endocrine system disorders, such as breast and prostate cancer. Another class of receptors were called orphan receptors because their ligands were unknown. Recently, the ligands were identified for some of these receptors, revealing their functions as metabolic sensors. Moreover the relationship between orphan receptors and various metabolic disorders that are now collectively called Syndrome X was unveiled. Syndrome X is lifestyle related health disorders including obesity, diabetes, hyperlipidemia, hypertension, and atherosclerosis. Orphan receptors also act as defense sensors against xeno- and endobiotics, for their target genes include drug and xenobiotic chemical metabolic enzymes (cytochromes P-450) and transporters. The nuclear receptors and Syndrome X offer a wide range of chemical computing and bioinformatics research topics: receptor modeling, ligand-receptor docking, interaction of DNA and receptor dimer complex, identification of target genes, cis-regulatory elements, product proteins, and the resultant pathways/networks, and modeling and simulation of the pancreatic beta-cell, adipocyte, insulin signaling, and obesity. The author reviews the state-of-the-art of nuclear receptor and Syndrome X research from chemical computing and bioinformatics viewpoint, and proposes a project, called Nuclear Receptor and Syndrome X (NR-SX) Project, which is a possible killer application of chemical computing and bioinformatics in the genomic era.

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