NMR and mutational identification of the collagen-binding site of the chaperone Hsp47.

Heat shock protein 47 (Hsp47) acts as a client-specific chaperone for collagen and plays a vital role in collagen maturation and the consequent embryonic development. In addition, this protein can be a potential target for the treatment of fibrosis. Despite its physiological and pathological importance, little is currently known about the collagen-binding mode of Hsp47 from a structural aspect. Here, we describe an NMR study that was conducted to identify the collagen-binding site of Hsp47. We used chicken Hsp47, which has higher solubility than its human counterpart, and applied a selective (15)N-labeling method targeting its tryptophan and histidine residues. Spectral assignments were made based on site-directed mutagenesis of the individual residues. By inspecting the spectral changes that were observed upon interaction with a trimeric collagen peptide and the mutational data, we successfully mapped the collagen-binding site in the B/C ß-barrel domain and a nearby loop in a 3D-homology model based upon a serpin fold. This conclusion was confirmed by mutational analysis. Our findings provide a molecular basis for the design of compounds that target the interaction between Hsp47 and procollagen as therapeutics for fibrotic diseases.

Drug interaction prediction using ontology-driven hypothetical assertion framework for pathway generation followed by numerical simulation.

BACKGROUND: In accordance with the increasing amount of information concerning individual differences in drug response and molecular interaction, the role of in silico prediction of drug interaction on the pathway level is becoming more and more important. However, in view of the interferences for the identification of new drug interactions, most conventional information models of a biological pathway would have limitations. As a reflection of real world biological events triggered by a stimulus, it is important to facilitate the incorporation of known molecular events for inferring (unknown) possible pathways and hypothetic drug interactions. Here, we propose a new Ontology-Driven Hypothetic Assertion (OHA) framework including pathway generation, drug interaction detection, simulation model generation, numerical simulation, and hypothetic assertion. Potential drug interactions are detected from drug metabolic pathways dynamically generated by molecular events triggered after the administration of certain drugs. Numerical simulation enables to estimate the degree of side effects caused by the predicted drug interactions. New hypothetic assertions of the potential drug interactions and simulation are deduced from the Drug Interaction Ontology (DIO) written in Web Ontology Language (OWL). RESULTS: The concept of the Ontology-Driven Hypothetic Assertion (OHA) framework was demonstrated with known interactions between irinotecan (CPT-11) and ketoconazole. Four drug interactions that involved cytochrome p450 (CYP3A4) and albumin as potential drug interaction proteins were automatically detected from Drug Interaction Ontology (DIO). The effect of the two interactions involving CYP3A4 were quantitatively evaluated with numerical simulation. The co-administration of ketoconazole may increase AUC and Cmax of SN-38(active metabolite of irinotecan) to 108% and 105%, respectively. We also estimates the potential effects of genetic variations: the AUC and Cmax of SN-38 may increase to 208% and 165% respectively with the genetic variation UGT1A1*28/*28 which reduces the expression of UGT1A1 down to 30%. CONCLUSION: These results demonstrate that the Ontology-Driven Hypothetic Assertion framework is a promising approach for in silico prediction of drug interactions. The following future researches for the in silico prediction of individual differences in the response to the drug and drug interactions after the administration of multiple drugs: expansion of the Drug Interaction Ontology for other drugs, and incorporation of virtual population model for genetic variation analysis, as well as refinement of the pathway generation rules, the drug interaction detection rules, and the numerical simulation models.

The chloroplast genome from a lycophyte (microphyllophyte), Selaginella uncinata, has a unique inversion, transpositions and many gene losses.

We determined the complete nucleotide sequence of the chloroplast genome of Selaginella uncinata, a lycophyte belonging to the basal lineage of the vascular plants. The circular double-stranded DNA is 144,170 bp, with an inverted repeat of 25,578 bp separated by a large single copy region (LSC) of 77,706 bp and a small single copy region (SSC) of 40,886 bp. We assigned 81 protein-coding genes including four pseudogenes, four rRNA genes and only 12 tRNA genes. Four genes, rps15, rps16, rpl32 and ycf10, found in most chloroplast genomes in land plants were not present in S. uncinata. While gene order and arrangement of the chloroplast genome of another lycophyte, Hupertzia lucidula, are almost the same as those of bryophytes, those of S. uncinata differ considerably from the typical structure of bryophytes with respect to the presence of a unique 20 kb inversion within the LSC, transposition of two segments from the LSC to the SSC and many gene losses. Thus, the organization of the S. uncinata chloroplast genome provides a new insight into the evolution of lycophytes, which were separated from euphyllophytes approximately 400 million years ago.

Phenosite: a web database integrating the mouse phenotyping platform and the experimental procedures in mice.

Recently, a number of collaborative large-scale mouse mutagenesis programs have been launched. These programs aim for a better understanding of the roles of all individual coding genes and the biological systems in which these genes participate. In international efforts to share phenotypic data among facilities/institutes, it is desirable to integrate information obtained from different phenotypic platforms reliably. Since the definitions of specific phenotypes often depend on a tacit understanding of concepts that tends to vary among different facilities, it is necessary to define phenotypes based on the explicit evidence of assay results. We have developed a website termed PhenoSITE (Phenome Semantics Information with Terminology of Experiments: http://www.gsc.riken.jp/Mouse/), in which we are trying to integrate phenotype-related information using an experimental-evidence-based approach. The site's features include (1) a baseline database for our phenotyping platform; (2) an ontology associating international phenotypic definitions with experimental terminologies used in our phenotyping platform; (3) a database for standardized operation procedures of the phenotyping platform; and (4) a database for mouse mutants using data produced from the large-scale mutagenesis program at RIKEN GSC. We have developed two types of integrated viewers to enhance the accessibility to mutant resource information. One viewer depicts a matrix view of the ontology-based classification and chromosomal location of each gene; the other depicts ontology-mediated integration of experimental protocols, baseline data, and mutant information. These approaches rely entirely upon experiment-based evidence, ensuring the reliability of the integrated data from different phenotyping platforms.

Drug interaction ontology (DIO) for inferences of possible drug-drug interactions.

Drug Interaction Ontology (DIO) was developed for formal representation of pharmacological knowledge. It provides a fundamental framework for accumulation of reusable knowledge components in molecular pharmacology. Ontology was employed and implemented as a relational model. Some features include: 1) Drug-biomolecule interaction was assumed as a primitive knowledge element. 2) Symbolic representation was developed for drug-biomolecule interaction. Consequences of two conjugated units of interaction were defined by using symbols. These are applied for query development for identification of possible drug-drug interaction. 3) The triadic relationship model was developed as a ground model for bio-logical interactions and/or function, including semantic ones. One application of DIO is to support hypothesis generation of drug interaction by providing new hypotheses from a structured database storing literature information on known drug-biomolecule interactions. A knowledge base using DIO that contains information beginning with anti-cancer drugs is now under development. Detection of possible drug interaction was tested and its capacity to lead clinically known ones was confirmed. The system generated theoretically possible drug-drug interactions, which implies potential usefulness of new drugs to be tested before actual clinical application. In this paper, sorivudine and 5-fluorouracil mediated by dihydropyrimidine dehydrogenase are presented.