The KEGG databases at GenomeNet

The Kyoto Encyclopedia of Genes and Genomes (KEGG) is the primary database resource of the Japanese GenomeNet service (http://www.genome.ad.jp/) for understanding higher order functional meanings and utilities of the cell or the organism from its genome information. KEGG consists of the PATHWAY database for the computerized knowledge on molecular interaction networks such as pathways and complexes, the GENES database for the information about genes and proteins generated by genome sequencing projects, and the LIGAND database for the information about chemical compounds and chemical reactions that are relevant to cellular processes. In addition to these three main databases, limited amounts of experimental data for microarray gene expression profiles and yeast two-hybrid systems are stored in the EXPRESSION and BRITE databases, respectively. Furthermore, a new database, named SSDB, is available for exploring the universe of all protein coding genes in the complete genomes and for identifying functional links and ortholog groups. The data objects in the KEGG databases are all represented as graphs and various computational methods are developed to detect graph features that can be related to biological functions. For example, the correlated clusters are graph similarities which can be used to predict a set of genes coding for a pathway or a complex, as summarized in the ortholog group tables, and the cliques in the SSDB graph are used to annotate genes. The KEGG databases are updated daily and made freely available (http://www.genome.ad.jp/kegg/).     

Validation of metabolic pathway databases based on chemical substructure search

Metabolic pathway databases such as KEGG contain information on thousands of biochemical reactions drawn from the biomedical literature. Ensuring consistency of such large metabolic pathways is essential to their proper use. In this paper, we present a new method to determine consistency of an important class of biochemical reactions. Our method exploits the knowledge of the atomic rearrangement pattern in biochemical reactions, to reduce the automatic atom mapping problem to a series of chemical substructure searches between the substrate and the product of a biochemical reaction. As an illustrative application, we describe the exhaustive validation of a substantial portion from the latest release of the KEGG LIGAND database.     

Searching for pharmacophoric patterns in databases of three-dimensional chemical structures

This paper provides an overview of the research that has been carried out in Sheffield over the last decade into searching techniques for databases of three-dimensional (3D) chemical structures. A 3D structure or query pattern is represented by a labelled graph, in which the nodes and the edges of the graph are used to represent atoms and the associated inter-atomic distances, respectively. The presence of a pharmacophore in each of the structures in a database can then be tested by means of a subgraph isomorphism algorithm, the computational requirements of which are minimized by the use of an initial screening procedure that eliminates the majority of the structures from the subgraph-isomorphism search. Analogous graph-based representation and searching methods can also be used with flexible 3D structures: in this case, the edges of the graphs represent inter-atomic distance ranges and a final conformational search needs to be carried out for those molecules that match the query pharmacophore in the subgraph-isomorphism search. The paper also reviews related work on the automatic identification of pharmacophoric patterns and on 3D similarity searching.     

A scalable machine-learning approach to recognize chemical names within large text databases

MOTIVATION: The use or study of chemical compounds permeates almost every scientific field and in each of them, the amount of textual information is growing rapidly. There is a need to accurately identify chemical names within text for a number of informatics efforts such as database curation, report summarization, tagging of named entities and keywords, or the development/curation of reference databases. RESULTS: A first-order Markov Model (MM) was evaluated for its ability to distinguish chemical names from words, yielding approximately 93% recall in recognizing chemical terms and approximately 99% precision in rejecting non-chemical terms on smaller test sets. However, because total false-positive events increase with the number of words analyzed, the scalability of name recognition was measured by processing 13.1 million MEDLINE records. The method yielded precision ranges from 54.7% to 100%, depending upon the cutoff score used, averaging 82.7% for approximately 1.05 million putative chemical terms extracted. Extracted chemical terms were analyzed to estimate the number of spelling variants per term, which correlated with the total number of times the chemical name appeared in MEDLINE. This variability in term construction was found to affect both information retrieval and term mapping when using PubMed and Ovid.     

Foundation and development of biochemistry at the Imperial Kharkov University

The article summarizes the biochemical researches carried out at Kharkiv Imperial University from the middle of XIX century up to the cessation of its existence in 1920 as a result of transformation into the Kharkiv Institute of National Education. Scientific activity at the Chair of Medical Chemistry at Medical Department is described in details. Information on professors who led the chair and their researches are represented. Among them a great attention is spared to the Kharkiv works of such famous scientists as A. Danilevsky and V. Gulevich, who made a great contribution to the development of Russian and world biochemistry. There are also some resordes about researches of biological and physiological chemistry carried out at other chairs of Medical Department and Department of Physics and Mathematics of the Kharkiv University. In particular, the article presents the works of well-known plant physiologists and biochemists prof. V. Palladin and V. Zalessky, and the endocrinological researches led by prof. A. Reprev.     

Remote access to ACNUC nucleotide and protein sequence databases at PBIL

The ACNUC biological sequence database system provides powerful and fast query and extraction capabilities to a variety of nucleotide and protein sequence databases. The collection of ACNUC databases served by the Pôle Bio-Informatique Lyonnais includes the EMBL, GenBank, RefSeq and UniProt nucleotide and protein sequence databases and a series of other sequence databases that support comparative genomics analyses: HOVERGEN and HOGENOM containing families of homologous protein-coding genes from vertebrate and prokaryotic genomes, respectively; Ensembl and Genome Reviews for analyses of prokaryotic and of selected eukaryotic genomes. This report describes the main features of the ACNUC system and the access to ACNUC databases from any internet-connected computer. Such access was made possible by the definition of a remote ACNUC access protocol and the implementation of Application Programming Interfaces between the C, Python and R languages and this communication protocol. Two retrieval programs for ACNUC databases, Query_win, with a graphical user interface and raa_query, with a command line interface, are also described. Altogether, these bioinformatics tools provide users with either ready-to-use means of querying remote sequence databases through a variety of selection criteria, or a simple way to endow application programs with an extensive access to these databases. Remote access to ACNUC databases is open to all and fully documented (http://pbil.univ-lyon1.fr/databases/acnuc/acnuc.html).     

Practical use of chemical shift databases for protein solid-state NMR: 2D chemical shift maps and amino-acid assignment with secondary-structure information

We introduce a Python-based program that utilizes the large database of ¹³C and ¹⁵N chemical shifts in the Biological Magnetic Resonance Bank to rapidly predict the amino acid type and secondary structure from correlated chemical shifts. The program, called PACSYlite Unified Query (PLUQ), is designed to help assign peaks obtained from 2D ¹³C–¹³C, ¹⁵N–¹³C, or 3D ¹⁵N–¹³C–¹³C magic-angle-spinning correlation spectra. We show secondary-structure specific 2D ¹³C–¹³C correlation maps of all twenty amino acids, constructed from a chemical shift database of 262,209 residues. The maps reveal interesting conformation-dependent chemical shift distributions and facilitate searching of correlation peaks during amino-acid type assignment. Based on these correlations, PLUQ outputs the most likely amino acid types and the associated secondary structures from inputs of experimental chemical shifts. We test the assignment accuracy using four high-quality protein structures. Based on only the Cα and Cβ chemical shifts, the highest-ranked PLUQ assignments were 40–60 % correct in both the amino-acid type and the secondary structure. For three input chemical shifts (CO–Cα–Cβ or N–Cα–Cβ), the first-ranked assignments were correct for 60 % of the residues, while within the top three predictions, the correct assignments were found for 80 % of the residues. PLUQ and the chemical shift maps are expected to be useful at the first stage of sequential assignment, for combination with automated sequential assignment programs, and for highly disordered proteins for which secondary structure analysis is the main goal of structure determination.