MOTIVATION: Ustiloxins A and B are toxic cyclic tetrapeptides, Tyr-Val/Ala-Ile-Gly (Y-V/A-I-G), that were originally identified from Ustilaginoidea virens, a pathogenic fungus affecting rice plants. Contrary to our report that ustiloxin B is ribosomally synthesized in Aspergillus flavus, a recent report suggested that ustiloxins are synthesized by a non-ribosomal peptide synthetase in U.virens. Thus, we analyzed the U.virens genome, to identify the responsible gene cluster. RESULTS: The biosynthetic gene cluster was identified from the genome of U.virens based on homologies to the ribosomal peptide biosynthetic gene cluster for ustiloxin B identified from A.flavus. It contains a gene encoding precursor protein having five Tyr-Val-Ile-Gly and three Tyr-Ala-Ile-Gly motifs for ustiloxins A and B, respectively, strongly indicating that ustiloxins A and B from U.virens are ribosomally synthesized. AVAILABILITY AND IMPLEMENTATION: Accession codes of the U.virens and A.flavus gene clusters in NCBI are BR001221 and BR001206, respectively. Supplementary data are available at Bioinformatics online.

BACKGROUND: Predicting enzyme active-sites in proteins is an important issue not only for protein sciences but also for a variety of practical applications such as drug design. Because enzyme reaction mechanisms are based on the local structures of enzyme active-sites, various template-based methods that compare local structures in proteins have been developed to date. In comparing such local sites, a simple measurement, RMSD, has been used so far. RESULTS: This paper introduces new machine learning algorithms that refine the similarity/deviation for comparison of local structures. The similarity/deviation is applied to two types of applications, single template analysis and multiple template analysis. In the single template analysis, a single template is used as a query to search proteins for active sites, whereas a protein structure is examined as a query to discover the possible active-sites using a set of templates in the multiple template analysis. CONCLUSIONS: This paper experimentally illustrates that the machine learning algorithms effectively improve the similarity/deviation measurements for both the analyses.

MOTIVATION: Finding functionally analogous enzymes based on the local structures of active sites is an important problem. Conventional methods use templates of local structures to search for analogous sites, but their performance depends on the selection of atoms for inclusion in the templates. RESULTS: The automatic selection of atoms so that site matches can be discriminated from mismatches. The algorithm provides not only good predictions, but also some insights into which atoms are important for the prediction. Our experimental results suggest that the metric learning automatically provides more effective templates than those whose atoms are selected manually. AVAILABILITY: Online software is available at http://www.net-machine.net/∼kato/lpmetric1/

MOTIVATION: TOPS cartoons are a schematic ion of protein three-dimensional structures in two dimensions, and are used for understanding and manual comparison of protein folds. Recently, an algorithm that produces the cartoons automatically from protein structures has been devised and cartoons have been generated to represent all the structures in the structural databank. There is now a need to be able to define target topological patterns and to search the database for matching domains. RESULTS: We have devised a formal language for describing TOPS diagrams and patterns, and have designed an efficient algorithm to match a pattern to a set of diagrams. A pattern-matching system has been implemented, and tested on a database derived from all the current entries in the Protein Data Bank (15,000 domains). Users can search on patterns selected from a library of motifs or, alternatively, they can define their own search patterns. AVAILABILITY: The system is accessible over the Web at http://tops.ebi.ac.uk/tops