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During 11-12 August 2014, a Protein Bioinformatics and Community Resources Retreat was held at the Wellcome Trust Genome Campus in Hinxton, UK. This meeting brought together the principal investigators of several specialized protein resources (such as CAZy, TCDB and MEROPS) as well as those from protein databases from the large Bioinformatics centres (including UniProt and RefSeq). The retreat was divided into five sessions: (1) key challenges, (2) the databases represented, (3) best practices for maintenance and curation, (4) information flow to and from large data centers and (5) communication and funding. An important outcome of this meeting was the creation of a Specialist Protein Resource Network that we believe will improve coordination of the activities of its member resources. We invite further protein database resources to join the network and continue the dialogue.

Abstract 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.

Many bioactive natural products are produced as “secondary metabolites” by plants, bacteria, and fungi. During the middle of the 20th century, several secondary metabolites from fungi revolutionized the pharmaceutical industry, for example, penicillin, lovastatin, and cyclosporine. They are generally biosynthesized by enzymes encoded by clusters of coordinately regulated genes, and several motif-based methods have been developed to detect secondary metabolite biosynthetic (SMB) gene clusters using the sequence information of typical SMB core genes such as polyketide synthases (PKS) and non-ribosomal peptide synthetases (NRPS). However, no detection method exists for SMB gene clusters that are functional and do not include core SMB genes at present. To advance the exploration of SMB gene clusters, especially those without known core genes, we developed MIDDAS-M, a motif-independent de novo detection algorithm for SMB gene clusters. We integrated virtual gene cluster generation in an annotated genome sequence with highly sensitive scoring of the cooperative transcriptional regulation of cluster member genes. MIDDAS-M accurately predicted 38 SMB gene clusters that have been experimentally confirmed and/or predicted by other motif-based methods in 3 fungal strains. MIDDAS-M further identified a new SMB gene cluster for ustiloxin B, which was experimentally validated. Sequence analysis of the cluster genes indicated a novel mechanism for peptide biosynthesis independent of NRPS. Because it is fully computational and independent of empirical knowledge about SMB core genes, MIDDAS-M allows a large-scale, comprehensive analysis of SMB gene clusters, including those with novel biosynthetic mechanisms that do not contain any functionally characterized genes. [ABSTRACT FROM AUTHOR]

Catalytic mechanisms of 270 enzymes from 131 superfamilies, mainly hydrolases and transferases, were analyzed based on their enzyme structures. A method of systematic comparison and classification of the catalytic reactions was developed. Hydrolysis and transfer reactions closely resemble one another, displaying common mechanisms, single displacement, and double displacement. These displacement mechanisms might be further subclassified according to the type of catalytic factors and nucleophilic substitution involved. Several types of catalytic factors exist: nucleophile, acid, base, stabilizer, modulator, cofactors. Nucleophilic substitution might be categorized as SN1/SN2 (or dissociative/associative) reactions. The classification indicates that some mechanisms favor particular types of catalytic factors. In hydrolyses of amide bonds and phosphoric ester bonds, mechanisms with single displacement tend to use inorganic cofactors such as zinc and magnesium ions as important catalysts, whereas those with double displacement frequently do not use such cofactors. In contrast, hydrolyses of O-glycoside bond rarely use such cofactors, with one exception. The trypsin-like hydrolytic reaction, which is catalyzed by the classic catalytic triad comprising serine/histidine/aspartate, can be considered as a 'super-reaction' because it is observed in at least three nonhomologous enzymes, whereas most reactions are singlets without any nonhomologous enzymes. By dividing complex reactions into several reactions, correlations between active site structures and catalytic functions can be suggested. This classification method is applicable to other reactions such as elimination and isomerization. Furthermore, it will facilitate annotation of enzyme functions from 3D patterns of enzyme active sites. The classification is available at . Proteins 2007. ©2006 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

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., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)