A gold standard set of mechanistically diverse enzyme superfamilies.
Ontology highlight
ABSTRACT:
SUBMITTER: Brown SD
PROVIDER: S-EPMC1431709 | biostudies-literature | 2006
REPOSITORIES: biostudies-literature
Publications
A gold standard set of mechanistically diverse enzyme superfamilies.
Brown Shoshana D SD Gerlt John A JA Seffernick Jennifer L JL Babbitt Patricia C PC
Genome biology 20060131 1
Superfamily and family analyses provide an effective tool for the functional classification of proteins, but must be automated for use on large datasets. We describe a 'gold standard' set of enzyme superfamilies, clustered according to specific sequence, structure, and functional criteria, for use in the validation of family and superfamily clustering methods. The gold standard set represents four fold classes and differing clustering difficulties, and includes five superfamilies, 91 families, 4 ...[more]
Similar Datasets
Project description:In functionally diverse enzyme superfamilies (SFs), conserved structural and active site features reflect catalytic capabilities 'hard-wired' in each SF architecture. Overlaid on this foundation, evolutionary changes in active site machinery, structural topology and other aspects of structural organization and interactions support the emergence of new reactions, mechanisms, and substrate specificity. This review connects topological with functional variation in each of the haloalkanoic acid dehalogenase (HAD) and vicinal oxygen chelate fold (VOC) SFs and a set of redox-active thioredoxin (Trx)-fold SFs to illustrate a few of the varied themes nature has used to evolve new functions from a limited set of structural scaffolds.
| S-EPMC3551608 | biostudies-literature
Project description:MotivationAlthough gene set enrichment analysis has become an integral part of high-throughput gene expression data analysis, the assessment of enrichment methods remains rudimentary and ad hoc. In the absence of suitable gold standards, evaluations are commonly restricted to selected datasets and biological reasoning on the relevance of resulting enriched gene sets.ResultsWe develop an extensible framework for reproducible benchmarking of enrichment methods based on defined criteria for applicability, gene set prioritization and detection of relevant processes. This framework incorporates a curated compendium of 75 expression datasets investigating 42 human diseases. The compendium features microarray and RNA-seq measurements, and each dataset is associated with a precompiled GO/KEGG relevance ranking for the corresponding disease under investigation. We perform a comprehensive assessment of 10 major enrichment methods, identifying significant differences in runtime and applicability to RNA-seq data, fraction of enriched gene sets depending on the null hypothesis tested and recovery of the predefined relevance rankings. We make practical recommendations on how methods originally developed for microarray data can efficiently be applied to RNA-seq data, how to interpret results depending on the type of gene set test conducted and which methods are best suited to effectively prioritize gene sets with high phenotype relevance.Availabilityhttp://bioconductor.org/packages/GSEABenchmarkeR.Contactludwig.geistlinger@sph.cuny.edu.
| S-EPMC7820859 | biostudies-literature
Project description:A set of cycloisomerization methodologies of alkynyl ketones and imines with concurrent acyloxy, phosphatyloxy, or sulfonyloxy group migration, which allow for the efficient synthesis of multisubstituted furans and N-fused heterocycles, has been developed. Investigation of the reaction course by way of employing 17O-labeled substrates allowed for elucidation of the mechanisms behind these diverse transformations. It was found that, while the phosphatyloxy migration in conjugated alkynyl imines in their cycloisomerization to N-fused pyrroles proceeded via a [3,3]-sigmatropic rearrangement, the analogous cycloisomerization of skipped alkynyl ketones proceeds through two consecutive 1,2-migrations, resulting in an apparent 1,3-shift, followed by a subsequent 1,2-migration through competitive oxirenium and dioxolenylium pathways. Investigations of the 1,2-acyloxy migration of conjugated alkynyl ketones en route to furans demonstrated the involvement of a dioxolenylium intermediate. The mechanism of cycloisomerization of skipped alkynyl ketones containing an acyloxy group was found to be catalyst dependent; Lewis and Brønsted acid catalysts caused an ionization/SN1' isomerization to the allene, followed by cycloisomerization to the furan, whereas transition metal catalysts evoked a Rautenstrauch-type mechanistic pathway. Furthermore, control experiments in the cycloisomerization of skipped alkynyl ketones under transition metal catalysis revealed that, indeed, these reactions were catalyzed by transition metal complexes as opposed to Brønsted acids resulting from hydrolysis of these catalysts with eventual water. Further synthetic utility of the obtained phosphatyloxy-substituted heterocycles was demonstrated through their efficient employment in the Kumada cross-coupling reaction with various Grignard reagents.
| S-EPMC2536635 | biostudies-literature
Project description:Naturally produced halogenated compounds are ubiquitous across all domains of life where they perform a multitude of biological functions and adopt a diversity of chemical structures. Accordingly, a diverse collection of enzyme catalysts to install and remove halogens from organic scaffolds has evolved in nature. Accounting for the different chemical properties of the four halogen atoms (fluorine, chlorine, bromine, and iodine) and the diversity and chemical reactivity of their organic substrates, enzymes performing biosynthetic and degradative halogenation chemistry utilize numerous mechanistic strategies involving oxidation, reduction, and substitution. Biosynthetic halogenation reactions range from simple aromatic substitutions to stereoselective C-H functionalizations on remote carbon centers and can initiate the formation of simple to complex ring structures. Dehalogenating enzymes, on the other hand, are best known for removing halogen atoms from man-made organohalogens, yet also function naturally, albeit rarely, in metabolic pathways. This review details the scope and mechanism of nature's halogenation and dehalogenation enzymatic strategies, highlights gaps in our understanding, and posits where new advances in the field might arise in the near future.
| S-EPMC5575885 | biostudies-literature
Project description:The evolution of enzymes affects how well a species can adapt to new environmental conditions. During enzyme evolution, certain aspects of molecular function are conserved while other aspects can vary. Aspects of function that are more difficult to change or that need to be reused in multiple contexts are often conserved, while those that vary may indicate functions that are more easily changed or that are no longer required. In analogy to the study of conservation patterns in enzyme sequences and structures, we have examined the patterns of conservation and variation in enzyme function by analyzing graph isomorphisms among enzyme substrates of a large number of enzyme superfamilies. This systematic analysis of substrate substructures establishes the conservation patterns that typify individual superfamilies. Specifically, we determined the chemical substructures that are conserved among all known substrates of a superfamily and the substructures that are reacting in these substrates and then examined the relationship between the two. Across the 42 superfamilies that were analyzed, substantial variation was found in how much of the conserved substructure is reacting, suggesting that superfamilies may not be easily grouped into discrete and separable categories. Instead, our results suggest that many superfamilies may need to be treated individually for analyses of evolution, function prediction, and guiding enzyme engineering strategies. Annotating superfamilies with these conserved and reacting substructure patterns provides information that is orthogonal to information provided by studies of conservation in superfamily sequences and structures, thereby improving the precision with which we can predict the functions of enzymes of unknown function and direct studies in enzyme engineering. Because the method is automated, it is suitable for large-scale characterization and comparison of fundamental functional capabilities of both characterized and uncharacterized enzyme superfamilies.
| S-EPMC2453236 | biostudies-literature
Project description:The dramatic increase in heterogeneous types of biological data--in particular, the abundance of new protein sequences--requires fast and user-friendly methods for organizing this information in a way that enables functional inference. The most widely used strategy to link sequence or structure to function, homology-based function prediction, relies on the fundamental assumption that sequence or structural similarity implies functional similarity. New tools that extend this approach are still urgently needed to associate sequence data with biological information in ways that accommodate the real complexity of the problem, while being accessible to experimental as well as computational biologists. To address this, we have examined the application of sequence similarity networks for visualizing functional trends across protein superfamilies from the context of sequence similarity. Using three large groups of homologous proteins of varying types of structural and functional diversity--GPCRs and kinases from humans, and the crotonase superfamily of enzymes--we show that overlaying networks with orthogonal information is a powerful approach for observing functional themes and revealing outliers. In comparison to other primary methods, networks provide both a good representation of group-wise sequence similarity relationships and a strong visual and quantitative correlation with phylogenetic trees, while enabling analysis and visualization of much larger sets of sequences than trees or multiple sequence alignments can easily accommodate. We also define important limitations and caveats in the application of these networks. As a broadly accessible and effective tool for the exploration of protein superfamilies, sequence similarity networks show great potential for generating testable hypotheses about protein structure-function relationships.
| S-EPMC2631154 | biostudies-literature
Project description:Enzymes are categorized into superfamilies by sequence, structural, and mechanistic similarities. The evolutionary implications can be profound. Until the mid-1990s, the approach was fragmented largely due to limited sequence and structural data. However, in 1996, Babbitt et al. published a paper in Biochemistry that demonstrated the potential power of mechanistically diverse superfamilies to identify common ancestry, predict function, and, in some cases, predict specificity. This Perspective describes the findings of the original work and reviews the current understanding of structure and mechanism in the founding family members. The outcomes of the genomic enzymology approach have reached far beyond the functional assignment of members of the enolase superfamily, inspiring the study of superfamilies and the adoption of sequence similarity networks and genome context and yielding fundamental insights into enzyme evolution.
| S-EPMC10251251 | biostudies-literature
Project description:In order to understand the evolution of enzyme reactions and to gain an overview of biological catalysis we have combined sequence and structural data to generate phylogenetic trees in an analysis of 276 structurally defined enzyme superfamilies, and used these to study how enzyme functions have evolved. We describe in detail the analysis of two superfamilies to illustrate different paradigms of enzyme evolution. Gathering together data from all the superfamilies supports and develops the observation that they have all evolved to act on a diverse set of substrates, whilst the evolution of new chemistry is much less common. Despite that, by bringing together so much data, we can provide a comprehensive overview of the most common and rare types of changes in function. Our analysis demonstrates on a larger scale than previously studied, that modifications in overall chemistry still occur, with all possible changes at the primary level of the Enzyme Commission (E.C.) classification observed to a greater or lesser extent. The phylogenetic trees map out the evolutionary route taken within a superfamily, as well as all the possible changes within a superfamily. This has been used to generate a matrix of observed exchanges from one enzyme function to another, revealing the scale and nature of enzyme evolution and that some types of exchanges between and within E.C. classes are more prevalent than others. Surprisingly a large proportion (71%) of all known enzyme functions are performed by this relatively small set of 276 superfamilies. This reinforces the hypothesis that relatively few ancient enzymatic domain superfamilies were progenitors for most of the chemistry required for life.
| S-EPMC3291543 | biostudies-literature
Project description:Due to the rapid release of new data from genome sequencing projects, the majority of protein sequences in public databases have not been experimentally characterized; rather, sequences are annotated using computational analysis. The level of misannotation and the types of misannotation in large public databases are currently unknown and have not been analyzed in depth. We have investigated the misannotation levels for molecular function in four public protein sequence databases (UniProtKB/Swiss-Prot, GenBank NR, UniProtKB/TrEMBL, and KEGG) for a model set of 37 enzyme families for which extensive experimental information is available. The manually curated database Swiss-Prot shows the lowest annotation error levels (close to 0% for most families); the two other protein sequence databases (GenBank NR and TrEMBL) and the protein sequences in the KEGG pathways database exhibit similar and surprisingly high levels of misannotation that average 5%-63% across the six superfamilies studied. For 10 of the 37 families examined, the level of misannotation in one or more of these databases is >80%. Examination of the NR database over time shows that misannotation has increased from 1993 to 2005. The types of misannotation that were found fall into several categories, most associated with "overprediction" of molecular function. These results suggest that misannotation in enzyme superfamilies containing multiple families that catalyze different reactions is a larger problem than has been recognized. Strategies are suggested for addressing some of the systematic problems contributing to these high levels of misannotation.
| S-EPMC2781113 | biostudies-literature
Project description:Predicting protein function from structure remains an active area of interest, particularly for the structural genomics initiatives where a substantial number of structures are initially solved with little or no functional characterisation. Although global structure comparison methods can be used to transfer functional annotations, the relationship between fold and function is complex, particularly in functionally diverse superfamilies that have evolved through different secondary structure embellishments to a common structural core. The majority of prediction algorithms employ local templates built on known or predicted functional residues. Here, we present a novel method (FLORA) that automatically generates structural motifs associated with different functional sub-families (FSGs) within functionally diverse domain superfamilies. Templates are created purely on the basis of their specificity for a given FSG, and the method makes no prior prediction of functional sites, nor assumes specific physico-chemical properties of residues. FLORA is able to accurately discriminate between homologous domains with different functions and substantially outperforms (a 2-3 fold increase in coverage at low error rates) popular structure comparison methods and a leading function prediction method. We benchmark FLORA on a large data set of enzyme superfamilies from all three major protein classes (alpha, beta, alphabeta) and demonstrate the functional relevance of the motifs it identifies. We also provide novel predictions of enzymatic activity for a large number of structures solved by the Protein Structure Initiative. Overall, we show that FLORA is able to effectively detect functionally similar protein domain structures by purely using patterns of structural conservation of all residues.
| S-EPMC2721411 | biostudies-literature