AnEnPi: identification and annotation of analogous enzymes

Otto, Thomas D.; Guimarães, Ana Carolina Ramos; Degrave, Wim; Miranda, Antônio Basílio de

doi:10.1186/1471-2105-9-544

Cited by 27 publications

(36 citation statements)

References 24 publications

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“…In the years elapsed since our 1998 analysis, several studies explored various groups of alternative enzymes catalyzing the same biochemical reaction, including those belonging to the same protein (super)families [13,14]. Here, we focus on enzyme variants that possess (or could be inferred to possess) different structural folds and therefore appear to be evolutionarily unrelated (bona fide NISE).…”

Section: Introductionmentioning

confidence: 99%

Non-homologous isofunctional enzymes: A systematic analysis of alternative solutions in enzyme evolution

et al. 2010

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BackgroundEvolutionarily unrelated proteins that catalyze the same biochemical reactions are often referred to as analogous - as opposed to homologous - enzymes. The existence of numerous alternative, non-homologous enzyme isoforms presents an interesting evolutionary problem; it also complicates genome-based reconstruction of the metabolic pathways in a variety of organisms. In 1998, a systematic search for analogous enzymes resulted in the identification of 105 Enzyme Commission (EC) numbers that included two or more proteins without detectable sequence similarity to each other, including 34 EC nodes where proteins were known (or predicted) to have distinct structural folds, indicating independent evolutionary origins. In the past 12 years, many putative non-homologous isofunctional enzymes were identified in newly sequenced genomes. In addition, efforts in structural genomics resulted in a vastly improved structural coverage of proteomes, providing for definitive assessment of (non)homologous relationships between proteins.ResultsWe report the results of a comprehensive search for non-homologous isofunctional enzymes (NISE) that yielded 185 EC nodes with two or more experimentally characterized - or predicted - structurally unrelated proteins. Of these NISE sets, only 74 were from the original 1998 list. Structural assignments of the NISE show over-representation of proteins with the TIM barrel fold and the nucleotide-binding Rossmann fold. From the functional perspective, the set of NISE is enriched in hydrolases, particularly carbohydrate hydrolases, and in enzymes involved in defense against oxidative stress.ConclusionsThese results indicate that at least some of the non-homologous isofunctional enzymes were recruited relatively recently from enzyme families that are active against related substrates and are sufficiently flexible to accommodate changes in substrate specificity.ReviewersThis article was reviewed by Andrei Osterman, Keith F. Tipton (nominated by Martijn Huynen) and Igor B. Zhulin. For the full reviews, go to the Reviewers' comments section.

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Section: Introductionmentioning

confidence: 99%

Non-homologous isofunctional enzymes: A systematic analysis of alternative solutions in enzyme evolution

et al. 2010

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“…Similarly, by combining sequence similarity with other functional features such as interacting partners, Espadaler et al [7] have shown that the protein sequences with sequence similarity are more likely to exhibit the same enzymatic activity if they share the same interacting partners. Otto et al [16] and Galperin et al [17] have developed methods for identification of analogous enzymes using sequence similarity by grouping proteins that share the same enzymatic activity (EC classes).…”

Section: Methods By Feature Spacementioning

confidence: 99%

Computational Approaches for Automated Classification of Enzyme Sequences

Mohammed

Guda

2011

JPB

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Determining the functional role(s) of enzymes is very important to build the metabolic blueprint of an organism and to identify the potential roles enzymes may play in metabolic and disease pathways. With exponential growth in gene and protein sequence data, it is not feasible to experimentally characterize the function(s) of all enzymes. Alternatively, computational methods can be used to annotate the enormous amount of unannotated enzyme sequences. For function prediction and classification of enzymes, features based on amino acid composition, sequence and structural properties, domain composition and specific peptide information have been widely used by different computational approaches. Each feature space has its own merits and limitations on the overall prediction accuracy. Prediction accuracy improves when machine-learning methods are used to classify enzymes. Given the incomplete and unbalanced nature of annotations in biological databases, ensemble methods or methods that bank on a combination of orthogonal feature are more desirable for achieving higher accuracy and coverage in enzyme classification. In this review article, we systematically describe all the features and methods used thus far for enzyme class prediction. To the authors’ knowledge, this review represents the most exhaustive description of methods used for computational prediction of enzyme classes.

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“…Several studies have suggested that convergent evolution is also widespread [5,20,21] and that some non-homologous enzymes share structurally and functionally equivalent active site residues and similar mechanisms [20,22]. Recently, a quantitative assessment of convergently evolved enzymes revealed that of those enzymes that have similar overall reactions one third also had similar catalytic mechanisms [23••], concluding that functional analogs, like homologs, often share common mechanistic aspects along their reaction pathways.…”

Section: Lessons Learned From Studying the Evolution Of Enzyme Functionsmentioning

confidence: 99%

Toward mechanistic classification of enzyme functions

Almonacid

Babbitt

2011

Current Opinion in Chemical Biology

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Classification of enzyme function should be quantitative, computationally accessible, and informed by sequences and structures to enable use of genomic information for functional inference and other applications. Large-scale studies have established that divergently evolved enzymes share conserved elements of structure and common mechanistic steps and that convergently evolved enzymes often converge to similar mechanisms too, suggesting that reaction mechanisms could be used to develop finer-grained functional descriptions than provided by the Enzyme Commission (EC) system currently in use. Here we describe how evolution informs these structure-function mappings and review the databases that store mechanisms of enzyme reactions along with recent developments to measure ligand and mechanistic similarities. Together, these provide a foundation for new classifications of enzyme function.

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AnEnPi: identification and annotation of analogous enzymes

Cited by 27 publications

References 24 publications

Non-homologous isofunctional enzymes: A systematic analysis of alternative solutions in enzyme evolution

Non-homologous isofunctional enzymes: A systematic analysis of alternative solutions in enzyme evolution

Computational Approaches for Automated Classification of Enzyme Sequences

Toward mechanistic classification of enzyme functions

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