CORRIE: enzyme sequence annotation with confidence estimates

Audit, Benjamin; Levy, Emmanuel D.; Gilks, Walter R.; Goldovsky, Leon; Ouzounis, Christos A.

doi:10.1186/1471-2105-8-s4-s3

Cited by 13 publications

(9 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…BLAST scores were used as the edge metric for full sequence‐based networks, and TM‐Align scores as the edge metric for structure‐based networks. Both of these methods have previously been used as metrics for sequence and structural comparison, respectively. For active site comparison, we used active site profiling, a method previously developed to identify and quantitatively compare active site microenvironments .…”

Section: Introductionmentioning

confidence: 99%

Comparison of topological clustering within protein networks using edge metrics that evaluate full sequence, full structure, and active site microenvironment similarity

et al. 2015

View full text Add to dashboard Cite

The development of accurate protein function annotation methods has emerged as a major unsolved biological problem. Protein similarity networks, one approach to function annotation via annotation transfer, group proteins into similarity-based clusters. An underlying assumption is that the edge metric used to identify such clusters correlates with functional information. In this contribution, this assumption is evaluated by observing topologies in similarity networks using three different edge metrics: sequence (BLAST), structure (TM-Align), and active site similarity (active site profiling, implemented in DASP). Network topologies for four well-studied protein superfamilies (enolase, peroxiredoxin (Prx), glutathione transferase (GST), and crotonase) were compared with curated functional hierarchies and structure. As expected, network topology differs, depending on edge metric; comparison of topologies provides valuable information on structure/function relationships. Subnetworks based on active site similarity correlate with known functional hierarchies at a single edge threshold more often than sequence- or structure-based networks. Sequence- and structure-based networks are useful for identifying sequence and domain similarities and differences; therefore, it is important to consider the clustering goal before deciding appropriate edge metric. Further, conserved active site residues identified in enolase and GST active site subnetworks correspond with published functionally important residues. Extension of this analysis yields predictions of functionally determinant residues for GST subgroups. These results support the hypothesis that active site similarity-based networks reveal clusters that share functional details and lay the foundation for capturing functionally relevant hierarchies using an approach that is both automatable and can deliver greater precision in function annotation than current similarity-based methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Comparison of topological clustering within protein networks using edge metrics that evaluate full sequence, full structure, and active site microenvironment similarity

et al. 2015

View full text Add to dashboard Cite

show abstract

“…In one such effort, a novel probabilistic method was proposed23, 24 to improve catalytic function predictions based on BLAST searches of a database of annotated enzymes. For a query enzyme, the method takes into account all BLAST search results and employs Bayesian statistics to determine the most probable EC number for the query enzyme.…”

Section: Introductionmentioning

confidence: 99%

Genome‐wide enzyme annotation with precision control: Catalytic families (CatFam) databases

Zavaljevski

Desai

et al. 2008

Proteins

View full text Add to dashboard Cite

In this article, we present a new method termed CatFam (Catalytic Families) to automatically infer the functions of catalytic proteins, which account for 20-40% of all proteins in living organisms and play a critical role in a variety of biological processes. CatFam is a sequence-based method that generates sequence profiles to represent and infer protein catalytic functions. CatFam generates profiles through a stepwise procedure that carefully controls profile quality and employs nonenzymes as negative samples to establish profile-specific thresholds associated with a predefined nominal false-positive rate (FPR) of predictions. The adjustable FPR allows for fine precision control of each profile and enables the generation of profile databases that meet different needs: function annotation with high precision and hypothesis generation with moderate precision but better recall. Multiple tests of CatFam databases (generated with distinct nominal FPRs) against enzyme and nonenzyme datasets show that the method's predictions have consistently high precision and recall. For example, a 1% FPR database predicts protein catalytic functions for a dataset of enzymes and nonenzymes with 98.6% precision and 95.0% recall. Comparisons of CatFam databases against other established profile-based methods for the functional annotation of 13 bacterial genomes indicate that CatFam consistently achieves higher precision and (in most cases) higher recall, and that (on average) CatFam provides 21.9% additional catalytic functions not inferred by the other similarly reliable methods. These results strongly suggest that the proposed method provides a valuable contribution to the automated prediction of protein catalytic functions. The CatFam databases and the database search program are freely available at http://www.bhsai.org/downloads/catfam.tar.gz.

show abstract

“…Some studies [2,14] suggest that homology-based tools are sufficient to determine the most probable EC number for the query sequence, but less coverage is achieved with these methods. However, simple pair-wise comparisons may be misleading due to the availability of redundant protein sequences in public databases [15].…”

Section: Methods By Feature Spacementioning

confidence: 99%

Computational Approaches for Automated Classification of Enzyme Sequences

Mohammed

Guda

2011

JPB

View full text Add to dashboard Cite

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.

show abstract

CORRIE: enzyme sequence annotation with confidence estimates

Cited by 13 publications

References 25 publications

Comparison of topological clustering within protein networks using edge metrics that evaluate full sequence, full structure, and active site microenvironment similarity

Comparison of topological clustering within protein networks using edge metrics that evaluate full sequence, full structure, and active site microenvironment similarity

Genome‐wide enzyme annotation with precision control: Catalytic families (CatFam) databases

Computational Approaches for Automated Classification of Enzyme Sequences

Contact Info

Product

Resources

About