ArticleBMC bioinformatics2012
Automated hierarchical classification of protein domain subfamilies based on functionally-divergent residue signatures.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Article
- Inference of Functionally-Relevant N-acetyltransferase Residues Based on Statistical Correlations.PLoS computational biology · 2016Article
- Hierarchical classification strategy for Phenotype extraction from epidermal growth factor receptor endocytosis screening.BMC bioinformatics · 2016Article
- Bayesian Top-Down Protein Sequence Alignment with Inferred Position-Specific Gap Penalties.PLoS computational biology · 2016Article
- Evaluating, comparing, and interpreting protein domain hierarchies.Journal of computational biology : a journal of computational molecular cell biology · 2014Article
- A Bayesian sampler for optimization of protein domain hierarchies.Journal of computational biology : a journal of computational molecular cell biology · 2014Article
- CDD: conserved domains and protein three-dimensional structure.Nucleic acids research · 2013Article
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3 authors.
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Abstract
backgroundThe NCBI Conserved Domain Database (CDD) consists of a collection of multiple sequence alignments of protein domains that are at various stages of being manually curated into evolutionary hierarchies based on conserved and divergent sequence and structural features. These domain models are annotated to provide insights into the relationships between sequence, structure and function via web-based BLAST searches.
resultsHere we automate the generation of conserved domain (CD) hierarchies using a combination of heuristic and Markov chain Monte Carlo (MCMC) sampling procedures and starting from a (typically very large) multiple sequence alignment. This procedure relies on statistical criteria to define each hierarchy based on the conserved and divergent sequence patterns associated with protein functional-specialization. At the same time this facilitates the sequence and structural annotation of residues that are functionally important. These statistical criteria also provide a means to objectively assess the quality of CD hierarchies, a non-trivial task considering that the protein subgroups are often very distantly related--a situation in which standard phylogenetic methods can be unreliable. Our aim here is to automatically generate (typically sub-optimal) hierarchies that, based on statistical criteria and visual comparisons, are comparable to manually curated hierarchies; this serves as the first step toward the ultimate goal of obtaining optimal hierarchical classifications. A plot of runtimes for the most time-intensive (non-parallelizable) part of the algorithm indicates a nearly linear time complexity so that, even for the extremely large Rossmann fold protein class, results were obtained in about a day.
conclusionsThis approach automates the rapid creation of protein domain hierarchies and thus will eliminate one of the most time consuming aspects of conserved domain database curation. At the same time, it also facilitates protein domain annotation by identifying those pattern residues that most distinguish each protein domain subgroup from other related subgroups.
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