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.
Highlights
The National Center for Biotechnology Information (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
In order to provide rapid and sensitive annotation for protein sequences, including direct links to structural and functional information, the National Center for Biotechnology Information (NCBI) initiated the Conserved Domain Database (CDD) [1] —a collection of positionspecific scoring matrices (PSSMs) that are derived from protein multiple sequence alignments
Results and discussion we lay out the basic automated mcBPPS (amcBPPS) algorithm, illustrate an implementation of the algorithm as applied to P-loop GTPases, compare its performance against various manually-curated conserved domain (CD) hierarchies, further evaluate its performance using both delete-half jackknifing and simulations, and apply it to several large protein classes for which existing hierarchies or alignments are currently unavailable
Summary
The 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. Our focus here is to obtain heuristically an initial (presumably sub-optimal) CD hierarchy starting from a typically very large multiple sequence alignment for an entire protein class whose domain boundaries remain fixed To do this we utilize procedures that obtain both subgroup assignments for aligned sequences and corresponding discriminating patterns associated with protein functional-divergence
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