The WD-repeat protein superfamily in Arabidopsis: conservation and divergence in structure and function

BACKGROUND: The WD motif (also known as the Trp-Asp or WD40 motif) is found in a multitude of eukaryotic proteins involved in a variety of cellular processes. Where studied, repeated WD motifs act as a site for protein-protein interaction, and proteins containing WD repeats (WDRs) are known to serve as platforms for the assembly of protein complexes or mediators of transient interplay among other proteins. In the model plant Arabidopsis thaliana, members of this superfamily are increasingly being recognized as key regulators of plant-specific developmental events. RESULTS: We analyzed the predicted complement of WDR proteins from Arabidopsis, and compared this to those from budding yeast, fruit fly and human to illustrate both conservation and divergence in structure and function. This analysis identified 237 potential Arabidopsis proteins containing four or more recognizable copies of the motif. These were classified into 143 distinct families, 49 of which contained more than one Arabidopsis member. Approximately 113 of these families or individual proteins showed clear homology with WDR proteins from the other eukaryotes analyzed. Where conservation was found, it often extended across all of these organisms, suggesting that many of these proteins are linked to basic cellular mechanisms. The functional characterization of conserved WDR proteins in Arabidopsis reveals that these proteins help adapt basic mechanisms for plant-specific processes. CONCLUSIONS: Our results show that most Arabidopsis WDR proteins are strongly conserved across eukaryotes, including those that have been found to play key roles in plant-specific processes, with diversity in function conferred at least in part by divergence in upstream signaling pathways, downstream regulatory targets and /or structure outside of the WDR regions

NBLAST: a cluster variant of BLAST for NxN comparisons

The BLAST algorithm compares biological sequences to one another in order to determine shared motifs and common ancestry. However, the comparison of all non-redundant (NR) sequences against all other NR sequences is a computationally intensive task. We developed NBLAST as a cluster computer implementation of the BLAST family of sequence comparison programs for the purpose of generating pre-computed BLAST alignments and neighbour lists of NR sequences.NBLAST performs the heuristic BLAST algorithm and generates an exhaustive database of alignments, but it only computes alignments (i.e. the upper triangle) of a possible N2 alignments, where N is the set of all sequences to be compared. A task-partitioning algorithm allows for cluster computing across all cluster nodes and the NBLAST master process produces a BLAST sequence alignment database and a list of sequence neighbours for each sequence record. The resulting sequence alignment and neighbour databases are used to serve the SeqHound query system through a C/C++ and PERL Application Programming Interface (API).NBLAST offers a local alternative to the NCBI's remote Entrez system for pre-computed BLAST alignments and neighbour queries. On our 216-processor 450 MHz PIII cluster, NBLAST requires ~24 hrs to compute neighbours for 850000 proteins currently in the non-redundant protein database

Species-specific protein sequence and fold optimizations

An organism's ability to adapt to its particular environmental niche is of fundamental importance to its survival and proliferation. In the largest study of its kind, we sought to identify and exploit the amino-acid signatures that make species-specific protein adaptation possible across 100 complete genomes.Environmental niche was determined to be a significant factor in variability from correspondence analysis using the amino acid composition of over 360,000 predicted open reading frames (ORFs) from 17 archaea, 76 bacteria and 7 eukaryote complete genomes. Additionally, we found clusters of phylogenetically unrelated archaea and bacteria that share similar environments by amino acid composition clustering. Composition analyses of conservative, domain-based homology modeling suggested an enrichment of small hydrophobic residues Ala, Gly, Val and charged residues Asp, Glu, His and Arg across all genomes. However, larger aromatic residues Phe, Trp and Tyr are reduced in folds, and these results were not affected by low complexity biases. We derived two simple log-odds scoring functions from ORFs (CG) and folds (CF) for each of the complete genomes. CF achieved an average cross-validation success rate of 85 +/- 8% whereas the CG detected 73 +/- 9% species-specific sequences when competing against all other non-redundant CG. Continuously updated results are available at http://genome.mshri.on.ca.Our analysis of amino acid compositions from the complete genomes provides stronger evidence for species-specific and environmental residue preferences in genomic sequences as well as in folds. Scoring functions derived from this work will be useful in future protein engineering experiments and possibly in identifying horizontal transfer events

Hardware-accelerated protein identification for mass spectrometry

An ongoing issue in mass spectrometry is the time it takes to search DNA sequences with MS/MS peptide fragments (see, e.g., Choudary et al., Proteomics 2001; 1: 651-667.) Search times are far longer than spectra acquisition time, and parallelization of search software on clusters requires doubling the size of a conventional computing cluster to cut the search time in half. Field programmable gate arrays (FPGAs) are used to create hardware-accelerated algorithms that reduce operating costs and improve search speed compared to large clusters. We present a novel hardware design that takes full spectra and computes 6-frame translation word searches on DNA databases at a rate of approximately 3 billion base pairs per second, with queries of up to 10 amino acids in length and arbitrary wildcard positions. Hardware post-processing identifies in silico tryptic peptides and scores them using a variety of techniques including mass frequency expected values. With faster FPGAs protein identifications from the human genome can be achieved in less than a second, and this makes it an ideal solution for a number of proteome-scale applications

Armadillo: Domain boundary prediction by amino acid composition

The identification and annotation of protein domains provides a critical step in the accurate determination of molecular function. Both computational and experimental methods of protein structure determination may be deterred by large multi-domain proteins or flexible linker regions. Knowledge of domains and their boundaries may reduce the experimental cost of protein structure determination by allowing researchers to work on a set of smaller and possibly more successful alternatives. Current domain prediction methods often rely on sequence similarity to conserved domains and as such are poorly suited to detect domain structure in poorly conserved or orphan proteins. We present here a simple computational method to identify protein domain linkers and their boundaries from sequence information alone. Our domain predictor, Armadillo (http://armadillo.blueprint.org), uses any amino acid index to convert a protein sequence to a smoothed numeric profile from which domains and domain boundaries may be predicted. We derived an amino acid index called the domain linker propensity index (DLI) from the amino acid composition of domain linkers using a non-redundant structure dataset. The index indicates that Pro and Gly show a propensity for linker residues while small hydrophobic residues do not. Armadillo predicts domain linker boundaries from Z-score distributions and obtains 35% sensitivity with DLI in a two-domain, single-linker dataset (within +/-20 residues from linker). The combination of DLI and an entropy-based amino acid index increases the overall Armadillo sensitivity to 56% for two domain proteins. Moreover, Armadillo achieves 37% sensitivity for multi-domain proteins, surpassing most other prediction methods. Armadillo provides a simple, but effective method by which prediction of domain boundaries can be obtained with reasonable sensitivity. Armadillo should prove to be a valuable tool for rapidly delineating protein domains in poorly conserved proteins or those with no sequence neighbors. As a first-line predictor, domain meta-predictors could yield improved results with Armadillo predictions

Hardware-accelerated protein identification for mass spectrometry

An ongoing issue in mass spectrometry is the time it takes to search DNA sequences with MS/MS peptide fragments (see, e.g., Choudary et al., Proteomics 2001; 1: 651-667.) Search times are far longer than spectra acquisition time, and parallelization of search software on clusters requires doubling the size of a conventional computing cluster to cut the search time in half. Field programmable gate arrays (FPGAs) are used to create hardware-accelerated algorithms that reduce operating costs and improve search speed compared to large clusters. We present a novel hardware design that takes full spectra and computes 6-frame translation word searches on DNA databases at a rate of approximately 3 billion base pairs per second, with queries of up to 10 amino acids in length and arbitrary wildcard positions. Hardware post-processing identifies in silico tryptic peptides and scores them using a variety of techniques including mass frequency expected values. With faster FPGAs protein identifications from the human genome can be achieved in less than a second, and this makes it an ideal solution for a number of proteome-scale applications

Armadillo: Domain boundary prediction by amino acid composition

The identification and annotation of protein domains provides a critical step in the accurate determination of molecular function. Both computational and experimental methods of protein structure determination may be deterred by large multi-domain proteins or flexible linker regions. Knowledge of domains and their boundaries may reduce the experimental cost of protein structure determination by allowing researchers to work on a set of smaller and possibly more successful alternatives. Current domain prediction methods often rely on sequence similarity to conserved domains and as such are poorly suited to detect domain structure in poorly conserved or orphan proteins. We present here a simple computational method to identify protein domain linkers and their boundaries from sequence information alone. Our domain predictor, Armadillo (http://armadillo.blueprint.org), uses any amino acid index to convert a protein sequence to a smoothed numeric profile from which domains and domain boundaries may be predicted. We derived an amino acid index called the domain linker propensity index (DLI) from the amino acid composition of domain linkers using a non-redundant structure dataset. The index indicates that Pro and Gly show a propensity for linker residues while small hydrophobic residues do not. Armadillo predicts domain linker boundaries from Z-score distributions and obtains 35% sensitivity with DLI in a two-domain, single-linker dataset (within +/-20 residues from linker). The combination of DLI and an entropy-based amino acid index increases the overall Armadillo sensitivity to 56% for two domain proteins. Moreover, Armadillo achieves 37% sensitivity for multi-domain proteins, surpassing most other prediction methods. Armadillo provides a simple, but effective method by which prediction of domain boundaries can be obtained with reasonable sensitivity. Armadillo should prove to be a valuable tool for rapidly delineating protein domains in poorly conserved proteins or those with no sequence neighbors. As a first-line predictor, domain meta-predictors could yield improved results with Armadillo predictions

CO: A chemical ontology for identification of functional groups and semantic comparison of small molecules

A novel chemical ontology based on chemical functional groups automatically, objectively assigned by a computer program, was developed to categorize small molecules. It has been applied to PubChem and the small molecule interaction database to demonstrate its utility as a basic pharmacophore search system. Molecules can be compared using a semantic similarity score based on functional group assignments rather than 3D shape, which succeeds in identifying small molecules known to bind a common binding site. This ontology will serve as a powerful tool for searching chemical databases and identifying key functional groups responsible for biological activities

CO: A chemical ontology for identification of functional groups and semantic comparison of small molecules

A novel chemical ontology based on chemical functional groups automatically, objectively assigned by a computer program, was developed to categorize small molecules. It has been applied to PubChem and the small molecule interaction database to demonstrate its utility as a basic pharmacophore search system. Molecules can be compared using a semantic similarity score based on functional group assignments rather than 3D shape, which succeeds in identifying small molecules known to bind a common binding site. This ontology will serve as a powerful tool for searching chemical databases and identifying key functional groups responsible for biological activities