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Background: Classification is difficult for shotgun metagenomics data from environments such as soils, where the diversity of sequences is high and where reference sequences from close relatives may not exist. Approaches based on sequence-similarity scores must deal with the confounding effects that inheritance and functional pressures exert on the relation between scores and phylogenetic distance, while approaches based on sequence alignment and tree-building are typically limited to a small fraction of gene families. We describe an approach based on finding one or more exact matches between a read and a precomputed set of peptide 10-mers. Results: At even the largest phylogenetic distances, thousands of 10-mer peptide exact matches can be found between pairs of bacterial genomes. Genes that share one or more peptide 10-mers typically have high reciprocal BLAST scores. Among a set of 403 representative bacterial genomes, some 20 million 10-mer peptides were found to be shared. We assign each of these peptides as a signature of a particular node in a phylogenetic reference tree based on the RNA polymerase genes. We classify the phylogeny of a genomic fragment (e.g., read) at the most specific node on the reference tree that is consistent with the phylogeny of observed signature peptides it contains. Using both synthetic data from four newly-sequenced soil-bacterium genomes and ten real soil metagenomics data sets, we demonstrate a sensitivity and specificity comparable to that of the MEGAN metagenomics analysis package using BLASTX against the NR database. Phylogenetic and functional similarity metrics applied to real metagenomics data indicates a signal-to-noise ratio of approximately 400 for distinguishing among environments. Our method assigns ~6.6 Gbp/hr on a single CPU, compared with 25 kbp/hr for methods based on BLASTX against the NR database. Conclusions: Classification by exact matching against a precomputed list of signature peptides provides comparable results to existing techniques for reads longer than about 300 bp and does not degrade severely with shorter reads. Orders of magnitude faster than existing methods, the approach is suitable now for inclusion in analysis pipelines and appears to be extensible in several different directions. [ABSTRACT FROM AUTHOR]

Background: There exist > 78,000 proteins and/or nucleic acids structures that were determined experimentally. Only a small portion of these structures corresponds to those of protein complexes. While homology modeling is able to exploit knowledge-based potentials of side-chain rotomers and backbone motifs to infer structures for new proteins, no such general method exists to extend our understanding of protein interaction motifs to novel protein complexes., Results: We use a Motif Binding Geometries (MBG) approach, to infer the structure of a protein complex from the database of complexes of homologous proteins taken from other contexts (such as the helix-turn-helix motif binding double stranded DNA), and demonstrate its utility on one of the more important regulatory complexes in biology, that of the RNA polymerase initiating transcription under conditions of phosphate starvation. The modeled PhoB/RNAP/σ-factor/DNA complex is stereo-chemically reasonable, has sufficient interfacial Solvent Excluded Surface Areas (SESAs) to provide adequate binding strength, is physically meaningful for transcription regulation, and is consistent with a variety of known experimental constraints., Conclusions: Based on a straightforward and easy to comprehend concept, "proteins and protein domains that fold similarly could interact similarly", a structural model of the PhoB dimer in the transcription initiation complex has been developed. This approach could be extended to enable structural modeling and prediction of other bio-molecular complexes. Just as models of individual proteins provide insight into molecular recognition, catalytic mechanism, and substrate specificity, models of protein complexes will provide understanding into the combinatorial rules of cellular regulation and signaling.

For the past decade, the Food and Agriculture Organization of the United Nations has been working toward eradicating rinderpest through vaccination and intense surveillance by 2012. Because of the potential severity of a rinderpest epidemic, it is prudent to prepare for an unexpected outbreak in animal populations. There is no immunity to the disease among the livestock or wildlife in the United States (US). If rinderpest were to emerge in the US, the loss in livestock could be devastating. We predict the potential spread of rinderpest using a two-stage model for the spread of a multi-host infectious disease among agricultural animals in the US. The model incorporates large-scale interactions among US counties and the small-scale dynamics of disease spread within a county. The model epidemic was seeded in 16 locations and there was a strong dependence of the overall epidemic size on the starting location. The epidemics were classified according to overall size into small epidemics of 100 to 300 animals (failed epidemics), epidemics infecting 3,000 to 30,000 animals (medium epidemics), and the large epidemics infecting around one million beef cattle. The size of the rinderpest epidemics were directly related to the origin of the disease and whether or not the disease moved into certain key counties in high-livestock-density areas of the US. The epidemic size also depended upon response time and effectiveness of movement controls.