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Rumen bacterial communities in forage-fed and grazing cattle continually adapt to a wide range of changing dietary composition, nutrient density, and environmental conditions. We hypothesized that very distinct community assemblages would develop between the fiber and liquid fractions of rumen contents in animals transitioned from bermudagrass hay diet to a grazed wheat diet. To address this hypothesis, we designed an experiment utilizing a 16S-based bTEFAP pyrosequencing technique to characterize and elucidate changes in bacterial diversity among the fiber and liquid rumen fractions and whole rumen contents of 14 (Angus x Hereford) ruminally cannulated steers sequentially fed bermudagrass hay (Cynodon dactylon; 34 days) and grazing wheat forage (28 days). Bermudagrass hay was a conserved C4 perennial grass lower in protein and higher in fiber (11% and 67%, respectively) content than grazed winter wheat (Triticum aestivum), a C3 annual grass with higher protein (20%) and a large (66%) soluble fraction.Significant differences in the OTU estimates (Chao1, Ace,and Rarefaction) were detected between fractions of both diets, with bermudagrass hay supporting greater diversity than wheat forage. Sequences were compared with a 16S database using BLASTn and assigned sequences to respective genera and genera-like units based on the similarity value to known sequences in the database. Predominant genera were Prevotella (up to 33%) and Rikenella-like (upto 28%) genera on the bermudagrass diet and Prevotella (upto 56%) genus on the wheat diet irrespective of the fractions. Principle component analyses accounted for over 95% of variation in 16S estimated bacterial community composition in all three fractions and clearly differentiated communities associated with each diet. Overall, bermudagrass hay diets clustered more clearly than wheat diets.These data are the first to explore bacterial diversity dynamics in a common population of animals in response to contrasting grass forage diets.

Structural genomics projects are determining the three-dimensional structure of proteins without full characterization of their function. A critical part of the annotation process involves appropriate knowledge representation and prediction of functionally important residue environments. We have developed a method to extract features from sequence, sequence alignments, three-dimensional structure, and structural environment conservation, and used support vector machines to annotate homologous and nonhomologous residue positions based on a specific training set of residue functions. In order to evaluate this pipeline for automated protein annotation, we applied it to the challenging problem of prediction of catalytic residues in enzymes. We also ranked the features based on their ability to discriminate catalytic from noncatalytic residues. When applying our method to a wellannotated set of protein structures, we found that top-ranked features were a measure of sequence conservation, a measure of structural conservation, a degree of uniqueness of a residue's structural environment, solvent accessibility, and residue hydrophobicity. We also found that features based on structural conservation were complementary to those based on sequence conservation and that they were capable of increasing predictor performance. Using a family nonredundant version of the ASTRAL 40 v1.65 data set, we estimated that the true catalytic residues were correctly predicted in 57.0% of the cases, with a precision of 18.5%. When testing on proteins containing novel folds not used in training, the best features were highly correlated with the training on families, thus validating the approach to nonhomologous catalytic residue prediction in general. We then applied the method to 2781 coordinate files from the structural genomics target pipeline and identified both highly ranked and highly clustered groups of predicted catalytic residues.

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