Coenzymes as coribozymes

Coenzymes are small organic molecules that supply a varied set of reactive groups to protein enzymes, thereby diversifying catalysis beyond the chemistries of amino acid sidechains. As RNA structures begin with a more limited chemical diversity than proteins, it seems likely that RNA enzymes would also use functional groups from other molecules to support a complex RNA world metabolism. In fact, ribonucleotide moieties in many coenzymes have long been thought to be surviving vestiges of covalently bound coenzymes in an RNA world. The idea of coenzyme utilization by ribozymes can be explored by selection-amplification of coenzyme-binding RNAs and coenzyme-assisted ribozymes. Here, we review coenzyme-RNAs, and discuss their possible significance for RNA-mediated metabolism. In summary, a plausible route from prebiotic chemistry to ribozyme biochemistry exists for CoA, and via similar activities, likely exists for all the nucleotidyl coenzymes.     

A two-stage classifier for identification of protein-protein interface residues

MOTIVATION: The ability to identify protein-protein interaction sites and to detect specific amino acid residues that contribute to the specificity and affinity of protein interactions has important implications for problems ranging from rational drug design to analysis of metabolic and signal transduction networks. RESULTS: We have developed a two-stage method consisting of a support vector machine (SVM) and a Bayesian classifier for predicting surface residues of a protein that participate in protein-protein interactions. This approach exploits the fact that interface residues tend to form clusters in the primary amino acid sequence. Our results show that the proposed two-stage classifier outperforms previously published sequence-based methods for predicting interface residues. We also present results obtained using the two-stage classifier on an independent test set of seven CAPRI (Critical Assessment of PRedicted Interactions) targets. The success of the predictions is validated by examining the predictions in the context of the three-dimensional structures of protein complexes.     

Bex,the Bacillus subtilis homolog of the essential Escherichia coli GTPase Era,is required for normal cell division and spore formation

The Bacillus subtilis bex gene complemented the defect in an Escherichia coli era mutant. The Bex protein showed 39 percent identity and 67 percent similarity to the E. coli Era GTPase. In contrast to era, bex was not essential in all strains. bex mutant cells were elongated and filled with diffuse nucleoid material. They grew slowly and exhibited severely impaired spore formation.     

Effect of prostaglandins on the in vitro steroidogenesis in human ovarian tissues

Although prostaglandins are luteolytic in some species, in $\text{in vitro}$ conditions they stimulate progesterone production in the corpus luteum (1). Apart from this effect prostaglandins may also stimulate other steps in the steroidogenic sequence e.g. corticosteroidogenesis in superfused rat adrenal glands (2) and aromatization of testosterone by perfused human placenta (3). With this possibility in view and also because of paucity of data on the effect of prostaglandins on steroidogenesis in human ovarian tissues we have been studying under $\text{in vitro}$ conditions the effect of prostaglandins on progesterone formation in human corpora lutea and on the utilization of C₂₁ steroids by the luteal and follicular compartments of the ovary. These studies are still in progress. However, the data obtained so far indicates that in addition to stimulating progesterone synthesis in the corpus luteum prostaglandins may also affect other steps in steroidogenesis in human ovarian tissues. We wish to report here in brief these preliminary results.     

Environmental Response and Genomic Regions Correlated with Rice Root Growth and Yield under Drought in the OryzaSNP Panel across Multiple Study Systems

The rapid progress in rice genotyping must be matched by advances in phenotyping. A better understanding of genetic variation in rice for drought response, root traits, and practical methods for studying them are needed. In this study, the OryzaSNP set (20 diverse genotypes that have been genotyped for SNP markers) was phenotyped in a range of field and container studies to study the diversity of rice root growth and response to drought. Of the root traits measured across more than 20 root experiments, root dry weight showed the most stable genotypic performance across studies. The environment (E) component had the strongest effect on yield and root traits. We identified genomic regions correlated with root dry weight, percent deep roots, maximum root depth, and grain yield based on a correlation analysis with the phenotypes and aus, indica, or japonica introgression regions using the SNP data. Two genomic regions were identified as hot spots in which root traits and grain yield were co-located; on chromosome 1 (39.7–40.7 Mb) and on chromosome 8 (20.3–21.9 Mb). Across experiments, the soil type/ growth medium showed more correlations with plant growth than the container dimensions. Although the correlations among studies and genetic co-location of root traits from a range of study systems points to their potential utility to represent responses in field studies, the best correlations were observed when the two setups had some similar properties. Due to the co-location of the identified genomic regions (from introgression block analysis) with QTL for a number of previously reported root and drought traits, these regions are good candidates for detailed characterization to contribute to understanding rice improvement for response to drought. This study also highlights the utility of characterizing a small set of 20 genotypes for root growth, drought response, and related genomic regions.     

Predicting the Binding Patterns of Hub Proteins: A Study Using Yeast Protein Interaction Networks

Background

Protein-protein interactions are critical to elucidating the role played by individual proteins in important biological pathways. Of particular interest are hub proteins that can interact with large numbers of partners and often play essential roles in cellular control. Depending on the number of binding sites, protein hubs can be classified at a structural level as singlish-interface hubs (SIH) with one or two binding sites, or multiple-interface hubs (MIH) with three or more binding sites. In terms of kinetics, hub proteins can be classified as date hubs (i.e., interact with different partners at different times or locations) or party hubs (i.e., simultaneously interact with multiple partners).

Methodology

Our approach works in 3 phases: Phase I classifies if a protein is likely to bind with another protein. Phase II determines if a protein-binding (PB) protein is a hub. Phase III classifies PB proteins as singlish-interface versus multiple-interface hubs and date versus party hubs. At each stage, we use sequence-based predictors trained using several standard machine learning techniques.

Conclusions

Our method is able to predict whether a protein is a protein-binding protein with an accuracy of 94% and a correlation coefficient of 0.87; identify hubs from non-hubs with 100% accuracy for 30% of the data; distinguish date hubs/party hubs with 69% accuracy and area under ROC curve of 0.68; and SIH/MIH with 89% accuracy and area under ROC curve of 0.84. Because our method is based on sequence information alone, it can be used even in settings where reliable protein-protein interaction data or structures of protein-protein complexes are unavailable to obtain useful insights into the functional and evolutionary characteristics of proteins and their interactions.

Availability

We provide a web server for our three-phase approach: http://hybsvm.gdcb.iastate.edu.