Phylogenetic web profiler

SUMMARY: Phylogenetic Web Profiler (PWP) is a web-based service designed to perform phylogenetic profiling of proteins against genomes. The current version offers a selection of 63 completed genomes and available plasmids as annotated in the PEDANT genome database. Unlike currently available applications, this tool offers several choices of ortholog prediction parameters including E-value cutoff, percent length difference tolerance, and annotation similarity. Additional features include tight integration with the PEDANT database and tools to analyze properties of predicted proteins. PWP should prove very useful for the analysis of functional-linkage between proteins.     

What we have learned about prokaryotes from structural genomics

Five years ago systematic determination and theoretical analysis of all protein structures encoded in model prokaryotic organisms was proposed as a powerful way to obtain new insights into protein function and the variety of protein folds. What has been the pay-off from studying structures in genomic context? Have we learned anything new about protein structure? Can we now predict protein function better? In this contribution, I summarize the status of large-scale structure determination projects on prokaryotes and provide an overview of the main results obtained from experimental and theoretical studies in this dynamic research field.     

Paradoxical effect of methyl mercury on mitochondrial protein synthesis in mouse brain tissue

The intraperitoneal administration of a single dose of methyl mercuric chloride (MeHg) (10 or 50 nmol/g body weight) to adult male mice led to a significant stimulation of protein synthesis directed by isolated brain mitochondria in a special cell-free translation system prepared from rabbit reticulocyte lysates. The pre0treatment of the isolated mouse brain mitochondria from MeHg-injected and control (saline-injected) animals with an inhibitor (oligomycin) or inducers (ADP, succinate) of ATP synthesis showed that mitochondrical translation activity was high when ATP synthesis was suppressed and low when ATP synthesis was stimulated.     

The Evolutionary Dynamics of Protein-Protein Interaction Networks Inferred from the Reconstruction of Ancient Networks

Cellular functions are based on the complex interplay of proteins, therefore the structure and dynamics of these protein-protein interaction (PPI) networks are the key to the functional understanding of cells. In the last years, large-scale PPI networks of several model organisms were investigated. A number of theoretical models have been developed to explain both the network formation and the current structure. Favored are models based on duplication and divergence of genes, as they most closely represent the biological foundation of network evolution. However, studies are often based on simulated instead of empirical data or they cover only single organisms. Methodological improvements now allow the analysis of PPI networks of multiple organisms simultaneously as well as the direct modeling of ancestral networks. This provides the opportunity to challenge existing assumptions on network evolution. We utilized present-day PPI networks from integrated datasets of seven model organisms and developed a theoretical and bioinformatic framework for studying the evolutionary dynamics of PPI networks. A novel filtering approach using percolation analysis was developed to remove low confidence interactions based on topological constraints. We then reconstructed the ancient PPI networks of different ancestors, for which the ancestral proteomes, as well as the ancestral interactions, were inferred. Ancestral proteins were reconstructed using orthologous groups on different evolutionary levels. A stochastic approach, using the duplication-divergence model, was developed for estimating the probabilities of ancient interactions from today''s PPI networks. The growth rates for nodes, edges, sizes and modularities of the networks indicate multiplicative growth and are consistent with the results from independent static analysis. Our results support the duplication-divergence model of evolution and indicate fractality and multiplicative growth as general properties of the PPI network structure and dynamics.