BackgroundThe current knowledge of genes and proteins comes from 'naturally designed' coding and non-coding regions. It would be interesting to move beyond natural boundaries and make user-defined parts. To explore this possibility we made six non-natural proteins in E. coli. We also studied their potential tertiary structure and phenotypic outcomes.ResultsThe chosen intergenic sequences were amplified and expressed using pBAD 202/D-TOPO vector. All six proteins showed significantly low similarity to the known proteins in the NCBI protein database. The protein expression was confirmed through Western blot. The endogenous expression of one of the proteins resulted in the cell growth inhibition. The growth inhibition was completely rescued by culturing cells in the inducer-free medium. Computational structure prediction suggests globular tertiary structure for two of the six non-natural proteins synthesized.ConclusionTo our best knowledge, this is the first study that demonstrates artificial synthesis of non-natural proteins from existing genomic template, their potential tertiary structure and phenotypic outcome. The work presented in this paper opens up a new avenue of investigating fundamental biology. Our approach can also be used to synthesize large numbers of non-natural RNA and protein parts for useful applications.

The apparently paradoxical lack of correlation between the huge increase in the discovery of new potential drug targets made possible by the post-genomic sciences and new drugs development has stimulated many different interpretations. Here we illustrate the general principle of redundancy of biological pathways on hand of simplified mathematical approaches applied to different models of biological regulation. The simulation was based on the analysis of the 'degree of autonomy' of network architectures in which the possibility for an external stimulus (e.g. a drug) impinging into a specific node to be sensed by the entire network, and eventually amplified up to a macroscopic consequence, was demonstrated to be limited to strictly linear pathways. The implications of such a result for poly-pharmacology and computational approaches to drug development are described as well.

Background: In this work a simple method for the computation of relative similarities between homologous metabolic network modules is presented. The method is similar to classical sequence alignment and allows for the generation of phenotypic trees amenable to be compared with correspondent sequence based trees. The procedure can be applied to both single metabolic modules and whole metabolic network data without the need of any specific assumption.

Hubs are ubiquitous network elements with high connectivity. One of the common observations about hub proteins is their preferential attachment leading to scale-free network topology. Here we examine the question: does rich protein always get richer, or can it get poor too? To answer this question, we compared similar and well-annotated hub proteins in six organisms, from prokaryotes to eukaryotes. Our findings indicate that hub proteins retain, gain or lose connectivity based on the context. Furthermore, the loss or gain of connectivity appears to correlate with the functional role of the protein in a given system.