The ability to catalytically cleave protein substrates after synthesis is fundamental for all forms of life. Accordingly, site-specific proteolysis is one of the most important post-translational modifications. The key to understanding the physiological role of a protease is to identify its natural substrate(s). Knowledge of the substrate specificity of a protease can dramatically improve our ability to predict its target protein substrates, but this information must be utilized in an effective manner in order to efficiently identify protein substrates by in silico approaches. To address this problem, we present PROSPER, an integrated feature-based server for in silico identification of protease substrates and their cleavage sites for twenty-four different proteases. PROSPER utilizes established specificity information for these proteases (derived from the MEROPS database) with a machine learning approach to predict protease cleavage sites by using different, but complementary sequence and structure characteristics. Features used by PROSPER include local amino acid sequence profile, predicted secondary structure, solvent accessibility and predicted native disorder. Thus, for proteases with known amino acid specificity, PROSPER provides a convenient, pre-prepared tool for use in identifying protein substrates for the enzymes. Systematic prediction analysis for the twenty-four proteases thus far included in the database revealed that the features we have included in the tool strongly improve performance in terms of cleavage site prediction, as evidenced by their contribution to performance improvement in terms of identifying known cleavage sites in substrates for these enzymes. In comparison with two state-of-the-art prediction tools, PoPS and SitePrediction, PROSPER achieves greater accuracy and coverage. To our knowledge, PROSPER is the first comprehensive server capable of predicting cleavage sites of multiple proteases within a single substrate sequence using machine learning techniques. It is freely available at http://lightning.med.monash.edu.au/PROSPER/.

The geographic origins of breeds and genetic basis of variation within the widely distributed and phenotypically diverse domestic rock pigeon (Columba livia) remain largely unknown. We generated a rock pigeon reference genome and additional genome sequences representing domestic and feral populations. We find evidence for the origins of major breed groups in the Middle East, and contributions from a racing breed to North American feral populations. We identify EphB2 as a strong candidate for the derived head crest phenotype shared by numerous breeds, an important trait in mate selection in many avian species. We also find evidence that this trait evolved just once and spread throughout the species, and that the crest originates early in development by the localized molecular reversal of feather bud polarity.

Bacterial communities are key drivers of soil fertility and agriculture productivity. Understanding how soil bacterial communities change in response to different conditions is an important aspect in the development of sustainable agriculture. There is a desire to reduce the current reliance on high inputs of chemicals and fertilisers in agriculture, but limited data are available on how this might impact soil bacterial communities. This study investigated the bacterial communities in a spring barley monoculture site subjected to two different input regimes for over 12 years: a conventional chemical/fertiliser regime, and a reduced input regime. A culture independent approach was performed to compare the bacterial communities through 16S rRNA gene PCR-DGGE. PCO analysis revealed that the rhizosphere has a strong structuring effect on the bacterial community. Moreover, high inputs of agrichemicals lead to an increase of phosphorus level in the soil and a concomitant reduction of the bacterial diversity. These results may help to evaluate the environmental risks associated with agrichemical usage.

Supplementary data are available at Bioinformatics online.