Target site specificity and in vivo complexity of the mammalian arginylome.

Protein arginylation mediated by arginyltransferase ATE1 is a key regulatory process essential for mammalian embryogenesis, cell migration, and protein regulation. Despite decades of studies, very little is known about the specificity of ATE1-mediated target site recognition. Here, we used in vitro assays and computational analysis to dissect target site specificity of mouse arginyltransferases and gain insights into the complexity of the mammalian arginylome. We found that the four ATE1 isoforms have different, only partially overlapping target site specificity that includes more variability in the target residues than previously believed. Based on all the available data, we generated an algorithm for identifying potential arginylation consensus motif and used this algorithm for global prediction of proteins arginylated in vivo on the N-terminal D and E. Our analysis reveals multiple proteins with potential ATE1 target sites and expand our understanding of the biological complexity of the intracellular arginylome.

Gene Cluster Profile Vectors: a method to infer functionally related gene sets by grouping proximity-based gene clusters.

BACKGROUND: Proximity-based methods and co-evolution-based phylogenetic profiles methods have been successfully used for the identification of functionally related genes. Proximity-based methods are effective for physically clustered genes while the phylogenetic profiles method is effective for co-occurring gene sets. However, both methods predict many false positives and false negatives. In this paper, we propose the Gene Cluster Profile Vector (GCPV) method, which combines these two methods by using phylogenetic profiles of whole gene clusters. The GCPV method is, currently, the only genome comparison based method that allows for the characterization of relationships between gene clusters based profiles of individual genes in clusters. RESULTS: The GCPV method groups together reasonably related operons in E. coli about 60% of the time. The method is not sensitive to the choice of a reference genome set used and it outperforms the conventional phylogenetic profiles method. Finally, we show that the method works well for predicted gene clusters from C. crescentus and can serve as an important tool not only for understanding gene function, but also for elucidating mechanisms of general biological processes. CONCLUSIONS: The GCPV method has shown to be an effective and robust approach to the prediction of functionally related gene sets from proximity-based gene clusters or operons.