Quantitative Proteomics Identify Novel miR-155 Target Proteins

Background: MicroRNAs are 22 nucleotides long non-coding RNAs and exert their function either by transcriptional or translational inhibition. Although many microRNA profiles in different tissues and disease states have already been discovered, only little is known about their target proteins. The microRNA miR-155 is deregulated in many diseases, including cancer, where it might function as an oncoMir. Methodology/Principal Findings: We employed a proteomics technique called ‘‘stable isotope labelling by amino acids in cell culture’ ’ (SILAC) allowing relative quantification to reliably identify target proteins of miR-155. Using SILAC, we identified 46 putative miR-155 target proteins, some of which were previously reported. With luciferase reporter assays, CKAP5 was confirmed as a new target of miR-155. Functional annotation of miR-155 target proteins pointed to a role in cell cycle regulation. Conclusions/Significance: To the best of our knowledge we have investigated for the first time miR-155 target proteins in the HEK293T cell line in large scale. In addition, by comparing our results to previously identified miR-155 target proteins i

Identifying Proteins in Zebrafish Embryos Using Spectral Libraries Generated from Dissected Adult Organs and Tissues

Proteomic

Modellentwicklung und maschinelles Lernen erhöhen die Proteinausbeute

Heterologous expression of genes requires their adaptation to the host organism to achieve adequate protein synthesis rates. Typically codons are adjusted to resemble those seen in highly expressed genes of the host organism which lacks a deeper understanding of codon optimality. The codon-specific elongation model (COSEM) identifies optimal codon choices by simulating ribosome dynamics during mRNA translation. COSEM is used in combination with machine learning techniques to predict protein abundance and to optimize codon usage