Predicting the Dynamics of Protein Abundance

• DOI: 10.1074/mcp.m113.033076
• Publication Date: 2014-02-17T01:13:34Z
• Authors: Ahmed M. Mehdi; Ralph Patrick; Timothy L. Bailey; Mikael Bodén
• Keywords: Proteome

Protein synthesis is finely regulated across all organisms, from bacteria to humans, and its integrity underpins many important processes. Emerging evidence suggests that the dynamic range of protein abundance greater than observed at transcript level. Technological breakthroughs now mean sequencing-based measurement mRNA levels routine, but protocols for measuring remain both complex expensive. This paper introduces a Bayesian network integrates transcriptomic proteomic data predict model effects determinants. We aim use this follow molecular response over time, condition-specific data, in order understand adaptation during processes such as cell cycle. With microarray available conditions, general utility predictor broad. Whereas most quantitative proteomics studies have focused on higher we developed predictive Saccharomyces cerevisiae Schizosaccharomyces pombe explore latitude Our primarily relies level, mRNA-protein interaction, folding energy half-life, tRNA adaptation. The combination key features, allowing low certainty uneven coverage experimental observations, gives comparatively minor robust prediction accuracy. substantially improved analysis regulation cycle: predicted identified twice cell-cycle-associated proteins levels. Predicted was more expression, agreeing with human line. illustrate how same can be used when available, lending credence emerging view affects translation efficiency. software research are http://bioinf.scmb.uq.edu.au/proteinabundance/.