Abstract

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

Highlights

  • Proteins are complex molecules with many cellular functions, including signaling, amplification and transduction, control of gene expression, and molecular transport

  • We aim to show that, compared with actual mRNA expression levels, predicted protein abundance more faithfully represents the extent of change we expect to see during the yeast cell cycle

  • A Bayesian Network Model of Protein Abundance—Our Bayesian network model integrates features that are derived from multiple sources: nucleic acid sequence, mRNA expression, protein expression, interaction between mRNA and RNA binding proteins, mRNA fold, and mRNA and protein half-life

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Summary

Introduction

Proteins are complex molecules with many cellular functions, including signaling, amplification and transduction, control of gene expression, and molecular transport The integrity of these processes depends on protein synthesis, which is finely regulated across all organisms, from bacteria to humans. Proteome-wide abundance illustrates the dynamic range of cellular activity during important processes such as the cell cycle [10] or in response to stress [11]. In order to predict (and model the determinants of) protein abundance under various conditions, we need methods that can integrate mRNA expression and complementary information on events and factors that modulate the Molecular & Cellular Proteomics 13.5. Predicting Protein Abundance rate of protein synthesis Such methods would allow us to follow a molecular response over time, from condition-specific data, to understand the cellular behavior and what regulates it. We investigate the dynamic range of predicted protein abundance during the yeast cell cycle, for which there are currently no experimental data available

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