Biological factors and statistical limitations prevent detection of most noncanonical proteins by mass spectrometry.
Aaron WacholderAnne-Ruxandra CarvunisPublished in: PLoS biology (2023)
Ribosome profiling experiments indicate pervasive translation of short open reading frames (ORFs) outside of annotated protein-coding genes. However, shotgun mass spectrometry (MS) experiments typically detect only a small fraction of the predicted protein products of this noncanonical translation. The rarity of detection could indicate that most predicted noncanonical proteins are rapidly degraded and not present in the cell; alternatively, it could reflect technical limitations. Here, we leveraged recent advances in ribosome profiling and MS to investigate the factors limiting detection of noncanonical proteins in yeast. We show that the low detection rate of noncanonical ORF products can largely be explained by small size and low translation levels and does not indicate that they are unstable or biologically insignificant. In particular, proteins encoded by evolutionarily young genes, including those with well-characterized biological roles, are too short and too lowly expressed to be detected by shotgun MS at current detection sensitivities. Additionally, we find that decoy biases can give misleading estimates of noncanonical protein false discovery rates, potentially leading to false detections. After accounting for these issues, we found strong evidence for 4 noncanonical proteins in MS data, which were also supported by evolution and translation data. These results illustrate the power of MS to validate unannotated genes predicted by ribosome profiling, but also its substantial limitations in finding many biologically relevant lowly expressed proteins.
Keyphrases
- mass spectrometry
- multiple sclerosis
- liquid chromatography
- ms ms
- loop mediated isothermal amplification
- real time pcr
- label free
- single cell
- genome wide
- capillary electrophoresis
- high resolution
- gas chromatography
- protein protein
- small molecule
- minimally invasive
- binding protein
- machine learning
- stem cells
- gene expression
- big data
- artificial intelligence
- dna methylation
- transcription factor
- simultaneous determination