Exponentially Modified Protein Abundance Index (emPAI) for Estimation of Absolute Protein Amount in Proteomics by the Number of Sequenced Peptides per Protein*S

By Yasushi Ishihama, Y. Oda, Tsuyoshi Tabata, Toshitaka Sato, T. Nagasu, Juri Rappsilber, Matthias Mann. Molecular & Cellular Proteomics, 2005

2,004 citations recorded by the source corpus.

To estimate absolute protein contents in complex mixtures, we previously defined a protein abundance index (PAI) as the number of observed peptides divided by the number of observable peptides per protein (Rappsilber, J., Ryder, U., Lamond, A. I., and Mann, M. (2002) Large-scale proteomic analysis of the human spliceosome. Genome. Res. 12, 1231–1245). Here we report that PAI values obtained at different concentrations of serum albumin show a linear relationship with the logarithm of protein concentration in LC-MS/MS experiments. This was also the case for 46 proteins in a mouse whole cell lysate. For absolute quantitation, PAI was converted to exponentially modified PAI (emPAI), equal to 10PAI minus one, which is proportional to protein content in a protein mixture. For the 46 proteins in the whole lysate, the deviation percentages of the emPAI-based abundances from the actual values were within 63% on average, similar or better than determination of abundance by protein staining. emPAI was applied to comprehensive protein expression analysis and to a comparison study between gene and protein expression in a human cancer cell line, HCT116. The values of emPAI are easily calculated and add important quantitation information to proteomic experiments; therefore we suggest that they should be reported in large scale proteomic identification projects.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/9fd12870195da7af5a2491cf2366b9f179ced0be

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Citations in the corpus

Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation (2007, background): “By contrast, approximating abundance from the number of repeat peptide observations per protein ignores protein size—as large proteins contribute more peptides than small ones, their abundance will be overestimated unless the data are normalize”
Less label, more free: Approaches in label‐free quantitative mass spectrometry (2011, methodology): “PAI was later exponentially modified to give emPAI, the exponential form of PAI minus one, which is directly proportional to the protein content in a sample [25].”
Absolute Quantification of Proteins by LCMSE (2006, background): “(12) reported an emPAI(1) value that the authors suggest is directly proportional to the protein content in a protein mixture.”
Correlation of mRNA and protein in complex biological samples (2009, neutral): “50 1103 [32]”
Significance Analysis of Spectral Count Data in Label-free Shotgun Proteomics*S (2008, background): “…various types of normalized scores based on transformed spectral counts, including methods that explore weighted scoring by peptide match score (16), normalization by the number of potential peptide matches (17), peptide sequence length, overall experiment-wide abundance (18), or incorporation…”
PaxDb, a Database of Protein Abundance Averages Across All Three Domains of Life (2012, background): “Nevertheless, these data sets often hold quantifiable information as well (mainly through spectral counting (19–21)), which is again best used by aggregating over a large number of samples.”
Multidimensional proteomics for cell biology (2015, methodology): “Most studies have used one of three main methodologies for the relative quantification of samples: label‑free quantification 5,16–18 , in vivo metabolic stable-isotope labelling (stable isotope labelling by amino acids in cell culture (SILAC) 15, 19 , N-labelling 20 and NeuCode SILAC 21 ), or…”
The beginning of a beautiful friendship: cross-linking/mass spectrometry and modelling of proteins and multi-protein complexes (2010, methodology): “This strategy is used when comparing the concentration of different proteins in the same sample (Rappsilber et al., 2002; Ishihama et al., 2005).”
Label-free, normalized quantification of complex mass spectrometry data for proteomics analysis (2009, neutral): “Large proteins can contribute more peptides than smaller ones, thus their abundance may be overestimated 16 , 17 .”
Mass Spectrometry Profiling of HLA-Associated Peptidomes in Mono-allelic Cells Enables More Accurate Epitope Prediction (2017, neutral): “On the other hand, adding RNA-Seq or iBAQ-based (Ishihama et al., 2005) protein expression (models ‘‘ASR’’ and ‘‘ASP’’) improved PPV dramatically—to 39% and 47%, respectively.”
The Protein Composition of Mitotic Chromosomes Determined Using Multiclassifier Combinatorial Proteomics (2010, positive): “To estimate the amounts of individual proteins in mitotic chromosomes, we used an established protocol (Rappsilber et al., 2002; Ishihama et al., 2005, 2008) to calculate a scaled protein abundance index based on the number of peptides observed and the number of times that each peptide is observed…”