Can we put Humpty Dumpty back together again? What does protein quantification mean in bottom-up proteomics?

Abstract

Bottom-up proteomics provides peptide measurements and has been invaluable for moving proteomics into large-scale analyses. In bottom-up proteomics, protein parsimony and protein inference derived from these measured peptides are important for determining which protein coding genes are present. However, given the complexity of RNA splicing processes, and how proteins can be modified post-translationally, it is overly simplistic to assume that all peptides that map to a singular protein coding gene will demonstrate the same quantitative response. Accordingly, by assuming all peptides from a protein coding sequence are representative of the same protein we may be missing out on detecting important biological differences. To better account for the complexity of the proteome we need to think of new or better ways of handling peptide data.

Mag-Net: Rapid enrichment of membrane-bound particles enables high coverage quantitative analysis of the plasma proteome

“…Imputation of missing data values and peptide grouping did not significantly alter the CV. As highlighted previously 69 , protein grouping further improved the median CV to 20.89%…”

“…Imputation of missing data values and peptide grouping did not significantly alter the CV. As highlighted previously 69 , protein grouping further improved the median CV to 20.89% To assess whether individual protein quantities measured by Mag-Net could distinguish between disease states we used a combination of receiver operator characteristic (ROC) curve analysis and machine learning. We performed six pairwise analyses including 1) ADD versus all others, 2) HCN versus all others, 3) PDD versus all others, 4) PDCN versus all others, 5) dementia (ADD and PDD) versus cognitively normal (HCN and PDCN), and 6) Parkinson's disease (PDCN and PDD) versus non-Parkinson's disease (ADD and HCN).…”

Characterization of peptide-protein relationships in protein ambiguity groups via bipartite graphs

“…The investigation of bipartite peptide-protein graphs is therefore highly relevant to the current research in proteomics. Furthermore, the analysis pf bipartite graphs can also be applied to the field of proteoform research by including isoforms and and peptides with PTMs [49].…”

Reanalysis of ProteomicsDB Using an Accurate, Sensitive, and Scalable False Discovery Rate Estimation Approach for Protein Groups

“…While such data is increasingly deposited into searchable public data repositories such as ProteomicsDB ( 5 ), PRIDE ( 6 ), MassIVE ( http://massive.ucsd.edu ), or PeptideAtlas ( 7 ), estimating the proportion of false positive identifications (false discovery rate; FDR) at the gene-level is a nontrivial task ( 8 , 9 , 10 ). An even larger challenge is presented by distinguishing between different protein products from the same gene ( 11 ), such as splice variants ( 12 ) or SNPs or when analyzing mixtures of orthologous proteins from different species such as human/mouse xenografts or bacterial communities in metaproteomics ( 13 ).…”