Bayesian Proteoform Modeling Improves Protein Quantification of Global Proteomic Measurements

Abstract

As the capability of mass spectrometry-based proteomics has matured, tens of thousands of peptides can be measured simultaneously, which has the benefit of offering a systems view of protein expression. However, a major challenge is that, with an increase in throughput, protein quantification estimation from the native measured peptides has become a computational task. A limitation to existing computationally driven protein quantification methods is that most ignore protein variation, such as alternate splicing of the RNA transcript and post-translational modifications or other possible proteoforms, which will affect a significant fraction of the proteome. The consequence of this assumption is that statistical inference at the protein level, and conse- The application of MS-based proteomics has resulted in large-scale studies in which the set of measured, and subsequently identified, peptides is often used to estimate protein abundance. In particular, label-free MS-based proteomics is highly effective for identification of peptides and measurement of relative peptide abundances (1, 2), but it does not directly yield protein quantities. The importance of accurate protein quantification cannot be understated; it is the essential component of identifying biomarkers of disease or defining the relationship between gene regulations, protein interactions, and signaling networks in a cellular system (3, 4). The major challenge is that protein abundance depends not only on transcription rates of the gene but also on additional control mechanisms, such as mRNA stability, translational regulation, and protein degradation. Moreover, the functional activity of proteins can be altered through a variety of posttranslational modifications or proteolytic processing and alternative splicing, events which selectively alter the abundance of some selected peptides while leaving others unchanged (4). This complexity of the proteome, in addition to issues associated with the measurement and identification

Review, Evaluation, and Discussion of the Challenges of Missing Value Imputation for Mass Spectrometry-Based Label-Free Global Proteomics

“…This dataset has already been described in detail. 45 As a summary, this dataset consists of 15 mouse plasma samples, which were subjected to four dilutions such that the total amount of protein was kept constant by supplementing with protein from Shewanella oneidensis . The dilutions consisted of a ratio of (1) 1:0 mouse/ S. oneidensis , (2) 1:1 mouse/S.…”

P-MartCancer–Interactive Online Software to Enable Analysis of Shotgun Cancer Proteomic Datasets

“…There is a wealth of capabilities that could be extremely useful to the proteomics community, many of which are under active development. For example, proteoform discovery, that is the identification of proteins with multiple forms, is also an important component of protein quantification (6). Additional future work is focused on adding new capabilities in statistical testing, machine learning and gene set enrichment analysis, as well as the development of a user-upload capability to enable all researchers with MS-based peak-intensity data to create reproducible statistical downstream processing pipelines.…”

Proteome sequencing goes deep

“…This feature makes label-free approaches very attractive for deep proteome quantification, though stable isotope labeling strategies are still more straightforward for the comparative analysis of low-abundance PTMs [52]. Furthermore, statistical analysis of signatures at the peptide-level can reveal information regarding the presence and expression patterns of one or more proteomes, an approach that will be greatly empowered by high protein sequence coverage [53]. …”