Compositional Proteomics: Effects of Spatial Constraints on Protein Quantification Utilizing Isobaric Tags.

Mass spectrometry (MS) has become an accessible tool for whole proteome quantitation with the ability to characterize protein expression across thousands of proteins within a single experiment. A subset of MS quantification methods (e.g., SILAC and label-free) monitor the relative intensity of intact peptides, where thousands of measurements can be made from a single mass spectrum. An alternative approach, isobaric labeling, enables precise quantification of multiple samples simultaneously through unique and sample specific mass reporter ions. Consequently, in a single scan, the quantitative signal comes from a limited number of spectral features (≤11). The signal observed for these features is constrained by automatic gain control, forcing codependence of concurrent signals. The study of constrained outcomes primarily belongs to the field of compositional data analysis. We show experimentally that isobaric tag proteomics data are inherently compositional and highlight the implications for data analysis and interpretation. We present a new statistical model and accompanying software that improves estimation accuracy and the ability to detect changes in protein abundance. Finally, we demonstrate a unique compositional effect on proteins with infinite changes. We conclude that many infinite changes will appear small and that the magnitude of these estimates is highly dependent on experimental design

Predictive overfitting in immunological applications: Pitfalls and solutions

Overfitting describes the phenomenon where a highly predictive model on the training data generalizes poorly to future observations. It is a common concern when applying machine learning techniques to contemporary medical applications, such as predicting vaccination response and disease status in infectious disease or cancer studies. This review examines the causes of overfitting and offers strategies to counteract it, focusing on model complexity reduction, reliable model evaluation, and harnessing data diversity. Through discussion of the underlying mathematical models and illustrative examples using both synthetic data and published real datasets, our objective is to equip analysts and bioinformaticians with the knowledge and tools necessary to detect and mitigate overfitting in their research

Interactome analyses revealed that the U1 snRNP machinery overlaps extensively with the RNAP II machinery and contains multiple ALS/SMA-causative proteins

Mutations in multiple RNA/DNA binding proteins cause Amyotrophic Lateral Sclerosis (ALS). Included among these are the three members of the FET family (FUS, EWSR1 and TAF15) and the structurally similar MATR3. Here, we characterized the interactomes of these four proteins, revealing that they largely have unique interactors, but share in common an association with U1 snRNP. The latter observation led us to analyze the interactome of the U1 snRNP machinery. Surprisingly, this analysis revealed the interactome contains ~220 components, and of these, >200 are shared with the RNA polymerase II (RNAP II) machinery. Among the shared components are multiple ALS and Spinal muscular Atrophy (SMA)-causative proteins and numerous discrete complexes, including the SMN complex, transcription factor complexes, and RNA processing complexes. Together, our data indicate that the RNAP II/U1 snRNP machinery functions in a wide variety of molecular pathways, and these pathways are candidates for playing roles in ALS/SMA pathogenesis

A phosphatase threshold sets the level of Cdk1 activity in early mitosis in budding yeast.

Entry into mitosis is initiated by synthesis of cyclins, which bind and activate cyclin-dependent kinase 1 (Cdk1). Cyclin synthesis is gradual, yet activation of Cdk1 occurs in a stepwise manner: a low level of Cdk1 activity is initially generated that triggers early mitotic events, which is followed by full activation of Cdk1. Little is known about how stepwise activation of Cdk1 is achieved. A key regulator of Cdk1 is the Wee1 kinase, which phosphorylates and inhibits Cdk1. Wee1 and Cdk1 show mutual regulation: Cdk1 phosphorylates Wee1, which activates Wee1 to inhibit Cdk1. Further phosphorylation events inactivate Wee1. We discovered that a specific form of protein phosphatase 2A (PP2A(Cdc55)) opposes the initial phosphorylation of Wee1 by Cdk1. In vivo analysis, in vitro reconstitution, and mathematical modeling suggest that PP2A(Cdc55) sets a threshold that limits activation of Wee1, thereby allowing a low constant level of Cdk1 activity to escape Wee1 inhibition in early mitosis. These results define a new role for PP2A(Cdc55) and reveal a systems-level mechanism by which dynamically opposed kinase and phosphatase activities can modulate signal strength

Proteome-Wide Evaluation of Two Common Protein Quantification Methods

Proteomics experiments commonly aim to estimate and detect differential abundance across all expressed proteins. Within this experimental design, some of the most challenging measurements are small fold changes for lower abundance proteins. While bottom-up proteomics methods are approaching comprehensive coverage of even complex eukaryotic proteomes, failing to reliably quantify lower abundance proteins can limit the precision and reach of experiments to much less than the identifiedlet alone totalproteome. Here we test the ability of two common methods, a tandem mass tagging (TMT) method and a label-free quantitation method (LFQ), to achieve comprehensive quantitative coverage by benchmarking their capacity to measure 3 different levels of change (3-, 2-, and 1.5-fold) across an entire data set. Both methods achieved comparably accurate estimates for all 3-fold-changes. However, the TMT method detected changes that reached statistical significance three times more often due to higher precision and fewer missing values. These findings highlight the importance of refining proteome quantitation methods to bring the number of usefully quantified proteins into closer agreement with the number of total quantified proteins

Proteome-Wide Evaluation of Two Common Protein Quantification Methods

Proteomics experiments commonly aim to estimate and detect differential abundance across all expressed proteins. Within this experimental design, some of the most challenging measurements are small fold changes for lower abundance proteins. While bottom-up proteomics methods are approaching comprehensive coverage of even complex eukaryotic proteomes, failing to reliably quantify lower abundance proteins can limit the precision and reach of experiments to much less than the identifiedlet alone totalproteome. Here we test the ability of two common methods, a tandem mass tagging (TMT) method and a label-free quantitation method (LFQ), to achieve comprehensive quantitative coverage by benchmarking their capacity to measure 3 different levels of change (3-, 2-, and 1.5-fold) across an entire data set. Both methods achieved comparably accurate estimates for all 3-fold-changes. However, the TMT method detected changes that reached statistical significance three times more often due to higher precision and fewer missing values. These findings highlight the importance of refining proteome quantitation methods to bring the number of usefully quantified proteins into closer agreement with the number of total quantified proteins

Proteome-Wide Evaluation of Two Common Protein Quantification Methods

Proteomics experiments commonly aim to estimate and detect differential abundance across all expressed proteins. Within this experimental design, some of the most challenging measurements are small fold changes for lower abundance proteins. While bottom-up proteomics methods are approaching comprehensive coverage of even complex eukaryotic proteomes, failing to reliably quantify lower abundance proteins can limit the precision and reach of experiments to much less than the identifiedlet alone totalproteome. Here we test the ability of two common methods, a tandem mass tagging (TMT) method and a label-free quantitation method (LFQ), to achieve comprehensive quantitative coverage by benchmarking their capacity to measure 3 different levels of change (3-, 2-, and 1.5-fold) across an entire data set. Both methods achieved comparably accurate estimates for all 3-fold-changes. However, the TMT method detected changes that reached statistical significance three times more often due to higher precision and fewer missing values. These findings highlight the importance of refining proteome quantitation methods to bring the number of usefully quantified proteins into closer agreement with the number of total quantified proteins

Proteome-Wide Evaluation of Two Common Protein Quantification Methods

Proteomics experiments commonly aim to estimate and detect differential abundance across all expressed proteins. Within this experimental design, some of the most challenging measurements are small fold changes for lower abundance proteins. While bottom-up proteomics methods are approaching comprehensive coverage of even complex eukaryotic proteomes, failing to reliably quantify lower abundance proteins can limit the precision and reach of experiments to much less than the identifiedlet alone totalproteome. Here we test the ability of two common methods, a tandem mass tagging (TMT) method and a label-free quantitation method (LFQ), to achieve comprehensive quantitative coverage by benchmarking their capacity to measure 3 different levels of change (3-, 2-, and 1.5-fold) across an entire data set. Both methods achieved comparably accurate estimates for all 3-fold-changes. However, the TMT method detected changes that reached statistical significance three times more often due to higher precision and fewer missing values. These findings highlight the importance of refining proteome quantitation methods to bring the number of usefully quantified proteins into closer agreement with the number of total quantified proteins