Membrane proteins represent the majority of clinical drug targets and are actively involved in a range of cellular processes. However, the complexity of membrane mimetics for membrane protein solubili...

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Data-independent acquisition (DIA) on ion mobility mass spectrometers enables deep proteome coverage and high data completeness in large-scale proteomics studies. For advanced acquisition schemes such as parallel accumulation serial fragmentation-based DIA (diaPASEF) stability of ion mobility (1/K0) over time is crucial for consistent data quality. We found that minor changes in environmental air pressure systematically affect the vacuum pressure in the TIMS analyzer, causing ion mobility shifts. By comparing experimental ion mobilities with historical weather data, we attributed observed drifts to fluctuations in the ground air pressure. Moderate air pressure changes of e.g. fifteen mbar induce ion mobility shifts of 0.025 Vs/cm2. These drifts negatively impact peptide quantification across consecutively acquired samples due to drift-dependent abundance changes and increased missing values for ions located at the boundaries of diaPASEF isolation windows, which cannot be corrected by postprocessing. To address this, we applied an in-batch mobility autocalibration feature on a run-wise basis, leading to full elimination of ion mobility drifts.

In BriefIn the early 2000s before high resolution mass spectrometers were readily available, the Hunt lab developed many approaches to analyze histone modifications on low resolution instruments.

In BriefA personal narrative is provided on Donald Hunt’s development of tandem mass spectrometry methods for sequencing peptides.

Scientific discovery relies on innovative software as much as experimental methods, especially in proteomics, where computational tools are essential for mass spectrometer setup, data analysis, and in...

Quantitative analysis of proteomics data frequently employs peptide-identity-propagation (PIP) — also known as match-between-runs (MBR) — to increase the number of peptides quantified in a given LC-MS/MS experiment. PIP can routinely account for up to 40% of all quantitative results, with that proportion rising as high as 75% in single-cell proteomics. Therefore, a significant concern for any PIP method is the possibility of false discoveries: errors that result in peptides being quantified incorrectly. Although several tools for label-free quantification (LFQ) claim to control the false discovery rate (FDR) of PIP, these claims cannot be validated as there is currently no accepted method to assess the accuracy of the stated FDR. We present a method for FDR control of PIP, called “PIP-ECHO” (PIP Error Control via Hybrid cOmpetition) and devise a rigorous protocol for evaluating FDR control of any PIP method. Using three different datasets, we evaluate PIP-ECHO alongside the PIP procedures implemented by FlashLFQ, IonQuant, and MaxQuant. These analyses show that PIP-ECHO can accurately control the FDR of PIP at 1% across multiple datasets. Only PIP-ECHO was able to control the FDR in data with injected sample size equivalent to a single-cell dataset. The three other methods fail to control the FDR at 1%, yielding false discovery proportions ranging from 2–6%. We demonstrate the practical implications of this work by performing differential expression analyses on spike-in datasets, where different known amounts of yeast or E. coli peptides are added to a constant background of HeLa cell lysate peptides. In this setting, PIP-ECHO increases both the accuracy and sensitivity of differential expression analysis: our implementation of PIP-ECHO within FlashLFQ enables the detection of 53% more differentially abundant proteins than MaxQuant and 146% more than IonQuant in the spike-in dataset. ### Competing Interest Statement The authors have declared no competing interest.

In proteomics, postproline cleaving enzymes (PPCEs), such as Aspergillus niger prolyl endopeptidase (AnPEP) and neprosin, complement proteolytic tools because proline is a stop site for many proteases...

In any machine learning study, high quality data for training and validating the model is critical. This paper describes the result of an iterative process of data wrangling and quality control, which...

Scientific Data - A multi-species benchmark for training and validating mass spectrometry proteomics machine learning models