Dataset Information

Bovine alpha-lactalbumine microLC-timsTOF PRo

ABSTRACT: The investigation focused on the impact of software settings Bovine alpha-lactalbumin was used as a model protein due to its relatively small size, allowing for detailed tracking of any resulting changes. Furthermore, the reproducibility of results was investigated based on 18 measurements of the same sample.

INSTRUMENT(S): timsTOF Pro

ORGANISM(S): Bos Taurus (bovine)

TISSUE(S): Milk

SUBMITTER: Andreas Mauser

LAB HEAD: Monika Pischetsrieder

PROVIDER: PXD050363 | Pride | 2024-09-23

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	ALA_Reproducibility.zip	Other
	ALA_Reproducibility_PEAKS-Studio.zip	Other
	Untreated-ALA_Sequenze-coverage.zip	Other
	Untreated-ALA_Sequenze-coverage_PEAKS-Studio.zip	Other
	heated_lactose-modified_untreated_ALA_enzyme-settings.rar	Other

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Publications

Influence of Software Settings on the Identification Rate, Quantification Results, and Reproducibility in Profiling Post-Translational Modifications by Microflow Liquid Chromatography-Ion Mobility-Quadrupole Time-Of-Flight Analysis Using PEAKS Software.

Mauser Andreas A Gensberger-Reigl Sabrina S Dalabasmaz Sevim S Schichtl Theresa Maria TM Dittrich Daniel D Pischetsrieder Monika M

Journal of proteome research 20240916 10

The influence of data evaluation parameters on qualitative and quantitative results of untargeted shotgun profiling of enzymatic and nonenzymatic post-translational modifications (PTMs) was investigated in a model of bovine whey protein α-lactalbumin heated with lactose. Based on the same raw data, individual adjustments to the protein database and enzyme settings of PEAKS studio software increased the identification rate from 27 unmodified peptides to 48 and from 322 peptides in total to 535. T ...[more]

PMID: 39284794

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Project description:For bottom-up proteomic analysis, the goal of analytical pipelines that process the raw output of mass spectrometers is to detect, characterise, identify, and quantify peptides. The initial steps of detecting and characterising features in raw data must overcome some considerable challenges. The data presents as a sparse array, sometimes containing billions of intensity readings over time. These points represent both signal and chemical or electrical noise. Depending on the biological sample's complexity, tens to hundreds of thousands of peptides may be present in this vast data landscape. For ion mobility-based LC-MS analysis, each peptide is comprised of a grouping of hundreds of single intensity readings in three dimensions: mass-over-charge (m/z), mobility, and retention time. There is no inherent information about any associations between individual points; whether they represent a peptide or noise must be inferred from their structure. Peptides each have multiple isotopes, different charge states, and a dynamic range of intensity of over six orders of magnitude. Due to the high complexity of most biological samples, peptides often overlap in time and mobility, making it very difficult to tease apart isotopic peaks, to apportion the intensity of each and the contribution of each isotope to the determination of the peptide's monoisotopic mass, which is critical for the peptide's identification. Here we describe four algorithms for the Bruker timsTOF Pro that each play an important role in finding peptide features and determining their characteristics. These algorithms focus on separate characteristics that determine how candidate features are detected in the raw data. The first two algorithms deal with the complexity of the raw data, rapidly clustering raw data into spectra that allows isotopic peaks to be resolved. The third algorithm compensates for saturation of the instrument's detector thereby recovering lost dynamic range, and lastly, the fourth algorithm increases confidence of peptide identifications by simplification of the fragment spectra. These algorithms are effective in processing raw data to detect features and extracting the attributes required for peptide identification, and make an important contribution to an analytical pipeline by detecting features that are higher quality and better segmented from other peptides in close proximity. The software has been developed in Python using Numpy and Pandas and made freely available with an open-source MIT license to facilitate experimentation and further improvement (DOI 10.5281/zenodo.6513126). Data are available via ProteomeXchange with identifier PXD030706.