21^st Annual US HUPO Conference
February 22 – 26, 2025 | Philadelphia, USA

Poster Session 1: Monday, February 24 | 4:30 PM – 6:00 PM

Laboratories working with bottom-up proteomics data are frequently confronted with computational challenges in the workflow from raw data to conclusions. Often, the lack of automated pipelines combined with disconnected local infrastructure for raw data storage, data processing, systematic result storage and data interpretation lead to substantial system administration overhead and manual, error-prone steps in this workflow. Recent developments of fast-scanning instruments further exacerbate the issue, as the number of files and raw file size quickly exceed the capacity of local infrastructure. Here, we present a highly scalable, fully automatable, cloud-based proteomics platform as a catalyst for the proteomic data workflow, encapsulating raw data storage, processing, systematic result storage, and easy data interpretation.

Peptide and protein quantification is essential for deriving biological insights from LC-MS/MS data, with label-free MS1 quantification being widely used due to its simplicity and universal applicability. However, extracting accurate quantitative data from MS1 spectra through feature detection is challenging, especially in complex samples with overlapping isotope peaks. To address these limitations, we present a novel approach for MS1 quantification based on spectrum-centric deconvolution to improve the accuracy of quantification.

Data-independent acquisition (DIA) offers reproducibility and high proteome coverage but generates complex spectra that challenge peptide identification. Various search engines such as DIA-NN, Spectronaut, and Chimerys have been developed to interpret DIA data, each using different methods to calculate false discovery rates (FDR) and report identifications (IDs). This study compares these tools' performance evaluating the impact of their scoring methods on peptide and protein identification beyond ID counting and scrutinizing tailor-made statistics and scores.

Acetylation and ubiquitination are key post-translational modifications (PTMs) that influence protein function and stability. Mass spectrometry-based bottom-up proteomics is commonly used to identify and quantify modified peptides. Initially, data-dependent acquisition (DDA) was favored for PTM analysis due to its selective precursor isolation. However, data-independent acquisition (DIA) is gaining momentum, despite challenges in PTM identification and localization. We present a major update to Chimerys, a spectrum-centric search algorithm, which adds compatibility with additional PTMs and improves DIA data processing speed.