72nd ASMS Conference
on Mass Spectrometry and Allied Topics
June 2 – 6, 2024 | Anaheim, USA
At this year's ASMS, MSAID will showcase its newest product and the latest updates to CHIMERYS, the intelligent search algorithm for proteomics.
Connect with us in Anaheim
Visit us at Booth #200
We are thrilled to announce our participation at ASMS in Anaheim. Get ready to join us at Booth #200, located next to the main entrance. Be prepared to be amazed by our newest product and the new capabilities of CHIMERYS 4.0, our cutting-edge proteomics software.
Register now to secure your spot for our breakfast seminar and stay tuned for exciting updates on our scientific content at the conference!
See you soon at #ASMS2024!
Our Poster Presentations
-
MP 448: Spectrum-centric and acquisition method-agnostic deconvolution of tandem mass spectra improves the identification and site localization of acetylated and ubiquitinated peptides
Topic Area: Informatics: Peptide ID and Quantification
Date and Time: Monday, June 3, 2024 | 10:30 am - 2:30 pm (Pacific Time)
Acetylation and ubiquitination are two important post-translational modifications (PTMs) influencing the function and half-life of proteins. Mass spectrometry-based bottom-up proteomics is the method of choice for identifying and quantifying peptides harboring PTMs. Traditionally, data dependent (DDA) data were acquired for PTM analysis because of its simplicity and high precursor specificity due to narrow isolation windows. Nowadays, scientists increasingly use data independent (DIA) data for PTM analysis, sometimes with wider isolation windows, which complicates PTM identification and site localization. Here, we present a major update to Chimerys offering compatibility with more PTMs, a novel module for intensity-based PTM localization in DDA, WWA and DIA data, and a new architecture, yielding a 2-fold reduction in processing time for DIA data. -
MP 441: Sunshine and rainbows: constructive interference and the deconvolution of peptide MS1 spectra
Topic Area: Informatics: Peptide ID and Quantification
Date and Time: Monday, June 3, 2024 | 10:30 am - 2:30 pm (Pacific Time)
Peptide and protein quantitation plays a pivotal role in extracting biological insights from LC-MS/MS data. Among various quantification methods, label-free MS1 quantification stands out due to its universality and simplicity without the need for specialized sample preparation. However, the process of extracting quantitative data from MS1 spectra poses significant challenges, predominantly due to the necessity of comprehensive feature detection. This method is susceptible to interference caused by overlapping isotope peaks originating from distinct peptide isotope envelopes. Such interference is increasingly probable in complex samples analyzed with short gradients, potentially leading to either missed detection of isotope envelopes or inaccurate quantification. To address these issues, we introduce a novel strategy to deconvolute MS1 spectra, enhancing the accuracy of MS1 quantification. -
MP 061: A comparison of peptide- and spectrum-centric search engines beyond bar charts
Topic Area: Data-Independent Acquisition
Date and Time: Monday, June 3, 2024 | 10:30 am - 2:30 pm (Pacific Time)
Data-independent acquisition (DIA) is known for its reproducibility, quantitative precision, and proteome coverage, at the expense of producing more complex spectra. In recent years, various algorithms were developed for peptide identification from DIA datasets. While peptide-centric search engines rely on correlating fragment ion elution curves, spectrum-centric methods analyze spectra one at a time. Generally, algorithms strive to maximize true identifications (IDs) and minimize false IDs through a diverse set of scores. All tools provide self-calculated q-values to control the false discovery rate (FDR) using a target-decoy approach and some provide estimates for fragment ion interference. Here, we compare the performance of DIA-NN, Spectronaut and Chimerys on an Orbitrap Astral dataset, and scrutinize their tailor-made statistics and scores. -
MP 672: Intensity-based site localization of post-translational modifications utilizing predicted peptide fragmentation spectra
Topic Area: Peptides: PTM Identification
Date and Time: Monday, June 3, 2024 | 10:30 am - 2:30 pm (Pacific Time)
In the last decade, mass spectrometry-based analysis of post-translational modifications (PTMs) in general and phosphorylation in particular has become a routine application in cell signaling studies, and accurate localization of PTM sites is crucial for differentiation of the underlying biology. So far, localization tools mostly rely on peak m/z matching to calculate site probabilities, which does not leverage fragment ion intensity differences between modified peptide isomers. Here, we exploit the capabilities of our deep-learning framework to accurately predict physicochemical properties of modified peptides. We demonstrate the usefulness of these predictions for the intensity-based localization of modification sites. We evaluate different scoring approaches and compare the performance of our intensity-driven approach to available localization tools. -
WP 421: The sky is the limit: a cloud-based proteomics platform for the masses
Topic Area: Informatics: Workflow and Data Management
Date and Time: Wednesday, June 5, 2024 | 10:30 am - 2:30 pm (Pacific Time)
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. -
WP 408: An infinite well: harmonizing all public proteomics data for machine learning
Topic Area: Informatics: Workflow and Data Management
Date and Time: Wednesday, June 5, 2024 | 10:30 am - 2:30 pm (Pacific Time)
Synthetic proteomics datasets like ProteomeTools are optimal for machine learning but are finite, in particular when considering spectra from post-translational modifications. Conversely, public data repositories contain abundant data, but harmonizing them is challenging: measurement environments, methods, analysis software and instrument calibrations differ across laboratories and time, and they lack shared quality control (QC) peptides for calibration. To overcome these challenges, we developed a distributed, standardized, and cost-effective workflow to search and harmonize public data with minimal manual intervention. We processed >3000 files containing ubiquitinated, phosphorylated, and acetylated peptides and applied a novel machine learning-based calibration for data harmonization. The resulting dataset contains ~1 million modified peptides from >33 million high-quality PSM that complements synthetic public data such as ProteomeTools.
