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PROSPECT: Labeled Tandem Mass Spectrometry Dataset for Machine Learning in Proteomics

Omar Shouman · Wassim Gabriel · Victor-George Giurcoiu · Vitor Sternlicht · Mathias Wilhelm

Hall J (level 1) #1029

Keywords: [ Deep Learning ] [ dataset ] [ Retention Time ] [ Neutral Losses ] [ Mass Spectrometry ] [ Annotated Spectra ] [ ProteomeTools ] [ Proteomics ] [ Fragment Ions ] [ Intensity ] [ machine learning ]


Proteomics is the interdisciplinary field focusing on the large-scale study of proteins. Proteins essentially organize and execute all functions within organisms. Today, the bottom-up analysis approach is the most commonly used workflow, where proteins are digested into peptides and subsequently analyzed using Tandem Mass Spectrometry (MS/MS). MS-based proteomics has transformed various fields in life sciences, such as drug discovery and biomarker identification. Today, proteomics is entering a phase where it is helpful for clinical decision-making. Computational methods are vital in turning large amounts of acquired raw MS data into information and, ultimately, knowledge. Deep learning has proved its success in multiple domains as a robust framework for supervised and unsupervised machine learning problems. In proteomics, scientists are increasingly leveraging the potential of deep learning to predict the properties of peptides based on their sequence to improve their confident identification. However, a reference dataset is missing, covering several proteomics tasks, enabling performance comparison, and evaluating reproducibility and generalization. Here, we present a large labeled proteomics dataset spanning several tasks in the domain to address this challenge. We focus on two common applications: peptide retention time and MS/MS spectrum prediction. We review existing methods and task formulations from a machine learning perspective and recommend suitable evaluation metrics and visualizations. With an accessible dataset, we aim to lower the entry barrier and enable faster development in machine learning for proteomics.

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