SCIENCE & TECHNOLOGY
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Almost all diseases manifest themselves as changes in the expression, abundance or signaling status of proteins. Therefore, the precise analysis of the proteome (the entirety of a biological system’s proteins) is a crucial step in the understanding, diagnosis, and treatment of diseases. Mass spectrometry-based proteomics is a powerful technique for the simultaneous analyses of thousands of proteins, fueling biomarker research and drug discovery.
PROTEOMIC DATA ANALYSIS
The analysis of proteomic data heavily relies on the automated matching of acquired tandem mass spectra of peptides (fragments of proteins) to protein sequence databases. This process relies on simple assumptions and the key concepts have remained largely unchanged since their introduction in 1993. We believe that we only see the tip of the iceberg. To date, only half of the data acquired from a sample can be identified using classical data analysis workflows, leading to lost productivity, precious samples, and opportunities.
THE POWER OF DEEP LEARNING
Recent developments in the field of machine learning revolutionize all branches of research. Artificial neural networks learn to perform tasks without previously defined rule sets, solely based on annotated training data. We have learned to harness this power to predict properties of peptides like liquid chromatography retention time or fragmentation behavior inside the mass spectrometer.
PREDICTING PEPTIDE PROPERTIES
The MSAID founders developed a generic deep learning framework called INFERYS which learns to predict any peptide property from training data. INFERYS demonstrates superior accuracy performance well above all other current approaches. The algorithm was trained using millions of mass spectra and can be adapted to all common mass spectrometers with minimal additional training. The model is universally applicable to proteins from any organism, creating huge opportunities in areas such as immunopeptidomics, proteogenomics, or metaproteomics. The novel, intelligent search algorithm CHIMERYS is fueled by accurate predictions provided by INFERYS and enables a deeper, more comprehensive data analysis.
HOW TO CITE MSAID SOFTWARE
The following publications by the MSAID team describe the technology in more detail.
INFERYS rescoring: Boosting peptide identifications and scoring confidence of database search results
Deep Single-Shot NanoLC-MS Proteome Profiling with a 1500 Bar UHPLC System, Long Fully Porous Columns, and HRAM MS
In-Depth Mass Spectrometry-Based Proteomics of Formalin-Fixed, Paraffin-Embedded Tissues with a Spatial Resolution of 50–200 μm
Protein SUMOylation is a sex-specific regulator of fear memory formation in the amygdala
Label-Free Profiling of up to 200 Single-Cell Proteomes per Day Using a Dual-Column Nanoflow Liquid Chromatography Platform
Proteomic Analysis Reveals Sex-Specific Protein Degradation Targets in the Amygdala During Fear Memory Formation
Ultrasensitive NanoLC-MS of Subnanogram Protein Samples Using Second Generation Micropillar Array LC Technology with Orbitrap Exploris 480 and FAIMS PRO
Proteome Discoverer - A Community Enhanced Data Processing Suite for Protein Informatics