A5036968128- Advanced Proteomics Techniques and Applications
- Mass Spectrometry Techniques and Applications
- Protein Structure and Dynamics
- Machine Learning in Bioinformatics
- Metabolomics and Mass Spectrometry Studies
- Chemical Synthesis and Analysis
- Protein Kinase Regulation and GTPase Signaling
- Advanced biosensing and bioanalysis techniques
- Click Chemistry and Applications
- Genomics and Phylogenetic Studies
- Scientific Computing and Data Management
ActionAid
2024-2025
Technical University of Munich
2019-2023
Database search engines for bottom‐up proteomics largely ignore peptide fragment ion intensities during the automated scoring of tandem mass spectra against protein databases. Recent advances in deep learning allow accurate prediction intensities. Using these predictions to calculate additional intensity‐based scores helps overcome this drawback. Here, we describe a processing workflow termed INFERYS™ rescoring Sequest HT engine results Thermo Scientific™ Proteome Discoverer™ 2.5 software....
Abstract Medicinal chemistry has discovered thousands of potent protein and lipid kinase inhibitors. These may be developed into therapeutic drugs or chemical probes to study biology. Because polypharmacology, a large part the human kinome currently lacks selective probes. To discover such probes, we profiled 1,183 compounds from drug discovery projects in lysates cancer cell lines using Kinobeads. The resulting 500,000 compound–target interactions are available ProteomicsDB exemplify how...
Abstract Proteomic workflows generate vastly complex peptide mixtures that are analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS), creating thousands of spectra, most which chimeric and contain fragment ions from more than one peptide. Because differences in data acquisition strategies such as data-dependent (DDA), data-independent (DIA) or parallel reaction monitoring (PRM), separate software packages employing different analysis concepts used for identification...
The exponential increase in proteomics data presents critical challenges for conventional processing workflows. These pipelines often consist of fragmented software packages, glued together using complex in-house scripts or error-prone manual workflows running on local hardware, which are costly to maintain and scale. MSAID Platform offers a fully automated, managed pipeline, consolidating formerly disjointed functions into unified, API-driven services that cover the entire process from raw...
Abstract Proteomic workflows generate vastly complex peptide mixtures that are analyzed by liquid chromatography–tandem mass spectrometry, creating thousands of spectra, most which chimeric and contain fragment ions from more than one peptide. Because differences in data acquisition strategies such as data-dependent, data-independent or parallel reaction monitoring, separate software packages employing different analysis concepts used for identification quantification, even though the...
Despite the increasing use of high-throughput experiments in molecular biology, methods for evaluating and classifying acquired results have not kept pace, requiring significant manual efforts to do so. Here, we present CiRCus, a framework generate custom machine learning models classify from proteomics binding experiments. We show experimental procedure that guided us layout this as well usage on an example data set consisting 557 166 protein/drug curves achieving AUC 0.9987. By applying...