Breakthroughs in Proteomics and Metabolomics
The presentations on Orbitrap Astral highlight significant advancements in proteomics and metabolomics. The first presentation from Alexey Chernobrovkin focuses on the integration of high throughput and high sensitivity mass spectrometry, demonstrating how the latest generation of mass spectrometers, coupled with advanced LC systems like the Vanquish Neo, can significantly increase sample processing speeds while maintaining robust protein coverage. This setup allows for efficient and reproducible results in quantitative proteomics, even with high sample loads, making it ideal for large-scale screening and clinical applications.
The second presentation from Giada Marino delves into the predictive capabilities of machine learning models in retention time prediction for LC-MS experiments. It showcases the development and application of models like DeepLC, which use neural networks to predict retention times, collision cross sections, and fragment densities. These predictions enhance the identification and quantification of biomolecules, offering a more comprehensive understanding of proteomic and metabolomic profiles. The integration of modifications and transfer learning further improves the accuracy and applicability of these predictions across different experimental setups.
Learning points :
- High Throughput and Sensitivity: The integration of high-throughput mass spectrometry with advanced LC systems like the Vanquish Neo significantly enhances sample processing speeds and maintains robust protein coverage. This setup is ideal for large-scale proteomics and clinical applications, enabling the processing of up to 200 samples per day.
- Machine Learning for Retention Time Prediction: The development and application of machine learning models, such as DeepLC, improve the prediction of retention times, collision cross sections, and fragment densities. These predictions enhance the identification and quantification of biomolecules, providing a more comprehensive understanding of proteomic and metabolomic profiles.
- Optimization and Flexibility in Chromatography: The use of optimized LC setups and tailored columns ensures maximum performance and reproducibility in quantitative proteomics. The flexibility to choose the right column for specific project needs, coupled with extensive QC and optimization, allows for efficient and reliable results in various experimental conditions.
Who should attend?
- Proteomics and Metabolomics Researchers: Scientists working in the fields of proteomics and metabolomics will gain insights into the latest advancements in mass spectrometry and chromatography, enhancing their research capabilities.
- Clinical Laboratory Professionals: Those involved in clinical diagnostics and large-scale screening will benefit from understanding how high-throughput and high-sensitivity mass spectrometry can improve sample processing and data accuracy.
- Data Scientists and Bioinformaticians: Professionals focused on developing and applying predictive models in biological research will learn about the integration of machine learning for retention time prediction, enhancing their analytical toolkit.
Additional Webinars
Take advantage of our growing library of convenient and educational recorded webinars and resources. Visit our webinars page
Presenter: Alexey Chernobrovkin (Principal Scientist, Pelago Bioscience AB)
Alexey Chernobrovkin, Principal Scientist at Pelago Bioscience. With a background in applied physics (MSc, MIPT, Moscow), got his PhD in Bioinformatics (Institute of Biomedical Chemistry, Moscow) and thenmoved for a postdoc at Karolinska Institutet, Stockholm in the group of prof. Roman Zubarev. At Pelago Bioscience AC leads development of MS-based platform for proteome-wide deconvolution of targets and mechanism of action (MoA) of bioactive compounds.
Presenter: Giada Marino (Senior Research Scientist, Evotec)
Dr. Giada Marino is a Senior Research Scientist at Evotec working on high sensitivity and high throughput proteomics applications. Giada has more than 10 years of experience in proteomics and mass spectrometry. After a postdoc in Chris Overall lab at UBC (Vancouver, Canada) focusing on N-terminomics applications, she managed the mass spectrometry core facility in the Biology Department at the Ludwig-Maximilian University working on a wide range of protein and peptide mass spectrometry projects.
