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presentation 1) MIND: a soft-sensor to improve mass accuracy in high-resolution top-down proteomics/
- Speaker(s)
- Dirk Valkenborg /Frederik Lermyte
- Affiliation
- DV: Hasselt University & VITO/ FL: Antwerp University
- Date
- Nov. 27, 2013, 10:15 a.m.
- Room
- room 3320
- Seminar
- Seminar Computational Biology and Bioinformatics
The presentations will be preceded by the short introduction to the topic of mass spectrometry.
abstract 1: MIND: a soft-sensor to improve mass accuracy in high-resolution top-down proteomics.
A typical approach to determine the mass and charge-state of intact proteins visualized by low-resolution mass spectrometry is based on the observed charge distribution of that molecule in a spectrum. However, in the case of high-resolution mass spectrometry, additional information becomes operative. Indeed, current instrumentation (e.g., FTMS) is capable of resolving the isotope distribution of multiply charged proteins. From the observed distribution, the charge-state can be easily inferred and the average mass of the molecule is calculated as a weighted sum of the intensity and mass values of the considered isotope peaks. We argue that this procedure is suboptimal and prone to error in the context of high-resolution analysis. Therefore, we present an accurate and robust approach to determine the mass of intact proteins.
By using our implementation of the BRAIN algorithm (Claesen et al., 2012), we are able to conduct calculations of the theoretical isotope distribution on extremely large protein databases. It should be noted that during this calculation, very precise information about the abundance and exact masses of the isotope variants is derived. We then model the relation between the monoisotopic mass and the isotope characteristics of the proteins in the database. The obtained model is used to predict the monoisotopic mass (typically unobservable) based on the partially observed isotope distribution of a molecule in a spectrum. Doing so, a more precise method and a valid alternative for the average mass calculation becomes available. The method is called MIND (MonoIsotopic mass liNear preDictor).
Proteins are one of the main classes of biomolecules and have many biological functions. Therefore, it is important to have access to good analytical techniques to study these molecules and mass spectrometry (MS) is one of the most important technologies currently used in this context. However, traditional MS techniques are unable to fully characterize proteins and often lead to data that is incomplete and/or difficult to interpret. An emerging technology called Electron Transfer Dissociation (ETD) could assist in overcoming some of these limitations, and we are currently investigating possible applications of ETD. Furthermore, we wish to characterize the ETD process itself in detail and are investigating the use of software to model the different reactions that can occur.
abstract 1: MIND: a soft-sensor to improve mass accuracy in high-resolution top-down proteomics.
A typical approach to determine the mass and charge-state of intact proteins visualized by low-resolution mass spectrometry is based on the observed charge distribution of that molecule in a spectrum. However, in the case of high-resolution mass spectrometry, additional information becomes operative. Indeed, current instrumentation (e.g., FTMS) is capable of resolving the isotope distribution of multiply charged proteins. From the observed distribution, the charge-state can be easily inferred and the average mass of the molecule is calculated as a weighted sum of the intensity and mass values of the considered isotope peaks. We argue that this procedure is suboptimal and prone to error in the context of high-resolution analysis. Therefore, we present an accurate and robust approach to determine the mass of intact proteins.
By using our implementation of the BRAIN algorithm (Claesen et al., 2012), we are able to conduct calculations of the theoretical isotope distribution on extremely large protein databases. It should be noted that during this calculation, very precise information about the abundance and exact masses of the isotope variants is derived. We then model the relation between the monoisotopic mass and the isotope characteristics of the proteins in the database. The obtained model is used to predict the monoisotopic mass (typically unobservable) based on the partially observed isotope distribution of a molecule in a spectrum. Doing so, a more precise method and a valid alternative for the average mass calculation becomes available. The method is called MIND (MonoIsotopic mass liNear preDictor).
abstract 2: Mass spectrometry analysis of proteins using Electron Transfer Dissociation
Proteins are one of the main classes of biomolecules and have many biological functions. Therefore, it is important to have access to good analytical techniques to study these molecules and mass spectrometry (MS) is one of the most important technologies currently used in this context. However, traditional MS techniques are unable to fully characterize proteins and often lead to data that is incomplete and/or difficult to interpret. An emerging technology called Electron Transfer Dissociation (ETD) could assist in overcoming some of these limitations, and we are currently investigating possible applications of ETD. Furthermore, we wish to characterize the ETD process itself in detail and are investigating the use of software to model the different reactions that can occur.
