PROTrEIN is a European innovative and interdisciplinary Research and Training network to train the next generation of researchers in computational proteomics. The network gathers top international experts in computational proteomics, network biology, data visualization, mass spectrometry and gamification from prestigious European academic and private institutions and it is composed of 11 beneficiaries, and 8 partner organizations.
The network’s mission is to train a new generation of computational proteomics researchers by providing them an inter-sectoral and interdisciplinary set of skills to tackle the main challenges in the field and improve their future employability.
PROTrein Website : http://protrein.eu/
The two PhD applications in the PROTrEIN network:
7: DEVELOPMENT OF SMART ACQUISITION METHODS USING REAL-TIME CONTROL OF INSTRUMENTS AND MACHINE LEARNING
Develop intelligent data acquisition workflows to enhance proteomic analytical depth. While global MS acquisition methods based on DDA enable the direct, large-scale and unbiased analysis of complex samples, low-abundant proteins remain undetected in such single-run measurements. Approaches such as PRM or DIA take advantage of previously acquired information (e.g. validated peptide sequence matches, libraries…) to improve the detection of low-abundant molecules during analysis of the samples. The problem of retention time (RT) variability complicates the efficient use of previously collected data when programming scheduling methods to specifically detect at high sensitivity many low abundant proteins (high-throughput PRM). We plan to develop new open-source software solutions for real-time control of MS acquisition in order to improve the implementation and multiplexing of high sensitivity PRM measurements, based on optimized RT prediction.
The ESR will develop new open-source software solutions for real-time control of instruments, integrated with the MS-Angel tool for the automation of data processing tasks and subsequent triggering of customized acquisition methods. He will also evaluate machine learning algorithms and existing solutions (e.g. Prosit) to improve the parameters of peptides fragmentation.
An open-source framework facilitating the development of new real-time and smart acquisition methods, improving the throughput of PRM assays.
Knowledge of data acquisition by mass spectrometry
C# programming language (experimented)
See the application on the PROTrEIN website : HERE
8: DEVELOPMENT OF NEW SIGNAL PROCESSING ALGORITHMS OF ION MOBILITY MS DATA TO IMPROVE THE QUANTIFICATION OF PHOSPHOPROTEOMICS SAMPLES
Improve the processing and quantitative analysis of phosphoproteomics data. Localization of labile PTMs such as phosphate groups on the backbone of peptide sequences is a challenging process and can lead to ambiguous assignments, depending on the presence or not of discriminant fragment ions in the MS/MS data. Additionally, isobaric species may not be resolved during the LC separation, generating mixed MS/MS spectra and making impossible the accurate quantification of peptide isoforms bearing the PTM at different positions. The introduction of the ion mobility dimension in new instruments improves separation of such isoforms. The objective is to develop new algorithms taking advantage of this additional dimension for accurate characterization and quantification of phosphopeptide isoforms across different MS runs.
The ESR will update the mzDB file format specification to add support for ion mobility, develop new peak-pickers taking advantage of the ion mobility dimension, and develop new algorithms for retention time alignment.
We expect to provide optimized methods and tools for the localization of PTMs and for the quantification of isobaric forms detected in different runs obtained using an ion mobility separation. These will be implemented in the MS-Angel software currently developed in the team, and applied to the analysis of large-scale phosphoproteomic datasets.
Knowledge of mass spectrometry data analysis
C++ programming language (experimented)
Basics of Python or R