Quantitative Proteomics using Bioinformatics

using high throughput sequencing data (NGS)

Downloadable poster in PDF

   IMPORTANT DATES for this Course
   Deadline for applications: Oct 7th 2016 (NEW)
   Course dates: Oct 17th - Oct 19th 2016

Candidates with adequate profile will be accepted in the next 72 hours after the application until we reach 20 participants.


Mikhail Savitski obtained his PhD at Uppsala university, Sweden, in 2007. During his PhD he worked on many fundamental aspects of mass spectrometry, such as studying peptide fragmentation pathways in the gas phase, as well as developing novel algorithms for identification of post translational modifications. In 2008 he joined the biotech company Cellzome where he worked on developing new methodologies for multiplexed quantitative mass spectrometry, as well as new technologies for unbiased proteome wide detection of protein ligand interactions. Since 2016 he works at EMBL in Heidelberg where he develops the novel field of fold-stability proteomics for understanding the phenomenon of aggregation and disaggregation, cell phenotyping, and detection of protein interactions with drugs, metabolites, DNA and RNA.
Affiliation: European Molecular Biology Laboratory (EMBL), Heidelberg, DE

Roman Zubarev has a 30-year experience in mass spectrometry. He received his PhD from Uppsala university in 1997. During his postdoctoral visit to Cornell (with Fred. W. McLafferty), he co-invented electron capture dissociation (ECD), for which he has received a number of international awards, including Biemann medal. After a 4-year stay in Odense, Denmark as Associate professor in Mass spectrometry, he returned to Uppsala in 2002 to lead a proteomics laboratory. In 2008 he accepted a professorship in Medicinal proteomics at the Karolinska Institutet, Stockholm. Roman has been an instructor in previous courses of the GTPB programme.
Affiliation: Karolinska Institutet, Stockholm, SE

Bo Zhang is currently in his last year of PhD study at Karolinska Institutet, in Stockholm. His project is centered on advanced bioinformatics for high-resolution mass spectrometry-based proteomics, supervised by Professor Roman Zubarev. During his PhD study, he developed a protein identification workflow, named DeMix, that utilizes the natural multiplexing feature in complex proteomics data and has increased protein identification efficiency by 30%. This workflow has been extended into DeMix-Q for sensitive and reproducible quantification, which aims to build on a new quantification-centered paradigm in shotgun proteomics.
Affiliation: Karolinska Institutet, Stockholm, SE

Course Description

Recent years have seen rapid development in biological mass spectrometry and proteomics. In particular, the quantitative performance has taken a huge leap forward, both as a label-free (LF) approach and as multiplexed quantitative mass spectrometry. This development has enabled new technologies for unbiased characterization of drug molecules with protein targets, which is one of the major challenges in drug development. Here we will explain the basic principle of protein quantification by mass spectrometry. A pragmatic description of strengths and weaknesses of both label-free as well as the multiplexed methodology will be presented. A novel technology, thermal proteome profiling (TPP), will be explained that detects protein drug interactions in living cells. A practical session will take the students through the analysis and interpretation of quantitative mass spectrometry data stemming from the TPP experiment using an R package. In the LF part of the course, we will explain the basics of the identification software DeMix that performs deconvolution of the naturally multiplexed tandem mass spectra, thus significantly improving the identification aspects. An extension to quantification, DeMix Q, introduces the false discovery rate (FDR) for peptide identity transfer between the LC-MS runs, and greatly improves the quantification performance. Both DeMix and DeMix Q are realized based on Open MS package. The participants will learn how to use these programs and process their data.

Target audience

This course is intended for all scientists interested in mass spectrometry, proteomics and drug development.


Basic biological knowledge, especially with regard to proteins. Interest in biotechnology. General computer literacy, (e.g. editing plain text data files, familiarity with Excel, navigating using the command line) will be assumed.

Detailed Program

Instituto Gulbenkian de Ciência,

Apartado 14, 2781-901 Oeiras, Portugal

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Last updated:  Aug 26th 2016