MS DOSS: the next generation of hybrid algorithms for PTM-enabled peptide search
Supervision
Viktoria Dorfer
FH OOE, 1st Supervisor
Kyowon Jeong
EKUT, 2nd Supervisor
Objectives
Develop a hybrid open PTM search to identify peptides at constant/low search space. This algorithm shall make use of different search strategies to be able to openly search for post-translationally modified peptides in high-resolution mass spectrometry data at constant/low search space.
Methodology
Multiple search strategies (de novo, database search, library search) shall be combined to quickly filter out false peptide candidates and reduce the search space before the search. Searches shall be open to scan for (unknown) modifications. Artificial intelligence methods (e.g., intensity prediction, fragmentation pattern prediction) shall help to identify and match candidates.
Required Skills
Basic skills in bioinformatics and (computational) mass spectrometry
Good programming skills, preferably C# and python/R
Good command of English
Expected Results
A new algorithm able to identify (post-translationally modified) peptides out of high-resolution tandem mass spectra at low search time with small search space but as an open search.
Planned Secondments
Host: VIB (S. Degroeve), duration: 2 Months; when: Year 1; goal: Integration of intensity prediction into MS DOSS.
Host: TUM (M. Wilhelm), duration: 3 Months; when: Year 2; goal: Integration of fragmentation prediction into MS DOSS.
Host: EKUT (K. Jeong), duration: 1 Month; when: Year 3; goal: Integration of spectra deconvolution into MS DOSS.
Enrolment in doctoral programs
Ph.D. in Computer Science from Johannes Kepler University Linz.
References
1. Dorfer, V., Pichler, P., Stranzl, T., Stadlmann, J., Taus, T., Winkler, S., & Mechtler, K. (2014). MS Amanda, a Universal Identification Algorithm Optimized for High Accuracy Tandem Mass Spectra. Journal of Proteome Research, 13(8), 3679–3684. https://doi.org/10.1021/pr500202e
2. Dorfer, V., Maltsev, S., Winkler, S., & Mechtler, K. (2018). CharmeRT: Boosting Peptide Identifications by Chimeric Spectra Identification and Retention Time Prediction. Journal of Proteome Research, 17(8), 2581–2589. https://doi.org/10.1021/acs.jproteome.7b00836
3. Dorl, S., Winkler, S., Mechtler, K., & Dorfer, V. (2017). PhoStar: Identifying Tandem Mass Spectra of Phosphorylated Peptides before Database Search. Journal of Proteome Research, 17(1), 290–295. https://doi.org/10.1021/acs.jproteome.7b00563