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Improving Crosslink Detection from Mass Spectrometry Data

Cross-Links are experimentally derived distance constraints that can be used to guide protein structure prediction. The cross-links are obtained from the mass spectrometry data: the different observed spectras are matched to peptide sequences and cross-links are predicted. The prediction of the cross-links from the mass spectrometry data is noisy. The goal of this thesis is to improve the prediction of cross-links by leveraging physico-chemical and geometrical data from the protein. By including external information and dealing directly with the ambiguity in the data we could improve the number of cross-links and/or need fewer experiments for a similar yield.

Requirements: Strong Python skills, Computation Biology Course (or similar knowledge), good knowledge of statistics and probabilistic models

Contact Persons 

Mahmoud Mabrouk [1] and Kolja Stahl [2]

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