title: Optimal de novo design of MRM experiments for rapid assay development in targeted proteomics.
abstract: Targeted proteomic approaches such as multiple reaction monitoring (MRM) overcome problems associated with classical shotgun mass spectrometry experiments. Developing MRM quantitation assays can be time consuming, because relevant peptide representatives of the proteins must be found and their retention time and the product ions must be determined. Given the transitions, hundreds to thousands of them can be scheduled into one experiment run. However, it is difficult to select which of the transitions should be included into a measurement. We present a novel algorithm that allows the construction of MRM assays from the sequence of the targeted proteins alone. This enables the rapid development of targeted MRM experiments without large libraries of transitions or peptide spectra. The approach relies on combinatorial optimization in combination with machine learning techniques to predict proteotypicity, retention time, and fragmentation of peptides. The resulting potential transitions are scheduled optimally by solving an integer linear program. We demonstrate that fully automated construction of MRM experiments from protein sequences alone is possible and over 80% coverage of the targeted proteins can be achieved without further optimization of the assay.
authors.affiliation: Center for Bioinformatics, Eberhard-Karls-Universität Tubingen, Germany. bertsch@informatik.uni-tuebingen.de
authors.forename: Andreas
authors.initials: A
authors.lastname: Bertsch
authors.forename: Stephan
authors.initials: S
authors.lastname: Jung
authors.forename: Alexandra
authors.initials: A
authors.lastname: Zerck
authors.forename: Nico
authors.initials: N
authors.lastname: Pfeifer
authors.forename: Sven
authors.initials: S
authors.lastname: Nahnsen
authors.forename: Carsten
authors.initials: C
authors.lastname: Henneges
authors.forename: Alfred
authors.initials: A
authors.lastname: Nordheim
authors.forename: Oliver
authors.initials: O
authors.lastname: Kohlbacher
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001557:Benzenesulfonates / Q000494:pharmacology; D045744:Cell Line, Tumor; D002851:Chromatography, High Pressure Liquid; D005260:Female; D006801:Humans; D016014:Linear Models; D008390:Markov Chains; D013058:Mass Spectrometry / Q000379:methods*; D008545:Melanoma / Q000378:metabolism; D051379:Mice; D009536:Niacinamide / Q000031:analogs & derivatives; D010446:Peptide Fragments / Q000032:analysis*; D010671:Phenylurea Compounds; D020543:Proteome / Q000032:analysis; D040901:Proteomics / Q000379:methods*; D011725:Pyridines / Q000494:pharmacology; D015203:Reproducibility of Results; D000077157:Sorafenib
chemical_list: D001557:Benzenesulfonates; D010446:Peptide Fragments; D010671:Phenylurea Compounds; D020543:Proteome; D011725:Pyridines; D009536:Niacinamide; D000077157:Sorafenib