Name
Abstract | ||
|---|---|---|
Mass spectrometry (MS) based protein profiling has become one of the key technologies in biomedical research and biomarker discovery. One bottleneck in MS-based protein analysis is sample preparation and an efficient fractionation step to reduce the complexity of the biological samples, which are too complex to be analyzed directly with MS. Sample preparation strategies that reduce the complexity of tryptic digests by using immunoaffinity based methods have shown to lead to a substantial increase in throughput and sensitivity in the proteomic mass spectrometry approach. The limitation of using such immunoaffinity-based approaches is the availability of the appropriate peptide specific capture antibodies. Recent developments in these approaches, where subsets of peptides with short identical terminal sequences can be enriched using antibodies directed against short terminal epitopes, promise a significant gain in efficiency.We show that the minimal set of terminal epitopes for the coverage of a target protein list can be found by the formulation as a set cover problem, preceded by a filtering pipeline for the exclusion of peptides and target epitopes with undesirable properties.For small datasets (a few hundred proteins) it is possible to solve the problem to optimality with moderate computational effort using commercial or free solvers. Larger datasets, like full proteomes require the use of heuristics. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1186/1748-7188-5-28 | Algorithms for Molecular Biology |
Keywords | Field | DocType |
bioinformatics,set covering problem,sample preparation,algorithms,mass spectrometry,biomedical research | Epitope,Biology,Peptide,Sample preparation,Mass spectrometry,Biomarker discovery,Protein profiling,Bioinformatics | Journal |
Volume | Issue | ISSN |
5 | 1 | 1748-7188 |
Citations | PageRank | References |
1 | 0.48 | 6 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hannes Planatscher | 1 | 76 | 5.90 |
| Jochen Supper | 2 | 106 | 8.69 |
| Oliver Poetz | 3 | 1 | 0.82 |
| Dieter Stoll | 4 | 1 | 0.48 |
| Thomas Joos | 5 | 1 | 0.82 |
| Markus F Templin | 6 | 1 | 1.16 |
| Andreas Zell | 7 | 1419 | 137.58 |