Abstract
While non-linear peptide natural products such as Vanco- mycin and Daptomycin are among the most effective antibiotics, the computational techniques for sequencing such peptides are still in infancy. Previous methods for sequencing peptide natural products are based on Nuclear Magnetic Resonance spectroscopy, and require large amounts (milligrams) of purified materials. Recently, development of mass spectrometry based methods has enabled accurate sequencing of non-linear peptidic natural products using picograms of materials, but the question of evaluating statistical significance of Peptide Spectrum Matches (PSM) for these peptides remains open. Moreover, it is unclear how to decide whether a given spectrum is produced by linear, cyclic, or branch-cyclic peptide. Surprisingly, all previous mass spectrometery studies overlooked the fact that a very similar problem has been succesfully addressed in particle physics in 1951. In this paper we develop a method for estimating statistical significance of PSMs defined by non-linear peptides, which makes it possible to identify whether a peptide is linear, cyclic or branch-cyclic, an important step toward identification of peptidic natural products.
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Mohimani, H., Kim, S., Pevzner, P.A. (2012). MS-DPR: An Algorithm for Computing Statistical Significance of Spectral Identifications of Non-linear Peptides. In: Raphael, B., Tang, J. (eds) Algorithms in Bioinformatics. WABI 2012. Lecture Notes in Computer Science(), vol 7534. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33122-0_24
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DOI: https://doi.org/10.1007/978-3-642-33122-0_24
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