Peptide refinement using a stochastic search

Lewis, Nicole H.; Hitchcock, David B.; Dryden, Ian L.; Rose, John R.

Abstract

Identifying a peptide based on a scan from a mass spectrometer is an important yet highly challenging problem. To identify peptides, we present a Bayesian approach which uses prior information about the average relative abundances of bond cleavages and the prior probability of any particular amino acid sequence. The proposed scoring function is composed of two overall distance measures, which measure how close an observed spectrum is to a theoretical scan for a peptide. Our use of our scoring function, which approximates a likelihood, has connections to the generalization presented by Bissiri et al. (2016) of the Bayesian framework. A Markov chain Monte Carlo algorithm is employed to simulate candidate choices from the posterior distribution of the peptide sequence. The true peptide is estimated as the peptide with the largest posterior density.

Citation

Lewis, N. H., Hitchcock, D. B., Dryden, I. L., & Rose, J. R. (in press). Peptide refinement using a stochastic search. Journal of the Royal Statistical Society: Series C, https://doi.org/10.1111/rssc.12280

• Journal Article Type: Article
• Acceptance Date: Feb 19, 2018
• Online Publication Date: Apr 18, 2018
• Deposit Date: Apr 20, 2018
• Publicly Available Date: Apr 19, 2019
• Journal: Journal of the Royal Statistical Society: Series C
• Print ISSN: 0035-9254
• Electronic ISSN: 0035-9254
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1111/rssc.12280
• Keywords: Stochastic Search, Bayesian Methods, Markov Chain Monte Carlo, Peptide Identification, Tandem Mass Spectrometry.
• Public URL: https://nottingham-repository.worktribe.com/output/927169
• Publisher URL: https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssc.12280
• Additional Information: This is the peer reviewed version of the following article: Lewis, Nicole H. and Hitchcock, David B. and Dryden, Ian L. and Rose, John R. (2018) Peptide refinement using a stochastic search. Journal of the Royal Statistical Society: Series C ,doi:10.1111/rssc.12280, which has been published in final form athttps://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssc.12280. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.