Automatic Poisson peak harvesting for high throughput protein identification

Abstract

High throughput identification of proteins by peptide mass fingerprinting requires an efficient means of picking peaks from mass spectra. Here, we report the development of a peak harvester to automatically pick monoisotopic peaks from spectra generated on matrix-assisted laser desorption/ionisation time of flight (MALDI-TOF) mass spectrometers. The peak harvester uses advanced mathematical morphology and watershed algorithms to first process spectra to stick representations. Subsequently, Poisson modelling is applied to determine which peak in an isotopically resolved group represents the monoisotopic mass of a peptide. We illustrate the features of the peak harvester with mass spectra of standard peptides, digests of gel-separated bovine serum albumin, and with Escherictia coli proteins prepared by two-dimensional polyacrylamide gel electrophoresis. In all cases, the peak harvester proved effective in its ability to pick similar monoisotopic peaks as an experienced human operator, and also proved effective in the identification of monoisotopic masses in cases where isotopic distributions of peptides were overlapping. The peak harvester can be operated in an interactive mode, or can be completely automated and linked through to peptide mass fingerprinting protein identification tools to achieve high throughput automated protein identification.

A model-based method for the prediction of the isotopic distribution of peptides

“…The isotopic distributions of sulphurcontaining peptides are different from those of nosulphur peptides. This indicates that, in contrast to the assumption made by Breen et al [3], the effect of sulphur should not be ignored in the approximation of the distributions.…”

“…Breen et al [3] suggested to approximate the result of the multinomial expansion (i.e., the expected proportional heights of different isotopic peaks) by a Poisson distribution. To compute the distribution, its mean, say M, needs to be known.…”

“…Senko et al calculated the average isotopic distribution for any mass peptide via the multinomial expansion (Yergey 1983) [1], using a scaled version of the average amino acid Averagine (Senko et al 1995) [2]. Another method, introduced by Breen et al, approximates the result of the multinomial expansion by a Poisson model (Breen et al 2000) [3]. Although both methods perform well, they have their specific limitations.…”

“…Another method, introduced by Breen et al, approximates the result of the multinomial expansion by a Poisson model [3]. Although both methods perform well, they have their specific limitations.…”

Improving automatic peptide mass fingerprint protein identification by combining many peak sets

“…Automated mono-isotopic peak detection algorithms, e.g. [11][12][13][14], work excellently on strong spectra but their performance is often insufficient on weak spectra. This is unsatisfying since strong spectra are often produced by high abundant proteins, which typically represent the bulk of the cell activity.…”