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A parallel algorithm for de novo peptide sequencing

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Introduction

Protein identification is a main problem in proteomics, the large-scale analysis of proteins. Tandem mass spectrometry (MS/MS) provides an important tool to handle protein identification problem. Indeed the spectrometer is capable of ionizing a mixture of peptides, essentially several copies of the same unknown peptide, dissociating every molecule into two fragments called complementary ions, and measuring the mass/charge ratios of the peptides and of their fragments. These measures are visualized as mass peaks in a mass spectrum.

There are two fundamental approaches to interpret the spectra. The first approach is to search in a database to find the peptides that match the MS/MS spectra. This database search approach is effective for known proteins, but does not permit to detect novel proteins. This second task can be dealt with the de novo sequencing that computes the amino acid sequence of the peptides directly from their MS/MS spectra.

In the de novo sequencing problem one knows the peptide mass m P , and a subset of the masses of its ions m1,...,m n , and the task is to determine a sequence Q of masses of residues such that subsets of its prefixes and suffixes give the masses in input. The solution is, in general, not unique.

Methods

We reformulate the problem in terms of searching paths in a graph. To this goal, let M P denote the set of ion masses m i in input increased with: their complementary masses m P - m i + 2, the mass of the hydrogen, 1, and of its complementary mass m P - 17. By abuse of notation, M P = {m1,...,m n }, where m i <m j if i <j.

We build a directed acyclic graph G P = (V, E) as follows. Let a node v i associate to a member m i of M P , and an edge from v i to v j if m j - m i equals the sum of residue masses.

The de novo sequencing problem consists in determining any path from v1 to v n in the graph G P .

Although there is a unique original protein, the de novo sequencing may have in general more solutions (or none). In order to choose one sequence among the possible solutions, researchers have introduced any scoring function [13] depending on the masses of the fragments in the spectra. Our algorithm can determine either the solution of maximum score according to any given function or that of maximum length.

We use 3 algorithms:

the first algorithm consists in building the graph;

the second algorithm permits to distinguish the feasible paths that start in v1 and terminate in v n among the others;

finally, the third algorithm retrieves the solution of maximum score.

Results and conclusion

The literature offers a wide range of sequential de novo sequencing algorithm, all taking O(n logn) time, at least [4, 5]. Aiming at lowering such time barrier, we proposed a work-efficient CREW (concurrent-read exclusive write) PRAM [6] algorithm for the de novo peptide sequencing that determines the maximum length sequence in O(n) time by using log n processors. Such theoretical result showed that our algorithm is clearly scalable and reaches the theoretical, ideal efficiency.

The next step we are working on is the implementation of the proposed algorithm on a parallel machine to verify such theoretical results and scalability features.

References

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    Bafna V, Edwards N: On de novo interpretation of tandem mass spectra for peptide identification. Proceeding of ICCMB. 2003, ACM Press,

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    Dancik V, Addona TA, Clauser KR, Vath JE: De novo peptide sequencing via tandem mass spectrometry. Journal of computational biology. 1999, 6:

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    Ma B, Zhang K, Hendrie C, Liang C, Li M, Doherty-Kirby A, Lajoie G: PEAKS: powerful Software for Peptide De Novo Sequencing by MS/MS. Rapid Communications in Mass Spect. 2003,

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    Brunetti S, Dutta D, Liberatori S, Mori E, Varrazzo D: An efficient algorithm for de novo Peptide Sequencing. Proceeding of the ICANNGA. 2005, Springer Verlag,

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    Pandurangan G, Ramesh H: The Restriction Mapping Problem Revisited. Journal of Computer and System Sciences. 2002,

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    Jàjà J: An introduction to parallel algorithms. 1992, Addison-Wesley,

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Author information

Correspondence to Elisa Mori.

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Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Mori, E., Brunetti, S., Campa, S. et al. A parallel algorithm for de novo peptide sequencing. BMC Syst Biol 1, P61 (2007) doi:10.1186/1752-0509-1-S1-P61

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Keywords

  • Peptide
  • Suffix
  • Protein Identification
  • Directed Acyclic Graph
  • Parallel Machine