Protein identification via mass spectrometry forms the foundation of high-throughput proteomics. Tandem mass spectrometry, when applied to a complex mixture of peptides, selects and fragments each peptide to reveal its amino-acid sequence structure. The successful analysis of such an experiment typically relies on amino-acid sequence databases to provide a set of biologically relevant peptides to examine. A key subproblem, then, for amino-acid sequence database search engines that analyze tandem mass spectra is to efficiently generate all the peptide candidates from a sequence database with mass equal to one of a large set of observed peptide masses. We demonstrate that to solve the problem efficiently, we must deal with substring redundancy in the amino-acid sequence database and focus our attention on looking up the observed peptide masses quickly. We show that it is possible, with some preprocessing and memory overhead, to solve the peptide candidate generation problem in time asymptotically proportional to the size of the sequence database and the number of peptide candidates output.

The identification of peptides from tandem mass spectra is an important part of many high-throughput proteomics pipelines. In the high-throughput setting, the spectra are typically identified using software that matches tandem mass spectra with putative peptides from amino-acid sequence databases. The e#ectiveness of these search engines depends heavily on the completeness of the amino-acid sequence database used, but suitably complete amino-acid sequence databases are large, and the sequence database search engines typically have search times that are proportional to the size of the sequence database.