Like peptide sequencing algorithms, peptide identification algorithms may return an erroneous peptide, particularly if the score of the highest-scoring peptide found in the proteome is much lower than the score of the highest-scoring peptide over all peptides. For this reason, biologists usually establish a score threshold and only pay attention to a solution of the Peptide Identification Problem if its score is at least equal to the threshold.

Given a set of spectral vectors SpectralVectors, an amino acid string Proteome, and a score threshold threshold, we will solve the Peptide Identification Problem for each vector Spectrum' in SpectralVectors and identify a peptide Peptide having maximum score for this spectral vector over all peptides in Proteome (ties are broken arbitrarily). If Score(Peptide, Spectrum) is greater than or equal to threshold, then we conclude that Peptide is present in the sample and call the pair (Peptide, Spectrum') a Peptide- Spectrum Match (PSM). The resulting collection of PSMs for SpectralVectors is denoted PSM_threshold(Proteome, SpectralVectors).

The following pseudocode identifies all PSMs scoring above a threshold for a set of spectra and a proteome, using an algorithm that you just implemented to solve the Peptide identification Problem, which we call PeptideIdentification.

Assignment

Given file containing a list of spectral vectors SpectralVectors, an amino acid string Proteome, and a score threshold T.

Return all unique Peptide-Spectrum Matches scoring at least as high as T.

Example

>>> PSM_search('data02.txt'1, 'RIGNSRQYHP', 19)
set()

>>> PSM_search('data03.txt'2, 'YEFPWWPDYKRHHEI', 10)
{'WPD'}

>>> PSM_search('data04.txt'3, 'KLWSTPNWCYGEHCHGFSMV', 7)
{'MV', 'CHGF'}