  The problem of inferring proteins from complex peptide cocktails (digestion
products of biological samples) in shotgun proteomic workflow sets extreme
demands on computational resources in respect of the required very high
processing throughputs, rapid processing rates and reliability of results. This
is exacerbated by the fact that, in general, a given protein cannot be defined
by a fixed sequence of amino acids due to the existence of splice variants and
isoforms of that protein. Therefore, the problem of protein inference could be
considered as one of identifying sequences of amino acids with some limited
tolerance. In the current paper a model-based hardware acceleration of a
structured and practical inference approach is developed and validated on a
mass spectrometry experiment of realistic size. We have achieved 10 times
maximum speed-up in the co-designed workflow compared to a similar
software-only workflow run on the processor used for co-design.
