  The problem of inferring proteins from complex peptide samples in shotgun
proteomic workflow sets extreme demands on computational resources. 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. Two problems arise from this: a) due to these variations, the
applicability of exact string matching methodologies could be questioned and b)
the difficulty of defining a reference sequence for a particular set of
proteins that are functionally indistinguishable, but with some variation in
features. This paper presents a model-based inference approach that is
developed and validated to solve the inference problem. Our approach starts
from an examination of the known set of splice variants and isoforms of a
target protein to identify the Greatest Common Stable Substring (GCSS) of amino
acids and the Substrings Subjects to Limited Variation (SSLV) and their
respective locations on the GCSS. Then we define and solve the Sub-string
Matching Problem with Limited Tolerance (SMPLT). This approach is validated on
identified peptides in a labelled and clustered data set from UNIPROT.
Identification of Baylisascaris Procyonis infection was used as an application
instance that achieved up to 70 times speedup compared to a software only
system. This workflow can be generalised to any inexact multiple pattern
matching application by replacing the patterns in a clustered and distributed
environment which permits a distance between member strings to account for
permitted deviations such as substitutions, insertions and deletions.
