A Structured Hardware Software Architecture for Peptide Based Diagnosis - Sub-string Matching Problem with Limited Tolerance (ICIAfS14)

TL;DR

This paper introduces a model-based peptide inference method that efficiently identifies proteins with variations, using a new substring matching approach with limited tolerance, validated on proteomic data with significant speed improvements.

Contribution

It proposes the Sub-string Matching Problem with Limited Tolerance (SMPLT) and a workflow for protein inference that handles variations, improving speed and accuracy over existing methods.

Findings

Achieved up to 70 times speedup in protein identification.

Validated approach on UNIPROT data set.

Applicable to inexact multiple pattern matching problems.

Abstract

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.