Probability-Based Scoring Function as a Software Tool Used in the Genome-Based Identification of Proteins from Spirulina platensis

Abstract

One of the major goals of proteomic research is the identification of proteins, a goal that often requires various software tools and databases. These tools have to be able to handle large amounts of data, such as those generated by PMF (Peptide Mass Fingerprinting), a high throughput technique. A newly sequenced organism, Spirulina platensis, was recently used to generate an in silico database, and thus an in-house tool designed for compatibility with this database and its inputs (PMF) was constructed in the present study. With a probability based scoring function, this tool effectively ranked ambiguous protein identification results by using five criteria: score, number of matched peptides, % coverage, pI and molecular weight. As a result, the protein identification step of Spirulina proteomic studies can be achieved precisely. Moreover, a very useful function of this tool is its capability for batch processing, in which the system can handle proteinidentification searches of a hundred of proteins automatically, from a single user’s input. Therefore, the tool not only gives accurate protein identification results but also saves the user time in processing a large amount of data.